diff --git a/.gitattributes b/.gitattributes index 818d649bf21cdef29b21f885c8f770f9baa1714e..bc7381932296cac341b1bdfe5ab148438db15a8c 100644 --- a/.gitattributes +++ b/.gitattributes @@ -29,3 +29,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +*.mp4 filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +*.jpg filter=lfs diff=lfs merge=lfs -text +*.whl filter=lfs diff=lfs merge=lfs -text +*.gif filter=lfs diff=lfs merge=lfs -text +*.wav filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..242dedd172ad41ee5a5e8542d7cf013108007736 --- /dev/null +++ b/.gitignore @@ -0,0 +1,43 @@ +# Python build +.eggs/ +gradio.egg-info +dist/ +*.pyc +__pycache__/ +*.py[cod] +*$py.class +build/ + +# JS build +gradio/templates/cdn +gradio/templates/frontend + +# Secrets +.env + +# Gradio run artifacts +*.db +*.sqlite3 +gradio/launches.json +flagged/ +gradio_cached_examples/ + +# Tests +.coverage +coverage.xml +test.txt + +# Demos +demo/tmp.zip +demo/files/*.avi +demo/files/*.mp4 + +# Etc +.idea/* +.DS_Store +*.bak +workspace.code-workspace +*.h5 + +# log files +.pnpm-debug.log \ No newline at end of file diff --git a/demos/blocks_component_shortcut/run.py b/demos/blocks_component_shortcut/run.py new file mode 100644 index 0000000000000000000000000000000000000000..6b408fdaf25de4e0241d33a60628f9f8c63bbe6a --- /dev/null +++ b/demos/blocks_component_shortcut/run.py @@ -0,0 +1,31 @@ +import gradio as gr + + +def greet(str): + return str + + +with gr.Blocks() as demo: + """ + You can make use of str shortcuts you use in Interface within Blocks as well. + + Interface shortcut example: + Interface(greet, "textarea", "textarea") + + You can use + 1. gr.component() + 2. gr.templates.Template() + 3. gr.Template() + All the templates are listed in gradio/templates.py + """ + with gr.Row(): + text1 = gr.component("textarea") + text2 = gr.TextArea() + text3 = gr.templates.TextArea() + text1.change(greet, text1, text2) + text2.change(greet, text2, text3) + text3.change(greet, text3, text1) + button = gr.component("button") + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_essay/run.py b/demos/blocks_essay/run.py new file mode 100644 index 0000000000000000000000000000000000000000..525af83bbcdf0bb9d5f7666076a0adf4d993a8fa --- /dev/null +++ b/demos/blocks_essay/run.py @@ -0,0 +1,22 @@ +import gradio as gr + + +def change_textbox(choice): + if choice == "short": + return gr.Textbox.update(lines=2, visible=True) + elif choice == "long": + return gr.Textbox.update(lines=8, visible=True) + else: + return gr.Textbox.update(visible=False) + + +with gr.Blocks() as demo: + radio = gr.Radio( + ["short", "long", "none"], label="What kind of essay would you like to write?" + ) + text = gr.Textbox(lines=2, interactive=True) + + radio.change(fn=change_textbox, inputs=radio, outputs=text) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_essay_update/run.py b/demos/blocks_essay_update/run.py new file mode 100644 index 0000000000000000000000000000000000000000..625da12f3cfad4f81ad363b331149ceccec0c270 --- /dev/null +++ b/demos/blocks_essay_update/run.py @@ -0,0 +1,19 @@ +import gradio as gr + +def change_textbox(choice): + if choice == "short": + return gr.update(lines=2, visible=True, value="Short story: ") + elif choice == "long": + return gr.update(lines=8, visible=True, value="Long story...") + else: + return gr.update(visible=False) + +with gr.Blocks() as demo: + radio = gr.Radio( + ["short", "long", "none"], label="Essay Length to Write?" + ) + text = gr.Textbox(lines=2, interactive=True) + radio.change(fn=change_textbox, inputs=radio, outputs=text) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/blocks_flag/requirements.txt b/demos/blocks_flag/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..296d654528b719e554528b956c4bf5a1516e812c --- /dev/null +++ b/demos/blocks_flag/requirements.txt @@ -0,0 +1 @@ +numpy \ No newline at end of file diff --git a/demos/blocks_flag/run.py b/demos/blocks_flag/run.py new file mode 100644 index 0000000000000000000000000000000000000000..2083d39edf84328acdb09450e518829a1e0d985b --- /dev/null +++ b/demos/blocks_flag/run.py @@ -0,0 +1,33 @@ +import numpy as np +import gradio as gr + +def sepia(input_img, strength): + sepia_filter = strength * np.array( + [[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]] + ) + (1-strength) * np.identity(3) + sepia_img = input_img.dot(sepia_filter.T) + sepia_img /= sepia_img.max() + return sepia_img + +callback = gr.CSVLogger() + +with gr.Blocks() as demo: + with gr.Row(): + with gr.Column(): + img_input = gr.Image() + strength = gr.Slider(0, 1, 0.5) + img_output = gr.Image() + with gr.Row(): + btn = gr.Button("Flag") + + # This needs to be called at some point prior to the first call to callback.flag() + callback.setup([img_input, strength, img_output], "flagged_data_points") + + img_input.change(sepia, [img_input, strength], img_output) + strength.change(sepia, [img_input, strength], img_output) + + # We can choose which components to flag -- in this case, we'll flag all of them + btn.click(lambda *args: callback.flag(args), [img_input, strength, img_output], None, _preprocess=False) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_flashcards/run.py b/demos/blocks_flashcards/run.py new file mode 100644 index 0000000000000000000000000000000000000000..a378f271962ce7fcfcfd10fa6c4879b571c1e65c --- /dev/null +++ b/demos/blocks_flashcards/run.py @@ -0,0 +1,92 @@ +import random + +import gradio as gr + +demo = gr.Blocks() + +with demo: + gr.Markdown( + "Load the flashcards in the table below, then use the Practice tab to practice." + ) + + with gr.Tabs(): + with gr.TabItem("Word Bank"): + flashcards_table = gr.Dataframe(headers=["front", "back"], type="array") + with gr.TabItem("Practice"): + with gr.Row(): + with gr.Column(): + front = gr.Textbox(label="Prompt") + with gr.Row(): + new_btn = gr.Button("New Card").style(full_width=True) + flip_btn = gr.Button("Flip Card").style(full_width=True) + with gr.Column(visible=False) as answer_col: + back = gr.Textbox(label="Answer") + selected_card = gr.Variable() + with gr.Row(): + correct_btn = gr.Button( + "Correct", + ).style(full_width=True) + incorrect_btn = gr.Button("Incorrect").style(full_width=True) + + with gr.TabItem("Results"): + results = gr.Variable(value={}) + correct_field = gr.Markdown("# Correct: 0") + incorrect_field = gr.Markdown("# Incorrect: 0") + gr.Markdown("Card Statistics: ") + results_table = gr.Dataframe(headers=["Card", "Correct", "Incorrect"]) + + def load_new_card(flashcards): + card = random.choice(flashcards) + return ( + card, + card[0], + gr.Column.update(visible=False), + ) + + new_btn.click( + load_new_card, + [flashcards_table], + [selected_card, front, answer_col], + ) + + def flip_card(card): + return card[1], gr.Column.update(visible=True) + + flip_btn.click(flip_card, [selected_card], [back, answer_col]) + + def mark_correct(card, results): + if card[0] not in results: + results[card[0]] = [0, 0] + results[card[0]][0] += 1 + correct_count = sum(result[0] for result in results.values()) + return ( + results, + f"# Correct: {correct_count}", + [[front, scores[0], scores[1]] for front, scores in results.items()], + ) + + def mark_incorrect(card, results): + if card[0] not in results: + results[card[0]] = [0, 0] + results[card[0]][1] += 1 + incorrect_count = sum(result[1] for result in results.values()) + return ( + results, + f"# Inorrect: {incorrect_count}", + [[front, scores[0], scores[1]] for front, scores in results.items()], + ) + + correct_btn.click( + mark_correct, + [selected_card, results], + [results, correct_field, results_table], + ) + + incorrect_btn.click( + mark_incorrect, + [selected_card, results], + [results, incorrect_field, results_table], + ) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_flipper/run.py b/demos/blocks_flipper/run.py new file mode 100644 index 0000000000000000000000000000000000000000..88859c6113e8887708bfc2762b87845166530363 --- /dev/null +++ b/demos/blocks_flipper/run.py @@ -0,0 +1,27 @@ +import numpy as np +import gradio as gr + +def flip_text(x): + return x[::-1] + +def flip_image(x): + return np.fliplr(x) + +with gr.Blocks() as demo: + gr.Markdown("Flip text or image files using this demo.") + with gr.Tabs(): + with gr.TabItem("Flip Text"): + text_input = gr.Textbox() + text_output = gr.Textbox() + text_button = gr.Button("Flip") + with gr.TabItem("Flip Image"): + with gr.Row(): + image_input = gr.Image() + image_output = gr.Image() + image_button = gr.Button("Flip") + + text_button.click(flip_text, inputs=text_input, outputs=text_output) + image_button.click(flip_image, inputs=image_input, outputs=image_output) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/blocks_flipper/screenshot.gif b/demos/blocks_flipper/screenshot.gif new file mode 100644 index 0000000000000000000000000000000000000000..84562331deddcbec95680423b5eb1c1129d482e3 --- /dev/null +++ b/demos/blocks_flipper/screenshot.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:21b814857d694e576b3e6db4cabe069f56e7386f7a1fabc6be81431c7176d700 +size 1108151 diff --git a/demos/blocks_form/run.py b/demos/blocks_form/run.py new file mode 100644 index 0000000000000000000000000000000000000000..8a3a9dde53a9a172836d5423bc987306631cbea5 --- /dev/null +++ b/demos/blocks_form/run.py @@ -0,0 +1,33 @@ +import gradio as gr + +with gr.Blocks() as demo: + error_box = gr.Textbox(label="Error", visible=False) + + name_box = gr.Textbox(label="Name") + age_box = gr.Number(label="Age") + symptoms_box = gr.CheckboxGroup(["Cough", "Fever", "Runny Nose"]) + submit_btn = gr.Button("Submit") + + with gr.Column(visible=False) as output_col: + diagnosis_box = gr.Textbox(label="Diagnosis") + patient_summary_box = gr.Textbox(label="Patient Summary") + + def submit(name, age, symptoms): + if len(name) == 0: + return {error_box: gr.update(value="Enter name", visible=True)} + if age < 0 or age > 200: + return {error_box: gr.update(value="Enter valid age", visible=True)} + return { + output_col: gr.update(visible=True), + diagnosis_box: "covid" if "Cough" in symptoms else "flu", + patient_summary_box: f"{name}, {age} y/o" + } + + submit_btn.click( + submit, + [name_box, age_box, symptoms_box], + [error_box, diagnosis_box, patient_summary_box, output_col], + ) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/blocks_gpt/run.py b/demos/blocks_gpt/run.py new file mode 100644 index 0000000000000000000000000000000000000000..8799cde02d2e4ea9fa2bf417a4c941c18235a942 --- /dev/null +++ b/demos/blocks_gpt/run.py @@ -0,0 +1,16 @@ +import gradio as gr + +api = gr.Interface.load("huggingface/EleutherAI/gpt-j-6B") + +def complete_with_gpt(text): + # Use the last 50 characters of the text as context + return text[:-50] + api(text[-50:]) + +with gr.Blocks() as demo: + textbox = gr.Textbox(placeholder="Type here and press enter...", lines=4) + btn = gr.Button("Generate") + + btn.click(complete_with_gpt, textbox, textbox) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/blocks_hello/run.py b/demos/blocks_hello/run.py new file mode 100644 index 0000000000000000000000000000000000000000..4d416ee4ee0594fbd7c2ab1035fe2a1dc399d903 --- /dev/null +++ b/demos/blocks_hello/run.py @@ -0,0 +1,17 @@ +import gradio as gr + +def welcome(name): + return f"Welcome to Gradio, {name}!" + +with gr.Blocks() as demo: + gr.Markdown( + """ + # Hello World! + Start typing below to see the output. + """) + inp = gr.Textbox(placeholder="What is your name?") + out = gr.Textbox() + inp.change(welcome, inp, out) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/blocks_inputs/__init__.py b/demos/blocks_inputs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demos/blocks_inputs/config.json b/demos/blocks_inputs/config.json new file mode 100644 index 0000000000000000000000000000000000000000..532160459961b23931646d99f34f23320015e763 --- /dev/null +++ b/demos/blocks_inputs/config.json @@ -0,0 +1,99 @@ +{ + "version": "3.0.6", + "mode": "blocks", + "dev_mode": true, + "components": [ + { + "id": 1, + "type": "textbox", + "props": { + "lines": 5, + "max_lines": 20, + "value": "", + "label": "Input", + "show_label": true, + "name": "textbox", + "visible": true, + "style": {} + } + }, + { + "id": 2, + "type": "textbox", + "props": { + "lines": 1, + "max_lines": 20, + "value": "", + "label": "Output-Interactive", + "show_label": true, + "name": "textbox", + "visible": true, + "style": {} + } + }, + { + "id": 3, + "type": "textbox", + "props": { + "lines": 1, + "max_lines": 20, + "value": "Hello friends\nhello friends\n\nHello friends\n\n", + "label": "Output", + "show_label": true, + "interactive": false, + "name": "textbox", + "visible": true, + "style": {} + } + }, + { + "id": 4, + "type": "button", + "props": { + "value": "Submit", + "variant": "secondary", + "name": "button", + "visible": true, + "style": {} + } + } + ], + "theme": "default", + "css": null, + "enable_queue": false, + "layout": { + "id": 0, + "children": [ + { + "id": 1 + }, + { + "id": 2 + }, + { + "id": 3 + }, + { + "id": 4 + } + ] + }, + "dependencies": [ + { + "targets": [ + 4 + ], + "trigger": "click", + "inputs": [ + 1 + ], + "outputs": [ + 2 + ], + "backend_fn": true, + "js": null, + "status_tracker": null, + "queue": null + } + ] +} \ No newline at end of file diff --git a/demos/blocks_inputs/lion.jpg b/demos/blocks_inputs/lion.jpg new file mode 100644 index 0000000000000000000000000000000000000000..2cf5afb1f0bfe6dac09b7fd6bfeb68e5e80dbe33 --- /dev/null +++ b/demos/blocks_inputs/lion.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c45ece00075f152cb2e6cfd5f1dfd7dc8e83042264685b4f470026240eff3ef +size 18489 diff --git a/demos/blocks_inputs/run.py b/demos/blocks_inputs/run.py new file mode 100644 index 0000000000000000000000000000000000000000..502b1ed9145c37c841f5d49b7eecc18877eb45ca --- /dev/null +++ b/demos/blocks_inputs/run.py @@ -0,0 +1,36 @@ +import gradio as gr +import os + +def combine(a, b): + return a + " " + b + +def mirror(x): + return x + +with gr.Blocks() as demo: + + txt = gr.Textbox(label="Input", lines=2) + txt_2 = gr.Textbox(label="Input 2") + txt_3 = gr.Textbox(value="", label="Output") + btn = gr.Button(value="Submit") + btn.click(combine, inputs=[txt, txt_2], outputs=[txt_3]) + + with gr.Row(): + im = gr.Image() + im_2 = gr.Image() + + btn = gr.Button(value="Mirror Image") + btn.click(mirror, inputs=[im], outputs=[im_2]) + + gr.Markdown("## Text Examples") + gr.Examples([["hi", "Adam"], ["hello", "Eve"]], [txt, txt_2], txt_3, combine, cache_examples=True) + gr.Markdown("## Image Examples") + gr.Examples( + examples=[os.path.join(os.path.dirname(__file__), "lion.jpg")], + inputs=im, + outputs=im_2, + fn=mirror, + cache_examples=True) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_interpretation/requirements.txt b/demos/blocks_interpretation/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..dd211e6e33eedca38484d19a8a782d0e3987f16a --- /dev/null +++ b/demos/blocks_interpretation/requirements.txt @@ -0,0 +1 @@ +shap \ No newline at end of file diff --git a/demos/blocks_interpretation/run.py b/demos/blocks_interpretation/run.py new file mode 100644 index 0000000000000000000000000000000000000000..edabcc888c3b0ee408598dfc0f6c34dab271d8f7 --- /dev/null +++ b/demos/blocks_interpretation/run.py @@ -0,0 +1,58 @@ +import gradio as gr +import shap +from transformers import pipeline +import matplotlib +import matplotlib.pyplot as plt +matplotlib.use('Agg') + + +sentiment_classifier = pipeline("text-classification", return_all_scores=True) + + +def classifier(text): + pred = sentiment_classifier(text) + return {p["label"]: p["score"] for p in pred[0]} + + +def interpretation_function(text): + explainer = shap.Explainer(sentiment_classifier) + shap_values = explainer([text]) + # Dimensions are (batch size, text size, number of classes) + # Since we care about positive sentiment, use index 1 + scores = list(zip(shap_values.data[0], shap_values.values[0, :, 1])) + + scores_desc = sorted(scores, key=lambda t: t[1])[::-1] + + # Filter out empty string added by shap + scores_desc = [t for t in scores_desc if t[0] != ""] + + fig_m = plt.figure() + plt.bar(x=[s[0] for s in scores_desc[:5]], + height=[s[1] for s in scores_desc[:5]]) + plt.title("Top words contributing to positive sentiment") + plt.ylabel("Shap Value") + plt.xlabel("Word") + return {"original": text, "interpretation": scores}, fig_m + + +with gr.Blocks() as demo: + with gr.Row(): + with gr.Column(): + input_text = gr.Textbox(label="Input Text") + with gr.Row(): + classify = gr.Button("Classify Sentiment") + interpret = gr.Button("Interpret") + with gr.Column(): + label = gr.Label(label="Predicted Sentiment") + with gr.Column(): + with gr.Tabs(): + with gr.TabItem("Display interpretation with built-in component"): + interpretation = gr.components.Interpretation(input_text) + with gr.TabItem("Display interpretation with plot"): + interpretation_plot = gr.Plot() + + classify.click(classifier, input_text, label) + interpret.click(interpretation_function, input_text, [interpretation, interpretation_plot]) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/blocks_joined/files/cheetah1.jpg b/demos/blocks_joined/files/cheetah1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..66d3b48fd19cd8cd8d8437b6f33183b3d3d42589 --- /dev/null +++ b/demos/blocks_joined/files/cheetah1.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:35550bfbba996e59c242af00f6a14a9c0d055dfbc52ad069a1a4e8c1c39ca095 +size 20552 diff --git a/demos/blocks_joined/run.py b/demos/blocks_joined/run.py new file mode 100644 index 0000000000000000000000000000000000000000..d560c3868967afbd77a771e83f92dd8722f59f60 --- /dev/null +++ b/demos/blocks_joined/run.py @@ -0,0 +1,58 @@ +from time import sleep +import gradio as gr +import os + +cheetah = os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg") + + +def img(text): + sleep(3) + return [ + cheetah, + cheetah, + cheetah, + cheetah, + cheetah, + cheetah, + cheetah, + cheetah, + cheetah, + ] + + +with gr.Blocks(css=".container { max-width: 800px; margin: auto; }") as demo: + gr.Markdown("

DALL·E mini

") + gr.Markdown( + "DALL·E mini is an AI model that generates images from any prompt you give!" + ) + with gr.Group(): + with gr.Box(): + with gr.Row().style(mobile_collapse=False, equal_height=True): + + text = gr.Textbox( + label="Enter your prompt", show_label=False, max_lines=1 + ).style( + border=(True, False, True, True), + rounded=(True, False, False, True), + container=False, + ) + btn = gr.Button("Run").style( + margin=False, + rounded=(False, True, True, False), + ) + gallery = gr.Gallery(label="Generated images", show_label=False).style( + grid=( + 1, + 3, + ), + height="auto", + ) + btn.click(img, inputs=text, outputs=gallery) + + +if __name__ == "__main__": + demo.launch() + + +# margin = (TOP, RIGHT, BOTTOM, LEFT) +# rounded = (TOPLEFT, TOPRIGHT, BOTTOMRIGHT, BOTTOMLEFT) diff --git a/demos/blocks_js_methods/run.py b/demos/blocks_js_methods/run.py new file mode 100644 index 0000000000000000000000000000000000000000..fd7ab727af45f54bf8033607278c5b5eac29e752 --- /dev/null +++ b/demos/blocks_js_methods/run.py @@ -0,0 +1,28 @@ +import gradio as gr + +blocks = gr.Blocks() + +with blocks as demo: + subject = gr.Textbox(placeholder="subject") + verb = gr.Radio(["ate", "loved", "hated"]) + object = gr.Textbox(placeholder="object") + + with gr.Row(): + btn = gr.Button("Create sentence.") + reverse_btn = gr.Button("Reverse sentence.") + foo_bar_btn = gr.Button("Foo bar.") + + def sentence_maker(w1, w2, w3): + return f"{w1} {w2} {w3}" + + output1 = gr.Textbox(label="output 1") + output2 = gr.Textbox(label="verb") + output3 = gr.Textbox(label="verb reversed") + + btn.click(sentence_maker, [subject, verb, object], output1) + reverse_btn.click(None, [subject, verb, object], output2, _js="(s, v, o) => o + ' ' + v + ' ' + s") + verb.change(lambda x: x, verb, output3, _js="(x) => [...x].reverse().join('')") + foo_bar_btn.click(None, [], subject, _js="(x) => x + ' foo'") + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/blocks_kinematics/config.json b/demos/blocks_kinematics/config.json new file mode 100644 index 0000000000000000000000000000000000000000..34aa00955e3ab4afd2158293e5589c33378c778c --- /dev/null +++ b/demos/blocks_kinematics/config.json @@ -0,0 +1,124 @@ +{ + "version": "3.0.6", + "mode": "blocks", + "dev_mode": true, + "components": [ + { + "id": 1, + "type": "markdown", + "props": { + "value": "

Let's do some kinematics! Choose the speed and angle to see the trajectory.

\n", + "name": "markdown", + "visible": true, + "style": {} + } + }, + { + "id": 2, + "type": "row", + "props": { + "type": "row", + "visible": true, + "style": {} + } + }, + { + "id": 3, + "type": "slider", + "props": { + "minimum": 1, + "maximum": 30, + "step": 0.1, + "value": 25, + "label": "Speed", + "show_label": true, + "name": "slider", + "visible": true, + "style": {} + } + }, + { + "id": 4, + "type": "slider", + "props": { + "minimum": 0, + "maximum": 90, + "step": 0.1, + "value": 45, + "label": "Angle", + "show_label": true, + "name": "slider", + "visible": true, + "style": {} + } + }, + { + "id": 5, + "type": "plot", + "props": { + "show_label": true, + "name": "plot", + "visible": true, + "style": {} + } + }, + { + "id": 6, + "type": "button", + "props": { + "value": "Run", + "variant": "secondary", + "name": "button", + "visible": true, + "style": {} + } + } + ], + "theme": "default", + "css": null, + "enable_queue": false, + "layout": { + "id": 0, + "children": [ + { + "id": 1 + }, + { + "id": 2, + "children": [ + { + "id": 3 + }, + { + "id": 4 + } + ] + }, + { + "id": 5 + }, + { + "id": 6 + } + ] + }, + "dependencies": [ + { + "targets": [ + 6 + ], + "trigger": "click", + "inputs": [ + 3, + 4 + ], + "outputs": [ + 5 + ], + "backend_fn": true, + "js": null, + "status_tracker": null, + "queue": null + } + ] +} \ No newline at end of file diff --git a/demos/blocks_kinematics/run.py b/demos/blocks_kinematics/run.py new file mode 100644 index 0000000000000000000000000000000000000000..b43b2476a77a236e1c6f0571428f2dc553348710 --- /dev/null +++ b/demos/blocks_kinematics/run.py @@ -0,0 +1,40 @@ +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +import numpy as np + +import gradio as gr + + +def plot(v, a): + g = 9.81 + theta = a / 180 * 3.14 + tmax = ((2 * v) * np.sin(theta)) / g + timemat = tmax * np.linspace(0, 1, 40)[:, None] + + x = (v * timemat) * np.cos(theta) + y = ((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2)) + + fig = plt.figure() + plt.scatter(x=x, y=y, marker=".") + plt.xlim(0, 100) + plt.ylim(0, 60) + return fig + + +demo = gr.Blocks() + +with demo: + gr.Markdown( + "Let's do some kinematics! Choose the speed and angle to see the trajectory." + ) + + with gr.Row(): + speed = gr.Slider(1, 30, 25, label="Speed") + angle = gr.Slider(0, 90, 45, label="Angle") + output = gr.Plot() + btn = gr.Button(value="Run") + btn.click(plot, [speed, angle], output) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_layout/run.py b/demos/blocks_layout/run.py new file mode 100644 index 0000000000000000000000000000000000000000..2759b2d4fe6bfcebec4c863b19e750ab5ac11688 --- /dev/null +++ b/demos/blocks_layout/run.py @@ -0,0 +1,31 @@ +import gradio as gr + + +demo = gr.Blocks() + +with demo: + with gr.Row(): + gr.Image(interactive=True) + gr.Image() + with gr.Row(): + gr.Textbox(label="Text") + gr.Number(label="Count") + gr.Radio(choices=["One", "Two"]) + with gr.Row(): + with gr.Row(): + with gr.Column(): + gr.Textbox(label="Text") + gr.Number(label="Count") + gr.Radio(choices=["One", "Two"]) + gr.Image() + with gr.Column(): + gr.Image(interactive=True) + gr.Image() + gr.Image() + gr.Textbox(label="Text") + gr.Number(label="Count") + gr.Radio(choices=["One", "Two"]) + + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_mask/lion.jpg b/demos/blocks_mask/lion.jpg new file mode 100644 index 0000000000000000000000000000000000000000..2cf5afb1f0bfe6dac09b7fd6bfeb68e5e80dbe33 --- /dev/null +++ b/demos/blocks_mask/lion.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c45ece00075f152cb2e6cfd5f1dfd7dc8e83042264685b4f470026240eff3ef +size 18489 diff --git a/demos/blocks_mask/run.py b/demos/blocks_mask/run.py new file mode 100644 index 0000000000000000000000000000000000000000..da8df5dd62164f81388dd7e763906f0850f18878 --- /dev/null +++ b/demos/blocks_mask/run.py @@ -0,0 +1,27 @@ +import gradio as gr +import os + + +def fn(mask): + return [mask["image"], mask["mask"]] + + +demo = gr.Blocks() + +with demo: + with gr.Row(): + with gr.Column(): + img = gr.Image( + tool="sketch", source="upload", label="Mask", value=os.path.join(os.path.dirname(__file__), "lion.jpg") + ) + with gr.Row(): + btn = gr.Button("Run") + with gr.Column(): + img2 = gr.Image() + img3 = gr.Image() + + btn.click(fn=fn, inputs=img, outputs=[img2, img3]) + + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_multiple_event_triggers/run.py b/demos/blocks_multiple_event_triggers/run.py new file mode 100644 index 0000000000000000000000000000000000000000..b6020c98bd14ebc56003c903e8c7cf5796671694 --- /dev/null +++ b/demos/blocks_multiple_event_triggers/run.py @@ -0,0 +1,35 @@ +import gradio as gr +import pypistats +from datetime import date +from dateutil.relativedelta import relativedelta +import pandas as pd + +pd.options.plotting.backend = "plotly" + + +def get_plot(lib, time): + data = pypistats.overall(lib, total=True, format="pandas") + data = data.groupby("category").get_group("with_mirrors").sort_values("date") + start_date = date.today() - relativedelta(months=int(time.split(" ")[0])) + data = data[(data['date'] > str(start_date))] + chart = data.plot(x="date", y="downloads") + return chart + + +with gr.Blocks() as demo: + gr.Markdown( + """ + ## Pypi Download Stats 📈 + See live download stats for all of Hugging Face's open-source libraries 🤗 + """) + with gr.Row(): + lib = gr.Dropdown(["transformers", "datasets", "huggingface-hub", "gradio", "accelerate"], label="Library") + time = gr.Dropdown(["3 months", "6 months", "9 months", "12 months"], label="Downloads over the last...") + + plt = gr.Plot() + # You can add multiple event triggers in 2 lines like this + for event in [lib.change, time.change]: + event(get_plot, [lib, time], [plt]) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_neural_instrument_coding/flute.wav b/demos/blocks_neural_instrument_coding/flute.wav new file mode 100644 index 0000000000000000000000000000000000000000..19224c46ec23f0516330a70fc60619bb70faa5e5 --- /dev/null +++ b/demos/blocks_neural_instrument_coding/flute.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4100bf2ed1909efdb3c583c41dd78cd9347a4b2d5c8068ed3fd9d99ce24014b6 +size 222798 diff --git a/demos/blocks_neural_instrument_coding/new-sax-1.mp3 b/demos/blocks_neural_instrument_coding/new-sax-1.mp3 new file mode 100644 index 0000000000000000000000000000000000000000..a323caeee337eb3845622ff0681031cb5ad89be6 Binary files /dev/null and b/demos/blocks_neural_instrument_coding/new-sax-1.mp3 differ diff --git a/demos/blocks_neural_instrument_coding/new-sax-1.wav b/demos/blocks_neural_instrument_coding/new-sax-1.wav new file mode 100644 index 0000000000000000000000000000000000000000..3c85ed0edef3e15764aa0efb9c560eeece5d1e8a --- /dev/null +++ b/demos/blocks_neural_instrument_coding/new-sax-1.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:55767e0cc8c40c40ff9372c57ec9847b9c768d3ccdb0e40a18d0e395fd067043 +size 153678 diff --git a/demos/blocks_neural_instrument_coding/new-sax.wav b/demos/blocks_neural_instrument_coding/new-sax.wav new file mode 100644 index 0000000000000000000000000000000000000000..b86071806af7438368e8a6d16cfe121d86b37482 --- /dev/null +++ b/demos/blocks_neural_instrument_coding/new-sax.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8d4ad24dbfeee7f3808f9b08d1379043d26eeb559f69875e543210686afae586 +size 384044 diff --git a/demos/blocks_neural_instrument_coding/run.py b/demos/blocks_neural_instrument_coding/run.py new file mode 100644 index 0000000000000000000000000000000000000000..3d387de781ee72df120c52a8a2bdbf2dd218ee4b --- /dev/null +++ b/demos/blocks_neural_instrument_coding/run.py @@ -0,0 +1,143 @@ +# A Blocks implementation of https://erlj.notion.site/Neural-Instrument-Cloning-from-very-few-samples-2cf41d8b630842ee8c7eb55036a1bfd6 + +import datetime +import os +import random + +import gradio as gr +from gradio.components import Markdown as m + + +def get_time(): + now = datetime.datetime.now() + return now.strftime("%m/%d/%Y, %H:%M:%S") + + +def generate_recording(): + return random.choice(["new-sax-1.mp3", "new-sax-1.wav"]) + + +def reconstruct(audio): + return random.choice(["new-sax-1.mp3", "new-sax-1.wav"]) + + +io1 = gr.Interface( + lambda x, y, z: os.path.join(os.path.dirname(__file__),"sax.wav"), + [ + gr.Slider(label="pitch"), + gr.Slider(label="loudness"), + gr.Audio(label="base audio file (optional)"), + ], + gr.Audio(), +) + +io2 = gr.Interface( + lambda x, y, z: os.path.join(os.path.dirname(__file__),"flute.wav"), + [ + gr.Slider(label="pitch"), + gr.Slider(label="loudness"), + gr.Audio(label="base audio file (optional)"), + ], + gr.Audio(), +) + +io3 = gr.Interface( + lambda x, y, z: os.path.join(os.path.dirname(__file__),"trombone.wav"), + [ + gr.Slider(label="pitch"), + gr.Slider(label="loudness"), + gr.Audio(label="base audio file (optional)"), + ], + gr.Audio(), +) + +io4 = gr.Interface( + lambda x, y, z: os.path.join(os.path.dirname(__file__),"sax2.wav"), + [ + gr.Slider(label="pitch"), + gr.Slider(label="loudness"), + gr.Audio(label="base audio file (optional)"), + ], + gr.Audio(), +) + +demo = gr.Blocks(title="Neural Instrument Cloning") + +with demo.clear(): + m( + """ + ## Neural Instrument Cloning from Very Few Samples +
""" + ) + m( + """ + This Blocks implementation is an adaptation [a report written](https://erlj.notion.site/Neural-Instrument-Cloning-from-very-few-samples-2cf41d8b630842ee8c7eb55036a1bfd6) by Nicolas Jonason and Bob L.T. Sturm. + + I've implemented it in Blocks to show off some cool features, such as embedding live ML demos. More on that ahead... + + ### What does this machine learning model do? + It combines techniques from neural voice cloning with musical instrument synthesis. This makes it possible to produce neural instrument synthesisers from just seconds of target instrument audio. + + ### Audio Examples + Here are some **real** 16 second saxophone recordings: + """ + ) + gr.Audio(os.path.join(os.path.dirname(__file__),"sax.wav"), label="Here is a real 16 second saxophone recording:") + gr.Audio(os.path.join(os.path.dirname(__file__),"sax.wav")) + + m( + """\n + Here is a **generated** saxophone recordings:""" + ) + a = gr.Audio(os.path.join(os.path.dirname(__file__),"new-sax.wav")) + + gr.Button("Generate a new saxophone recording") + + m( + """ + ### Inputs to the model + The inputs to the model are: + * pitch + * loudness + * base audio file + """ + ) + + m( + """ + Try the model live! + """ + ) + + gr.TabbedInterface( + [io1, io2, io3, io4], ["Saxophone", "Flute", "Trombone", "Another Saxophone"] + ) + + m( + """ + ### Using the model for cloning + You can also use this model a different way, to simply clone the audio file and reconstruct it + using machine learning. Here, we'll show a demo of that below: + """ + ) + + a2 = gr.Audio() + a2.change(reconstruct, a2, a2) + + m( + """ + Thanks for reading this! As you may have realized, all of the "models" in this demo are fake. They are just designed to show you what is possible using Blocks 🤗. + + For details of the model, read the [original report here](https://erlj.notion.site/Neural-Instrument-Cloning-from-very-few-samples-2cf41d8b630842ee8c7eb55036a1bfd6). + + *Details for nerds*: this report was "launched" on: + """ + ) + + t = gr.Textbox(label="timestamp") + + demo.load(get_time, [], t) + + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_neural_instrument_coding/sax.wav b/demos/blocks_neural_instrument_coding/sax.wav new file mode 100644 index 0000000000000000000000000000000000000000..ed727c5408ced9c0b81ccf940b89425d6291dc65 --- /dev/null +++ b/demos/blocks_neural_instrument_coding/sax.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:12ee32c66257e1c98ed0f2f7b708a1eab638ec09f4c69dda3ec1d78047a7be4d +size 1536044 diff --git a/demos/blocks_neural_instrument_coding/sax2.wav b/demos/blocks_neural_instrument_coding/sax2.wav new file mode 100644 index 0000000000000000000000000000000000000000..c2aff941986ba4472c43fa1fc74c7a574dfd3f2e --- /dev/null +++ b/demos/blocks_neural_instrument_coding/sax2.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6ce10418426c151219a2f10697d348e00329fdd723197d60e49906420a443859 +size 384044 diff --git a/demos/blocks_neural_instrument_coding/trombone.wav b/demos/blocks_neural_instrument_coding/trombone.wav new file mode 100644 index 0000000000000000000000000000000000000000..aa308b1c0986d40ba4b725af2bc27cca075db88d --- /dev/null +++ b/demos/blocks_neural_instrument_coding/trombone.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:141e4dd80b5e9a31f5c5f2f862b48a5802c334e594227b2955967e70df9126a2 +size 384044 diff --git a/demos/blocks_outputs/config.json b/demos/blocks_outputs/config.json new file mode 100644 index 0000000000000000000000000000000000000000..f76ac02dc70e829fd695a7dd36365297c25b0a49 --- /dev/null +++ b/demos/blocks_outputs/config.json @@ -0,0 +1,325 @@ +{ + "mode": "blocks", + "components": [ + { + "id": 1, + "type": "column", + "props": { + "type": "column", + "variant": "default", + "css": {}, + "default_value": true + } + }, + { + "id": 2, + "type": "textbox", + "props": { + "lines": 1, + "max_lines": 20, + "default_value": "", + "label": "Small Textbox", + "show_label": false, + "name": "textbox", + "css": {} + } + }, + { + "id": 3, + "type": "textbox", + "props": { + "lines": 5, + "max_lines": 20, + "default_value": "", + "label": "Large Textbox", + "show_label": false, + "name": "textbox", + "css": {} + } + }, + { + "id": 4, + "type": "number", + "props": { + "label": "Number", + "show_label": false, + "name": "number", + "css": {} + } + }, + { + "id": 5, + "type": "checkbox", + "props": { + "default_value": false, + "label": "Checkbox", + "show_label": false, + "name": "checkbox", + "css": {} + } + }, + { + "id": 6, + "type": "checkboxgroup", + "props": { + "choices": [ + "One", + "Two", + "Three" + ], + "default_value": [], + "label": "Checkbox Group", + "show_label": false, + "name": "checkboxgroup", + "css": {} + } + }, + { + "id": 7, + "type": "radio", + "props": { + "choices": [ + "One", + "Two", + "Three" + ], + "default_value": "One", + "label": "Radio", + "show_label": false, + "name": "radio", + "css": {} + } + }, + { + "id": 8, + "type": "dropdown", + "props": { + "choices": [ + "One", + "Two", + "Three" + ], + "default_value": "One", + "label": "Dropdown", + "show_label": false, + "name": "dropdown", + "css": {} + } + }, + { + "id": 9, + "type": "slider", + "props": { + "minimum": 0, + "maximum": 100, + "step": 1, + "default_value": 0, + "label": "Slider", + "show_label": false, + "name": "slider", + "css": {} + } + }, + { + "id": 10, + "type": "audio", + "props": { + "source": "upload", + "show_label": false, + "name": "audio", + "css": {} + } + }, + { + "id": 11, + "type": "file", + "props": { + "file_count": "single", + "show_label": false, + "name": "file", + "css": {} + } + }, + { + "id": 12, + "type": "video", + "props": { + "source": "upload", + "show_label": false, + "name": "video", + "css": {} + } + }, + { + "id": 13, + "type": "image", + "props": { + "image_mode": "RGB", + "source": "upload", + "tool": "editor", + "show_label": false, + "name": "image", + "css": {} + } + }, + { + "id": 14, + "type": "timeseries", + "props": { + "show_label": false, + "name": "timeseries", + "css": {} + } + }, + { + "id": 15, + "type": "dataframe", + "props": { + "datatype": "str", + "row_count": 1, + "col_count": 3, + "default_value": [ + [ + "", + "", + "" + ], + [ + "", + "", + "" + ], + [ + "", + "", + "" + ] + ], + "max_rows": 20, + "overflow_row_behaviour": "paginate", + "show_label": false, + "name": "dataframe", + "css": {} + } + }, + { + "id": 16, + "type": "html", + "props": { + "default_value": "", + "show_label": false, + "name": "html", + "css": {} + } + }, + { + "id": 17, + "type": "json", + "props": { + "default_value": "\"\"", + "show_label": false, + "name": "json", + "css": {} + } + }, + { + "id": 18, + "type": "markdown", + "props": { + "default_value": "", + "name": "markdown", + "css": {} + } + }, + { + "id": 19, + "type": "label", + "props": { + "show_label": false, + "name": "label", + "css": {} + } + }, + { + "id": 20, + "type": "highlightedtext", + "props": { + "show_legend": false, + "default_value": "", + "show_label": false, + "name": "highlightedtext", + "css": {} + } + } + ], + "theme": "default", + "enable_queue": false, + "layout": { + "id": 0, + "children": [ + { + "id": 1, + "children": [ + { + "id": 2 + }, + { + "id": 3 + }, + { + "id": 4 + }, + { + "id": 5 + }, + { + "id": 6 + }, + { + "id": 7 + }, + { + "id": 8 + }, + { + "id": 9 + }, + { + "id": 10 + }, + { + "id": 11 + }, + { + "id": 12 + }, + { + "id": 13 + }, + { + "id": 14 + }, + { + "id": 15 + }, + { + "id": 16 + }, + { + "id": 17 + }, + { + "id": 18 + }, + { + "id": 19 + }, + { + "id": 20 + } + ] + } + ] + }, + "dependencies": [] +} \ No newline at end of file diff --git a/demos/blocks_outputs/run.py b/demos/blocks_outputs/run.py new file mode 100644 index 0000000000000000000000000000000000000000..084be0da9c4b676974e06a2cd7dc87f4b11f95e0 --- /dev/null +++ b/demos/blocks_outputs/run.py @@ -0,0 +1,95 @@ +import gradio as gr + + +def make_markdown(): + return [ + [ + "# hello again", + "Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.", + '', + ], + [ + "## hello again again", + "Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.", + '', + ], + [ + "### hello thrice", + "Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.", + '', + ], + ] + + +with gr.Blocks() as demo: + with gr.Column(): + txt = gr.Textbox(label="Small Textbox", lines=1, show_label=False) + txt = gr.Textbox(label="Large Textbox", lines=5, show_label=False) + num = gr.Number(label="Number", show_label=False) + check = gr.Checkbox(label="Checkbox", show_label=False) + check_g = gr.CheckboxGroup( + label="Checkbox Group", choices=["One", "Two", "Three"], show_label=False + ) + radio = gr.Radio( + label="Radio", choices=["One", "Two", "Three"], show_label=False + ) + drop = gr.Dropdown( + label="Dropdown", choices=["One", "Two", "Three"], show_label=False + ) + slider = gr.Slider(label="Slider", show_label=False) + audio = gr.Audio(show_label=False) + file = gr.File(show_label=False) + video = gr.Video(show_label=False) + image = gr.Image(show_label=False) + ts = gr.Timeseries(show_label=False) + df = gr.Dataframe(show_label=False) + html = gr.HTML(show_label=False) + json = gr.JSON(show_label=False) + md = gr.Markdown(show_label=False) + label = gr.Label(show_label=False) + highlight = gr.HighlightedText(show_label=False) + gr.Dataframe(interactive=True, col_count=(3, "fixed"), label="Dataframe") + gr.Dataframe(interactive=True, col_count=4, label="Dataframe") + gr.Dataframe( + interactive=True, headers=["One", "Two", "Three", "Four"], label="Dataframe" + ) + gr.Dataframe( + interactive=True, + headers=["One", "Two", "Three", "Four"], + col_count=(4, "fixed"), + row_count=(7, "fixed"), + value=[[0, 0, 0, 0]], + label="Dataframe", + ) + gr.Dataframe( + interactive=True, headers=["One", "Two", "Three", "Four"], col_count=4 + ) + df = gr.DataFrame( + [ + [ + "# hello", + "Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.", + '', + ], + [ + "## hello", + "Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.", + '', + ], + [ + "### hello", + "Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.", + '', + ], + ], + headers=["One", "Two", "Three"], + wrap=True, + datatype=["markdown", "markdown", "html"], + interactive=True, + ) + btn = gr.Button("Run") + btn.click(fn=make_markdown, inputs=None, outputs=df) + + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_page_load/config.json b/demos/blocks_page_load/config.json new file mode 100644 index 0000000000000000000000000000000000000000..3f8f7159d4fdf73895af71a41768ad26c5893561 --- /dev/null +++ b/demos/blocks_page_load/config.json @@ -0,0 +1,58 @@ +{ + "mode": "blocks", + "components": [ + { + "id": 1, + "type": "textbox", + "props": { + "lines": 1, + "max_lines": 20, + "default_value": "Frank", + "label": "Name", + "show_label": true, + "name": "textbox", + "css": {} + } + }, + { + "id": 2, + "type": "textbox", + "props": { + "lines": 1, + "max_lines": 20, + "default_value": "", + "label": "Output", + "show_label": true, + "name": "textbox", + "css": {} + } + } + ], + "theme": "default", + "enable_queue": false, + "layout": { + "id": 0, + "children": [ + { + "id": 1 + }, + { + "id": 2 + } + ] + }, + "dependencies": [ + { + "targets": [], + "trigger": "load", + "inputs": [ + 1 + ], + "outputs": [ + 2 + ], + "status_tracker": null, + "queue": null + } + ] +} \ No newline at end of file diff --git a/demos/blocks_page_load/run.py b/demos/blocks_page_load/run.py new file mode 100644 index 0000000000000000000000000000000000000000..5022a41023f15f69513bb1734f30aa621cab8e72 --- /dev/null +++ b/demos/blocks_page_load/run.py @@ -0,0 +1,15 @@ +import gradio as gr + + +def print_message(n): + return "Welcome! This page has loaded for " + n + + +with gr.Blocks() as demo: + t = gr.Textbox("Frank", label="Name") + t2 = gr.Textbox(label="Output") + demo.load(print_message, t, t2) + + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_plug/run.py b/demos/blocks_plug/run.py new file mode 100644 index 0000000000000000000000000000000000000000..97684fa61b5c6a66eb5e07fa4162510f9d155415 --- /dev/null +++ b/demos/blocks_plug/run.py @@ -0,0 +1,36 @@ +import gradio as gr + + +def change_tab(): + return gr.Tabs.update(selected=2) + + +identity_demo, input_demo, output_demo = gr.Blocks(), gr.Blocks(), gr.Blocks() + +with identity_demo: + gr.Interface(lambda x: x, "text", "text") + +with input_demo: + t = gr.Textbox(label="Enter your text here") + with gr.Row(): + btn = gr.Button("Submit") + clr = gr.Button("Clear") + clr.click(lambda x: "", t, t) + +with output_demo: + gr.Textbox("This is a static output") + +with gr.Blocks() as demo: + gr.Markdown("Three demos in one!") + with gr.Tabs(selected=1) as tabs: + with gr.TabItem("Text Identity", id=0): + identity_demo.render() + with gr.TabItem("Text Input", id=1): + input_demo.render() + with gr.TabItem("Text Static", id=2): + output_demo.render() + btn = gr.Button("Change tab") + btn.click(inputs=None, outputs=tabs, fn=change_tab) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_random_slider/run.py b/demos/blocks_random_slider/run.py new file mode 100644 index 0000000000000000000000000000000000000000..105ddcb0e4f8e77d7da977400d25e95210add2fc --- /dev/null +++ b/demos/blocks_random_slider/run.py @@ -0,0 +1,18 @@ + +import gradio as gr + + +def func(slider_1, slider_2): + return slider_1 * 5 + slider_2 + + +with gr.Blocks() as demo: + slider = gr.Slider(minimum=-10.2, maximum=15, label="Random Slider (Static)", randomize=True) + slider_1 = gr.Slider(minimum=100, maximum=200, label="Random Slider (Input 1)", randomize=True) + slider_2 = gr.Slider(minimum=10, maximum=23.2, label="Random Slider (Input 2)", randomize=True) + slider_3 = gr.Slider(value=3, label="Non random slider") + btn = gr.Button("Run") + btn.click(func, inputs=[slider_1, slider_2], outputs=gr.Number()) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_scroll/run.py b/demos/blocks_scroll/run.py new file mode 100644 index 0000000000000000000000000000000000000000..2b2194dfb8050fc2a50b9491f2e57f672289ffe7 --- /dev/null +++ b/demos/blocks_scroll/run.py @@ -0,0 +1,24 @@ +import gradio as gr + + +demo = gr.Blocks() + +with demo: + inp = gr.Textbox(placeholder="Enter text.") + scroll_btn = gr.Button("Scroll") + no_scroll_btn = gr.Button("No Scroll") + big_block = gr.HTML(""" +
+ """) + out = gr.Textbox() + + scroll_btn.click(lambda x: x, + inputs=inp, + outputs=out, + scroll_to_output=True) + no_scroll_btn.click(lambda x: x, + inputs=inp, + outputs=out) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/blocks_simple_squares/config.json b/demos/blocks_simple_squares/config.json new file mode 100644 index 0000000000000000000000000000000000000000..08f41782884614ddd96ec8a6cab432331b14edea --- /dev/null +++ b/demos/blocks_simple_squares/config.json @@ -0,0 +1,115 @@ +{ + "version": "3.0.6", + "mode": "blocks", + "dev_mode": true, + "components": [ + { + "id": 1, + "type": "variable", + "props": { + "value": 0, + "show_label": true, + "name": "variable", + "visible": true, + "style": {} + } + }, + { + "id": 2, + "type": "number", + "props": { + "value": 0.0, + "show_label": true, + "name": "number", + "visible": true, + "style": { + "text_color": "blue", + "container_bg_color": "yellow" + } + } + }, + { + "id": 3, + "type": "button", + "props": { + "value": "Next Square", + "variant": "secondary", + "name": "button", + "visible": true, + "elem_id": "btn", + "style": { + "rounded": false, + "bg_color": "purple" + } + } + }, + { + "id": 4, + "type": "variable", + "props": { + "value": { + "a": "a" + }, + "show_label": true, + "name": "variable", + "visible": true, + "style": {} + } + }, + { + "id": 5, + "type": "json", + "props": { + "show_label": true, + "name": "json", + "visible": true, + "style": {} + } + } + ], + "theme": "default", + "css": "#btn {color: red}", + "enable_queue": false, + "layout": { + "id": 0, + "children": [ + { + "id": 1 + }, + { + "id": 2 + }, + { + "id": 3 + }, + { + "id": 4 + }, + { + "id": 5 + } + ] + }, + "dependencies": [ + { + "targets": [ + 3 + ], + "trigger": "click", + "inputs": [ + 1, + 4 + ], + "outputs": [ + 1, + 2, + 4, + 5 + ], + "backend_fn": true, + "js": null, + "status_tracker": null, + "queue": null + } + ] +} \ No newline at end of file diff --git a/demos/blocks_simple_squares/run.py b/demos/blocks_simple_squares/run.py new file mode 100644 index 0000000000000000000000000000000000000000..33519c743be15483757ced87b98ac9be20a8780d --- /dev/null +++ b/demos/blocks_simple_squares/run.py @@ -0,0 +1,23 @@ +import gradio as gr + +demo = gr.Blocks(css="#btn {color: red}") + +with demo: + default_json = {"a": "a"} + + num = gr.Variable(value=0) + squared = gr.Number(value=0) + btn = gr.Button("Next Square", elem_id="btn").style(rounded=False) + + stats = gr.Variable(value=default_json) + table = gr.JSON() + + def increase(var, stats_history): + var += 1 + stats_history[str(var)] = var**2 + return var, var**2, stats_history, stats_history + + btn.click(increase, [num, stats], [num, squared, stats, table]) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_speech_text_sentiment/requirements.txt b/demos/blocks_speech_text_sentiment/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..39dab0fdd98d55da5ce06ddf1dacbdbda14b1372 --- /dev/null +++ b/demos/blocks_speech_text_sentiment/requirements.txt @@ -0,0 +1,2 @@ +torch +transformers \ No newline at end of file diff --git a/demos/blocks_speech_text_sentiment/run.py b/demos/blocks_speech_text_sentiment/run.py new file mode 100644 index 0000000000000000000000000000000000000000..2c9343fd1233859ec226129c890fc43939f352a1 --- /dev/null +++ b/demos/blocks_speech_text_sentiment/run.py @@ -0,0 +1,32 @@ +from transformers import pipeline + +import gradio as gr + +asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h") +classifier = pipeline("text-classification") + + +def speech_to_text(speech): + text = asr(speech)["text"] + return text + + +def text_to_sentiment(text): + return classifier(text)[0]["label"] + + +demo = gr.Blocks() + +with demo: + audio_file = gr.Audio(type="filepath") + text = gr.Textbox() + label = gr.Label() + + b1 = gr.Button("Recognize Speech") + b2 = gr.Button("Classify Sentiment") + + b1.click(speech_to_text, inputs=audio_file, outputs=text) + b2.click(text_to_sentiment, inputs=text, outputs=label) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_static_textbox/run.py b/demos/blocks_static_textbox/run.py new file mode 100644 index 0000000000000000000000000000000000000000..cf05b243a07084d7382c4df9162df8bf1b0b41fe --- /dev/null +++ b/demos/blocks_static_textbox/run.py @@ -0,0 +1,10 @@ +import gradio as gr + +demo = gr.Blocks() + +with demo: + gr.Textbox("Hello") + gr.Number(5) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_style/run.py b/demos/blocks_style/run.py new file mode 100644 index 0000000000000000000000000000000000000000..9e3b9e58a89c548f03c6bff3a5d2615c2d8f64ef --- /dev/null +++ b/demos/blocks_style/run.py @@ -0,0 +1,104 @@ +import gradio as gr + +with gr.Blocks(title="Styling Examples") as demo: + with gr.Column(): + txt = gr.Textbox(label="Small Textbox", lines=1).style( + rounded=False, + border=False, + container=False, + ) + + num = gr.Number(label="Number", show_label=False).style( + rounded=False, + border=False, + container=False, + ) + slider = gr.Slider(label="Slider", show_label=False).style( + container=False, + ) + check = gr.Checkbox(label="Checkbox", show_label=False).style( + rounded=False, + border=False, + container=False, + ) + check_g = gr.CheckboxGroup( + label="Checkbox Group", choices=["One", "Two", "Three"], show_label=False + ).style(rounded=False, container=False, item_container=False) + radio = gr.Radio( + label="Radio", choices=["One", "Two", "Three"], show_label=False + ).style( + item_container=False, + container=False, + ) + drop = gr.Dropdown( + label="Dropdown", choices=["One", "Two", "Three"], show_label=False + ).style( + rounded=False, + border=False, + container=False, + ) + image = gr.Image(show_label=False).style( + rounded=False, + ) + video = gr.Video(show_label=False).style( + rounded=False, + ) + audio = gr.Audio(show_label=False).style( + rounded=False, + ) + file = gr.File(show_label=False).style( + rounded=False, + ) + df = gr.Dataframe(show_label=False).style( + rounded=False, + ) + + ts = gr.Timeseries(show_label=False).style( + rounded=False, + ) + label = gr.Label().style( + container=False, + ) + highlight = gr.HighlightedText( + "+ hello. - goodbye", show_label=False, color_map={"+": "green", "-": "red"} + ).style(rounded=False, container=False) + json = gr.JSON().style(container=False) + html = gr.HTML(show_label=False).style() + gallery = gr.Gallery().style( + rounded=False, + grid=(3, 3, 1), + height="auto", + container=False, + ) + chat = gr.Chatbot("hi", color_map=("pink", "blue")).style( + rounded=False, + ) + + model = gr.Model3D().style( + rounded=False, + ) + + gr.Plot().style() + md = gr.Markdown(show_label=False).style() + + highlight = gr.HighlightedText().style( + rounded=False, + ) + + btn = gr.Button("Run").style( + rounded=False, + full_width=True, + border=False, + ) + + # Not currently public + # TODO: Uncomment at next release + # gr.Dataset().style( + # rounded=False, + # margin=False, + # border=False, + # ) + + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_textbox_max_lines/run.py b/demos/blocks_textbox_max_lines/run.py new file mode 100644 index 0000000000000000000000000000000000000000..a50ec1a7f7e2f6f31a555a9c3a2117546f175e67 --- /dev/null +++ b/demos/blocks_textbox_max_lines/run.py @@ -0,0 +1,13 @@ +import gradio as gr + + +def greet(name: str, repeat: float): + return "Hello " + name * int(repeat) + "!!" + + +demo = gr.Interface( + fn=greet, inputs=[gr.Textbox(lines=2, max_lines=4), gr.Number()], outputs="textarea" +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_update/run.py b/demos/blocks_update/run.py new file mode 100644 index 0000000000000000000000000000000000000000..35576951d117a12a1bddc0ed096d9bfec28e93e7 --- /dev/null +++ b/demos/blocks_update/run.py @@ -0,0 +1,46 @@ +import gradio as gr + +with gr.Blocks() as demo: + gr.Markdown( + """ + # Animal Generator + Once you select a species, the detail panel should be visible. + """ + ) + + species = gr.Radio(label="Animal Class", choices=["Mammal", "Fish", "Bird"]) + animal = gr.Dropdown(label="Animal", choices=[]) + + with gr.Column(visible=False) as details_col: + weight = gr.Slider(0, 20) + details = gr.Textbox(label="Extra Details") + generate_btn = gr.Button("Generate") + output = gr.Textbox(label="Output") + + species_map = { + "Mammal": ["Elephant", "Giraffe", "Hamster"], + "Fish": ["Shark", "Salmon", "Tuna"], + "Bird": ["Chicken", "Eagle", "Hawk"], + } + + def filter_species(species): + return gr.Dropdown.update( + choices=species_map[species], value=species_map[species][1] + ), gr.update(visible=True) + + species.change(filter_species, species, [animal, details_col]) + + def filter_weight(animal): + if animal in ("Elephant", "Shark", "Giraffe"): + return gr.update(maximum=100) + else: + return gr.update(maximum=20) + + animal.change(filter_weight, animal, weight) + weight.change(lambda w: gr.update(lines=int(w / 10) + 1), weight, details) + + generate_btn.click(lambda x: x, details, output) + + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/blocks_webcam/run.py b/demos/blocks_webcam/run.py new file mode 100644 index 0000000000000000000000000000000000000000..4b9259868408d77c816b082b60e5d838a443a60e --- /dev/null +++ b/demos/blocks_webcam/run.py @@ -0,0 +1,13 @@ +import numpy as np + +import gradio as gr + + +def snap(image): + return np.flipud(image) + + +demo = gr.Interface(snap, "webcam", "image") + +if __name__ == "__main__": + demo.launch() diff --git a/demos/blocks_xray/config.json b/demos/blocks_xray/config.json new file mode 100644 index 0000000000000000000000000000000000000000..f4153b9097f6c664c084917183dff475ed04406b --- /dev/null +++ b/demos/blocks_xray/config.json @@ -0,0 +1,339 @@ +{ + "version": "3.1.1\n", + "mode": "blocks", + "dev_mode": true, + "components": [ + { + "id": 1, + "type": "markdown", + "props": { + "value": "

Detect Disease From Scan

\n

With this model you can lorem ipsum

\n\n", + "name": "markdown", + "visible": true, + "style": {} + } + }, + { + "id": 2, + "type": "checkboxgroup", + "props": { + "choices": [ + "Covid", + "Malaria", + "Lung Cancer" + ], + "value": [], + "label": "Disease to Scan For", + "show_label": true, + "name": "checkboxgroup", + "visible": true, + "style": {} + } + }, + { + "id": 3, + "type": "tabs", + "props": { + "visible": true, + "style": {} + } + }, + { + "id": 4, + "type": "tabitem", + "props": { + "label": "X-ray", + "visible": true, + "style": {} + } + }, + { + "id": 5, + "type": "row", + "props": { + "type": "row", + "visible": true, + "style": {} + } + }, + { + "id": 6, + "type": "image", + "props": { + "image_mode": "RGB", + "source": "upload", + "tool": "editor", + "streaming": false, + "mirror_webcam": true, + "show_label": true, + "name": "image", + "visible": true, + "style": {} + } + }, + { + "id": 7, + "type": "json", + "props": { + "show_label": true, + "name": "json", + "visible": true, + "style": {} + } + }, + { + "id": 8, + "type": "button", + "props": { + "value": "Run", + "variant": "secondary", + "name": "button", + "visible": true, + "style": {} + } + }, + { + "id": 9, + "type": "statustracker", + "props": { + "cover_container": true, + "name": "statustracker", + "visible": true, + "style": {} + } + }, + { + "id": 10, + "type": "tabitem", + "props": { + "label": "CT Scan", + "visible": true, + "style": {} + } + }, + { + "id": 11, + "type": "row", + "props": { + "type": "row", + "visible": true, + "style": {} + } + }, + { + "id": 12, + "type": "image", + "props": { + "image_mode": "RGB", + "source": "upload", + "tool": "editor", + "streaming": false, + "mirror_webcam": true, + "show_label": true, + "name": "image", + "visible": true, + "style": {} + } + }, + { + "id": 13, + "type": "json", + "props": { + "show_label": true, + "name": "json", + "visible": true, + "style": {} + } + }, + { + "id": 14, + "type": "button", + "props": { + "value": "Run", + "variant": "secondary", + "name": "button", + "visible": true, + "style": {} + } + }, + { + "id": 15, + "type": "statustracker", + "props": { + "cover_container": true, + "name": "statustracker", + "visible": true, + "style": {} + } + }, + { + "id": 16, + "type": "button", + "props": { + "value": "Upload Results", + "variant": "secondary", + "name": "button", + "visible": true, + "style": {} + } + }, + { + "id": 17, + "type": "statustracker", + "props": { + "cover_container": false, + "name": "statustracker", + "visible": true, + "style": {} + } + } + ], + "theme": "default", + "css": null, + "title": "Gradio", + "enable_queue": false, + "layout": { + "id": 0, + "children": [ + { + "id": 1 + }, + { + "id": 2 + }, + { + "id": 3, + "children": [ + { + "id": 4, + "children": [ + { + "id": 5, + "children": [ + { + "id": 6 + }, + { + "id": 7 + } + ] + }, + { + "id": 8 + }, + { + "id": 9 + } + ] + }, + { + "id": 10, + "children": [ + { + "id": 11, + "children": [ + { + "id": 12 + }, + { + "id": 13 + } + ] + }, + { + "id": 14 + }, + { + "id": 15 + } + ] + } + ] + }, + { + "id": 16 + }, + { + "id": 17 + } + ] + }, + "dependencies": [ + { + "targets": [ + 8 + ], + "trigger": "click", + "inputs": [ + 2, + 6 + ], + "outputs": [ + 7 + ], + "backend_fn": true, + "js": null, + "status_tracker": 9, + "queue": null, + "api_name": "xray_model", + "scroll_to_output": false, + "show_progress": true, + "documentation": [ + [ + null, + null + ], + [ + null + ] + ] + }, + { + "targets": [ + 14 + ], + "trigger": "click", + "inputs": [ + 2, + 12 + ], + "outputs": [ + 13 + ], + "backend_fn": true, + "js": null, + "status_tracker": 15, + "queue": null, + "api_name": "ct_model", + "scroll_to_output": false, + "show_progress": true, + "documentation": [ + [ + null, + null + ], + [ + null + ] + ] + }, + { + "targets": [ + 16 + ], + "trigger": "click", + "inputs": [ + 13, + 7 + ], + "outputs": [], + "backend_fn": true, + "js": null, + "status_tracker": 17, + "queue": null, + "api_name": null, + "scroll_to_output": false, + "show_progress": true + } + ] +} \ No newline at end of file diff --git a/demos/blocks_xray/run.py b/demos/blocks_xray/run.py new file mode 100644 index 0000000000000000000000000000000000000000..4a98f39cb527986afaf21b1a587092feed176298 --- /dev/null +++ b/demos/blocks_xray/run.py @@ -0,0 +1,67 @@ +import gradio as gr +import random +import time + + +def xray_model(diseases, img): + time.sleep(4) + return [{disease: random.random() for disease in diseases}] + + +def ct_model(diseases, img): + time.sleep(3) + return [{disease: 0.1 for disease in diseases}] + + +with gr.Blocks() as demo: + gr.Markdown( + """ +# Detect Disease From Scan +With this model you can lorem ipsum +- ipsum 1 +- ipsum 2 +""" + ) + disease = gr.CheckboxGroup( + choices=["Covid", "Malaria", "Lung Cancer"], label="Disease to Scan For" + ) + + with gr.Tabs(): + with gr.TabItem("X-ray") as x_tab: + with gr.Row(): + xray_scan = gr.Image() + xray_results = gr.JSON() + xray_run = gr.Button("Run") + xray_progress = gr.StatusTracker(cover_container=True) + xray_run.click( + xray_model, + inputs=[disease, xray_scan], + outputs=xray_results, + status_tracker=xray_progress, + api_name="xray_model" + ) + + with gr.TabItem("CT Scan"): + with gr.Row(): + ct_scan = gr.Image() + ct_results = gr.JSON() + ct_run = gr.Button("Run") + ct_progress = gr.StatusTracker(cover_container=True) + ct_run.click( + ct_model, + inputs=[disease, ct_scan], + outputs=ct_results, + status_tracker=ct_progress, + api_name="ct_model" + ) + + upload_btn = gr.Button("Upload Results") + upload_btn.click( + lambda ct, xr: time.sleep(5), + inputs=[ct_results, xray_results], + outputs=[], + status_tracker=gr.StatusTracker(), + ) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/calculator/examples/log.csv b/demos/calculator/examples/log.csv new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demos/calculator/run.py b/demos/calculator/run.py new file mode 100644 index 0000000000000000000000000000000000000000..ac1bd12235f9f4be5feced048d571bde8f6b9be3 --- /dev/null +++ b/demos/calculator/run.py @@ -0,0 +1,31 @@ +import gradio as gr + +def calculator(num1, operation, num2): + if operation == "add": + return num1 + num2 + elif operation == "subtract": + return num1 - num2 + elif operation == "multiply": + return num1 * num2 + elif operation == "divide": + return num1 / num2 + +demo = gr.Interface( + calculator, + [ + "number", + gr.Radio(["add", "subtract", "multiply", "divide"]), + "number" + ], + "number", + examples=[ + [5, "add", 3], + [4, "divide", 2], + [-4, "multiply", 2.5], + [0, "subtract", 1.2], + ], + title="Toy Calculator", + description="Here's a sample toy calculator. Enjoy!", +) +if __name__ == "__main__": + demo.launch() diff --git a/demos/calculator/screenshot.gif b/demos/calculator/screenshot.gif new file mode 100644 index 0000000000000000000000000000000000000000..8ae0d0494adf069b4e23cf30956b43262258021c --- /dev/null +++ b/demos/calculator/screenshot.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3698fb03b6507ff954de47559f6830dfff88aa66487d2029a9bcf1c2f3762e08 +size 5718090 diff --git a/demos/calculator_blocks/run.py b/demos/calculator_blocks/run.py new file mode 100644 index 0000000000000000000000000000000000000000..957b8d9ab879493ea5b6072dbe5acdf1d6c52524 --- /dev/null +++ b/demos/calculator_blocks/run.py @@ -0,0 +1,33 @@ +import gradio as gr + + +def calculator(num1, operation, num2): + if operation == "add": + return num1 + num2 + elif operation == "subtract": + return num1 - num2 + elif operation == "multiply": + return num1 * num2 + elif operation == "divide": + return num1 / num2 + + +with gr.Blocks() as demo: + with gr.Row(): + with gr.Column(): + num_1 = gr.Number(value=4) + operation = gr.Radio(["add", "subtract", "multiply", "divide"]) + num_2 = gr.Number(value=0) + submit_btn = gr.Button(value="Calculate") + with gr.Column(): + result = gr.Number() + + submit_btn.click(calculator, inputs=[num_1, operation, num_2], outputs=[result]) + examples = gr.Examples(examples=[[5, "add", 3], + [4, "divide", 2], + [-4, "multiply", 2.5], + [0, "subtract", 1.2]], + inputs=[num_1, operation, num_2]) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/calculator_blocks_cached/run.py b/demos/calculator_blocks_cached/run.py new file mode 100644 index 0000000000000000000000000000000000000000..084884b1d99eccb8021095d729b99683a2c33436 --- /dev/null +++ b/demos/calculator_blocks_cached/run.py @@ -0,0 +1,36 @@ +import gradio as gr + + +def calculator(num1, operation, num2): + if operation == "add": + return num1 + num2 + elif operation == "subtract": + return num1 - num2 + elif operation == "multiply": + return num1 * num2 + elif operation == "divide": + return num1 / num2 + + +with gr.Blocks() as demo: + with gr.Row(): + with gr.Column(): + num_1 = gr.Number() + operation = gr.Radio(["add", "subtract", "multiply", "divide"]) + num_2 = gr.Number() + submit_btn = gr.Button(value="Calculate") + with gr.Column(): + result = gr.Number() + + submit_btn.click(calculator, inputs=[num_1, operation, num_2], outputs=[result]) + examples = gr.Examples(examples=[[5, "add", 3], + [4, "divide", 2], + [-4, "multiply", 2.5], + [0, "subtract", 1.2]], + inputs=[num_1, operation, num_2], + outputs=[result], + fn=calculator, + cache_examples=True) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/calculator_live/run.py b/demos/calculator_live/run.py new file mode 100644 index 0000000000000000000000000000000000000000..09856c099eb6ffa080b355d09bc3ecebe3ea714c --- /dev/null +++ b/demos/calculator_live/run.py @@ -0,0 +1,24 @@ +import gradio as gr + +def calculator(num1, operation, num2): + if operation == "add": + return num1 + num2 + elif operation == "subtract": + return num1 - num2 + elif operation == "multiply": + return num1 * num2 + elif operation == "divide": + return num1 / num2 + +demo = gr.Interface( + calculator, + [ + "number", + gr.Radio(["add", "subtract", "multiply", "divide"]), + "number" + ], + "number", + live=True, +) +if __name__ == "__main__": + demo.launch() diff --git a/demos/calculator_live/screenshot.gif b/demos/calculator_live/screenshot.gif new file mode 100644 index 0000000000000000000000000000000000000000..d1da057e4aaa02fca4e2a05160c5afd2b43bbaa8 --- /dev/null +++ b/demos/calculator_live/screenshot.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aa2424ecfa662e71fdb73f35809fb06f2ce9cbf0be7ffea3a72c05f0513063e4 +size 776302 diff --git a/demos/chatbot_demo/run.py b/demos/chatbot_demo/run.py new file mode 100644 index 0000000000000000000000000000000000000000..482c9908994cd229c1ac7d52f3877f04c5813848 --- /dev/null +++ b/demos/chatbot_demo/run.py @@ -0,0 +1,26 @@ +import random +import gradio as gr + +def chat(message, history): + history = history or [] + message = message.lower() + if message.startswith("how many"): + response = random.randint(1, 10) + elif message.startswith("how"): + response = random.choice(["Great", "Good", "Okay", "Bad"]) + elif message.startswith("where"): + response = random.choice(["Here", "There", "Somewhere"]) + else: + response = "I don't know" + history.append((message, response)) + return history, history + +chatbot = gr.Chatbot().style(color_map=("green", "pink")) +demo = gr.Interface( + chat, + ["text", "state"], + [chatbot, "state"], + allow_flagging="never", +) +if __name__ == "__main__": + demo.launch() diff --git a/demos/chatbot_demo/screenshot.gif b/demos/chatbot_demo/screenshot.gif new file mode 100644 index 0000000000000000000000000000000000000000..965c2a91e34723aa878cd743366f44fff898928e --- /dev/null +++ b/demos/chatbot_demo/screenshot.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:819b3338ce7fee6df6835a868bcb56f9f15deace2043150c55ea761fb0bdd8f9 +size 601861 diff --git a/demos/chatbot_demo/screenshot.png b/demos/chatbot_demo/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..33780f6b41a997d25dea98c0239233a05346653c --- /dev/null +++ b/demos/chatbot_demo/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1f089f3aeba8bf663216d0becec86f312a3cd81635ee4f4c1adc5de2688ac041 +size 183749 diff --git a/demos/color_generator/run.py b/demos/color_generator/run.py new file mode 100644 index 0000000000000000000000000000000000000000..3b18bd0839fba3897d92660a7eb8bd79d493d2f1 --- /dev/null +++ b/demos/color_generator/run.py @@ -0,0 +1,63 @@ +import gradio as gr +import cv2 +import numpy as np +import random + + +# Convert decimal color to hexadecimal color +def RGB_to_Hex(rgb): + color = "#" + for i in rgb: + num = int(i) + color += str(hex(num))[-2:].replace("x", "0").upper() + return color + + +# Randomly generate light or dark colors +def random_color(is_light=True): + return ( + random.randint(0, 127) + int(is_light) * 128, + random.randint(0, 127) + int(is_light) * 128, + random.randint(0, 127) + int(is_light) * 128, + ) + + +def switch_color(color_style): + if color_style == "light": + is_light = True + elif color_style == "dark": + is_light = False + back_color_ = random_color(is_light) # Randomly generate colors + back_color = RGB_to_Hex(back_color_) # Convert to hexadecimal + + # Draw color pictures. + w, h = 50, 50 + img = np.zeros((h, w, 3), np.uint8) + cv2.rectangle(img, (0, 0), (w, h), back_color_, thickness=-1) + + return back_color, back_color, img + + +inputs = [gr.Radio(["light", "dark"], value="light")] + +outputs = [ + gr.ColorPicker(label="color"), + gr.Textbox(label="hexadecimal color"), + gr.Image(type="numpy", label="color picture"), +] + +title = "Color Generator" +description = ( + "Click the Submit button, and a dark or light color will be randomly generated." +) + +demo = gr.Interface( + fn=switch_color, + inputs=inputs, + outputs=outputs, + title=title, + description=description, +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/color_picker/rabbit.png b/demos/color_picker/rabbit.png new file mode 100644 index 0000000000000000000000000000000000000000..09139e45f7de42faf07bdd3c6a5548b1e02924b0 --- /dev/null +++ b/demos/color_picker/rabbit.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:700d0d0ac98df8c706ac3a9c110d2efc1849be987b510e9393862807ff41e097 +size 11164 diff --git a/demos/color_picker/requirements.txt b/demos/color_picker/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..5873a2224bf91656bb173e87f56252466b812f4e --- /dev/null +++ b/demos/color_picker/requirements.txt @@ -0,0 +1 @@ +Pillow \ No newline at end of file diff --git a/demos/color_picker/run.py b/demos/color_picker/run.py new file mode 100644 index 0000000000000000000000000000000000000000..b2e460a852c979c2b5c0e01e7592525176069127 --- /dev/null +++ b/demos/color_picker/run.py @@ -0,0 +1,44 @@ +import gradio as gr +import numpy as np +import os +from PIL import Image, ImageColor + + +def change_color(icon, color): + + """ + Function that given an icon in .png format changes its color + Args: + icon: Icon whose color needs to be changed. + color: Chosen color with which to edit the input icon. + Returns: + edited_image: Edited icon. + """ + img = icon.convert("LA") + img = img.convert("RGBA") + image_np = np.array(icon) + _, _, _, alpha = image_np.T + mask = alpha > 0 + image_np[..., :-1][mask.T] = ImageColor.getcolor(color, "RGB") + edited_image = Image.fromarray(image_np) + return edited_image + + +inputs = [ + gr.Image(label="icon", type="pil", image_mode="RGBA"), + gr.ColorPicker(label="color"), +] +outputs = gr.Image(label="colored icon") + +demo = gr.Interface( + fn=change_color, + inputs=inputs, + outputs=outputs, + examples=[ + [os.path.join(os.path.dirname(__file__), "rabbit.png"), "#ff0000"], + [os.path.join(os.path.dirname(__file__), "rabbit.png"), "#0000FF"], + ], +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/color_picker/screenshot.gif b/demos/color_picker/screenshot.gif new file mode 100644 index 0000000000000000000000000000000000000000..3283240a8687e79e7d2c70e4a36c494a4a97adc3 --- /dev/null +++ b/demos/color_picker/screenshot.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:339847aef03df1b31807d72c4e697a94932d8c02588c22473ceefea81d494a5e +size 411225 diff --git a/demos/color_picker/screenshot.png b/demos/color_picker/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..a034b2bf52106654dfc73b9d40ac8866ece3113c --- /dev/null +++ b/demos/color_picker/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7b9f4e76a76dac6f2f0f2b923852ff67db785a3e04eb83eb3f9a1000e142676e +size 17941 diff --git a/demos/components_demos/requirements.txt b/demos/components_demos/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..8248f97be94ef0dabec7bd3223a2d0d51a0c847c --- /dev/null +++ b/demos/components_demos/requirements.txt @@ -0,0 +1,2 @@ +matplotlib +numpy \ No newline at end of file diff --git a/demos/components_demos/run.py b/demos/components_demos/run.py new file mode 100644 index 0000000000000000000000000000000000000000..08fa8866dd4f2bd175fe72a637595778389f637f --- /dev/null +++ b/demos/components_demos/run.py @@ -0,0 +1,100 @@ +# This file is used to embed components in gradio.app/docs + +import gradio as gr + + +with gr.Blocks() as Textbox_demo: + gr.Textbox() + +with gr.Blocks() as Number_demo: + gr.Number() + +with gr.Blocks() as Slider_demo: + gr.Slider() + +with gr.Blocks() as Checkbox_demo: + gr.Checkbox() + +with gr.Blocks() as CheckboxGroup_demo: + gr.CheckboxGroup(choices=["First Choice", "Second Choice", "Third Choice"]) + +with gr.Blocks() as Radio_demo: + gr.Radio(choices=["First Choice", "Second Choice", "Third Choice"]) + +with gr.Blocks() as Dropdown_demo: + gr.Dropdown(choices=["First Choice", "Second Choice", "Third Choice"]) + +with gr.Blocks() as Image_demo: + gr.Image() + +with gr.Blocks() as Video_demo: + gr.Video() + +with gr.Blocks() as Audio_demo: + gr.Audio() + +with gr.Blocks() as File_demo: + gr.File() + +with gr.Blocks() as Dataframe_demo: + gr.Dataframe(interactive=True) + +with gr.Blocks() as Timeseries_demo: + gr.Timeseries() + +with gr.Blocks() as Variable_demo: + gr.Variable() + +with gr.Blocks() as Button_demo: + gr.Button() + +with gr.Blocks() as ColorPicker_demo: + gr.ColorPicker() + +with gr.Blocks() as Label_demo: + gr.Label(value={"First Label": 0.7, "Second Label": 0.2, "Third Label": 0.1}) + +with gr.Blocks() as HighlightedText_demo: + gr.HighlightedText(value=[("Text","Label 1"),("to be","Label 2"),("highlighted","Label 3")]) + +with gr.Blocks() as JSON_demo: + gr.JSON(value={"Key 1": "Value 1", "Key 2": {"Key 3": "Value 2", "Key 4": "Value 3"}, "Key 5": ["Item 1", "Item 2", "Item 3"]}) + +with gr.Blocks() as HTML_demo: + gr.HTML(value="

Gradio Docs Readers: visitor badge

") + +with gr.Blocks() as Gallery_demo: + cheetahs = [ + "https://upload.wikimedia.org/wikipedia/commons/0/09/TheCheethcat.jpg", + "https://nationalzoo.si.edu/sites/default/files/animals/cheetah-003.jpg", + "https://img.etimg.com/thumb/msid-50159822,width-650,imgsize-129520,,resizemode-4,quality-100/.jpg", + "https://nationalzoo.si.edu/sites/default/files/animals/cheetah-002.jpg", + "https://images.theconversation.com/files/375893/original/file-20201218-13-a8h8uq.jpg?ixlib=rb-1.1.0&rect=16%2C407%2C5515%2C2924&q=45&auto=format&w=496&fit=clip", + "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQeSdQE5kHykTdB970YGSW3AsF6MHHZzY4QiQ&usqp=CAU", + "https://www.lifegate.com/app/uploads/ghepardo-primo-piano.jpg", + "https://i.natgeofe.com/n/60004bcc-cd85-4401-8bfa-6f96551557db/cheetah-extinction-3_3x4.jpg", + "https://qph.cf2.quoracdn.net/main-qimg-0bbf31c18a22178cb7a8dd53640a3d05-lq" + ] + gr.Gallery(value=cheetahs) + +with gr.Blocks() as Chatbot_demo: + gr.Chatbot(value=[["Hello World","Hey Gradio!"],["❤️","😍"],["🔥","🤗"]]) + +with gr.Blocks() as Model3D_demo: + gr.Model3D() + +import matplotlib.pyplot as plt +import numpy as np + +Fs = 8000 +f = 5 +sample = 8000 +x = np.arange(sample) +y = np.sin(2 * np.pi * f * x / Fs) +plt.plot(x, y) + +with gr.Blocks() as Plot_demo: + gr.Plot(value=plt) + +with gr.Blocks() as Markdown_demo: + gr.Markdown(value="This _example_ was **written** in [Markdown](https://en.wikipedia.org/wiki/Markdown)\n") \ No newline at end of file diff --git a/demos/dataframe_datatype/run.py b/demos/dataframe_datatype/run.py new file mode 100644 index 0000000000000000000000000000000000000000..eb9d49bd051a4ac5d4e48fe19c3122d3c3c76d1e --- /dev/null +++ b/demos/dataframe_datatype/run.py @@ -0,0 +1,21 @@ +import gradio as gr +import pandas as pd +import numpy as np + + +def make_dataframe(n_periods): + return pd.DataFrame({"date_1": pd.date_range("2021-01-01", periods=n_periods), + "date_2": pd.date_range("2022-02-15", periods=n_periods).strftime('%B %d, %Y, %r'), + "number": np.random.random(n_periods).astype(np.float64), + "number_2": np.random.randint(0, 100, n_periods).astype(np.int32), + "bool": [True] * n_periods, + "markdown": ["# Hello"] * n_periods}) + + +demo = gr.Interface(make_dataframe, + gr.Number(precision=0), + gr.Dataframe(datatype=["date", "date", "number", "number", "bool", "markdown"])) + + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/diff_texts/run.py b/demos/diff_texts/run.py new file mode 100644 index 0000000000000000000000000000000000000000..0f1b764950060b5c944ca7b682197610cdcd916f --- /dev/null +++ b/demos/diff_texts/run.py @@ -0,0 +1,34 @@ +from difflib import Differ + +import gradio as gr + + +def diff_texts(text1, text2): + d = Differ() + return [ + (token[2:], token[0] if token[0] != " " else None) + for token in d.compare(text1, text2) + ] + + +demo = gr.Interface( + diff_texts, + [ + gr.Textbox( + label="Initial text", + lines=3, + value="The quick brown fox jumped over the lazy dogs.", + ), + gr.Textbox( + label="Text to compare", + lines=3, + value="The fast brown fox jumps over lazy dogs.", + ), + ], + gr.HighlightedText( + label="Diff", + combine_adjacent=True, + ).style(color_map={"+": "red", "-": "green"}), +) +if __name__ == "__main__": + demo.launch() diff --git a/demos/diff_texts/screenshot.png b/demos/diff_texts/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..f9174839993b1679778398c1a77fd4e7072255a2 --- /dev/null +++ b/demos/diff_texts/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a25e1d98b89d810d27011a69a7d9b15e592563693891bca4fc559fbec800f821 +size 30915 diff --git a/demos/digit_classifier/requirements.txt b/demos/digit_classifier/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..b3a60b6da25b54d3e1a3696814eed57ef4ef62b7 --- /dev/null +++ b/demos/digit_classifier/requirements.txt @@ -0,0 +1 @@ +tensorflow \ No newline at end of file diff --git a/demos/digit_classifier/run.py b/demos/digit_classifier/run.py new file mode 100644 index 0000000000000000000000000000000000000000..329dd4420f54d6d2302895de26d787c73faa9031 --- /dev/null +++ b/demos/digit_classifier/run.py @@ -0,0 +1,33 @@ +import os +from urllib.request import urlretrieve + +import tensorflow as tf + +import gradio +import gradio as gr + +urlretrieve( + "https://gr-models.s3-us-west-2.amazonaws.com/mnist-model.h5", "mnist-model.h5" +) +model = tf.keras.models.load_model("mnist-model.h5") + + +def recognize_digit(image): + image = image.reshape(1, -1) + prediction = model.predict(image).tolist()[0] + return {str(i): prediction[i] for i in range(10)} + + +im = gradio.Image(shape=(28, 28), image_mode="L", invert_colors=False, source="canvas") + +demo = gr.Interface( + recognize_digit, + im, + gradio.Label(num_top_classes=3), + live=True, + interpretation="default", + capture_session=True, +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/digit_classifier/screenshot.png b/demos/digit_classifier/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..9734783806a665d1082e5c7f860533e82989c7c8 --- /dev/null +++ b/demos/digit_classifier/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:78f82ce272eaca967b7c28330640100dec0898c5aea7519b30ebecc317ae8f5e +size 40841 diff --git a/demos/english_translator/run.py b/demos/english_translator/run.py new file mode 100644 index 0000000000000000000000000000000000000000..7b265cca0fd83df162057ae7b6f4646f5d61d303 --- /dev/null +++ b/demos/english_translator/run.py @@ -0,0 +1,25 @@ +import gradio as gr + +from transformers import pipeline + +pipe = pipeline("translation", model="t5-base") + + +def translate(text): + return pipe(text)[0]["translation_text"] + + +with gr.Blocks() as demo: + with gr.Row(): + with gr.Column(): + english = gr.Textbox(label="English text") + translate_btn = gr.Button(value="Translate") + with gr.Column(): + german = gr.Textbox(label="German Text") + + translate_btn.click(translate, inputs=english, outputs=german) + examples = gr.Examples(examples=["I went to the supermarket yesterday.", "Helen is a good swimmer."], + inputs=[english]) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/fake_gan/files/cheetah1.jpg b/demos/fake_gan/files/cheetah1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..66d3b48fd19cd8cd8d8437b6f33183b3d3d42589 --- /dev/null +++ b/demos/fake_gan/files/cheetah1.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:35550bfbba996e59c242af00f6a14a9c0d055dfbc52ad069a1a4e8c1c39ca095 +size 20552 diff --git a/demos/fake_gan/run.py b/demos/fake_gan/run.py new file mode 100644 index 0000000000000000000000000000000000000000..4287662ebf9d8292fb47da5ea4e1a8ff9172ba8a --- /dev/null +++ b/demos/fake_gan/run.py @@ -0,0 +1,53 @@ +# This demo needs to be run from the repo folder. +# python demo/fake_gan/run.py +import os +import random +import time + +import gradio as gr + + +def fake_gan(count, *args): + time.sleep(1) + images = [ + random.choice( + [ + "https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80", + "https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80", + "https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80", + "https://images.unsplash.com/photo-1546456073-92b9f0a8d413?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80", + "https://images.unsplash.com/photo-1601412436009-d964bd02edbc?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=464&q=80", + ] + ) + for _ in range(int(count)) + ] + return images + + +cheetah = os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg") + +demo = gr.Interface( + fn=fake_gan, + inputs=[ + gr.Number(label="Generation Count"), + gr.Image(label="Initial Image (optional)"), + gr.Slider(0, 50, 25, label="TV_scale (for smoothness)"), + gr.Slider(0, 50, 25, label="Range_Scale (out of range RBG)"), + gr.Number(label="Seed"), + gr.Number(label="Respacing"), + ], + outputs=gr.Gallery(label="Generated Images"), + title="FD-GAN", + description="This is a fake demo of a GAN. In reality, the images are randomly chosen from Unsplash.", + examples=[ + [2, cheetah, 12, None, None, None], + [1, cheetah, 2, None, None, None], + [4, cheetah, 42, None, None, None], + [5, cheetah, 23, None, None, None], + [4, cheetah, 11, None, None, None], + [3, cheetah, 1, None, None, None], + ], +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/fake_gan_2/files/cheetah1.jpg b/demos/fake_gan_2/files/cheetah1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..66d3b48fd19cd8cd8d8437b6f33183b3d3d42589 --- /dev/null +++ b/demos/fake_gan_2/files/cheetah1.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:35550bfbba996e59c242af00f6a14a9c0d055dfbc52ad069a1a4e8c1c39ca095 +size 20552 diff --git a/demos/fake_gan_2/files/elephant.jpg b/demos/fake_gan_2/files/elephant.jpg new file mode 100644 index 0000000000000000000000000000000000000000..9e73dfa569601a400272ac06398ebd6280cce2a3 --- /dev/null +++ b/demos/fake_gan_2/files/elephant.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:08a0bc9fb256c98bd98dbb31e54634724196e244f80a37370e22051963884bb3 +size 65355 diff --git a/demos/fake_gan_2/files/tiger.jpg b/demos/fake_gan_2/files/tiger.jpg new file mode 100644 index 0000000000000000000000000000000000000000..1d078ef300abc9df89fb2b2be55a0e038710f379 --- /dev/null +++ b/demos/fake_gan_2/files/tiger.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c92d7a493b2d7da61129112b4ff815f2378428461606c21fc15639dfebd797ff +size 57482 diff --git a/demos/fake_gan_2/files/zebra.jpg b/demos/fake_gan_2/files/zebra.jpg new file mode 100644 index 0000000000000000000000000000000000000000..90ef83d0c40d6bcfc9203962bfe721be44294925 --- /dev/null +++ b/demos/fake_gan_2/files/zebra.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c7114c450c21cc398b74887980d9e98b466564f850765c7970402162274cc80e +size 52203 diff --git a/demos/fake_gan_2/run.py b/demos/fake_gan_2/run.py new file mode 100644 index 0000000000000000000000000000000000000000..43df9a2b157463eb2e53a3bada0d54503071d261 --- /dev/null +++ b/demos/fake_gan_2/run.py @@ -0,0 +1,41 @@ +# This demo needs to be run from the repo folder. +# python demo/fake_gan/run.py +import os +import random +import time + +import gradio as gr + + +def fake_gan(*args): + time.sleep(1) + image = random.choice( + [ + "https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80", + "https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80", + "https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80", + "https://images.unsplash.com/photo-1546456073-92b9f0a8d413?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80", + "https://images.unsplash.com/photo-1601412436009-d964bd02edbc?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=464&q=80", + ] + ) + return image + + +demo = gr.Interface( + fn=fake_gan, + inputs=[ + gr.Image(label="Initial Image (optional)"), + ], + outputs=gr.Image(label="Generated Image"), + title="FD-GAN", + description="This is a fake demo of a GAN. In reality, the images are randomly chosen from Unsplash.", + examples=[ + [os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg")], + [os.path.join(os.path.dirname(__file__), "files/elephant.jpg")], + [os.path.join(os.path.dirname(__file__), "files/tiger.jpg")], + [os.path.join(os.path.dirname(__file__), "files/zebra.jpg")], + ], +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/fake_gan_no_input/run.py b/demos/fake_gan_no_input/run.py new file mode 100644 index 0000000000000000000000000000000000000000..9b7f5152937b5694917e8fa25a2c7b7e83d6080e --- /dev/null +++ b/demos/fake_gan_no_input/run.py @@ -0,0 +1,32 @@ +# This demo needs to be run from the repo folder. +# python demo/fake_gan/run.py +import random +import time + +import gradio as gr + + +def fake_gan(): + time.sleep(1) + image = random.choice( + [ + "https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80", + "https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80", + "https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80", + "https://images.unsplash.com/photo-1546456073-92b9f0a8d413?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80", + "https://images.unsplash.com/photo-1601412436009-d964bd02edbc?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=464&q=80", + ] + ) + return image + + +demo = gr.Interface( + fn=fake_gan, + inputs=None, + outputs=gr.Image(label="Generated Image"), + title="FD-GAN", + description="This is a fake demo of a GAN. In reality, the images are randomly chosen from Unsplash.", +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/filter_records/run.py b/demos/filter_records/run.py new file mode 100644 index 0000000000000000000000000000000000000000..92e54b1136f9e0af85e67fd7f054fe4aaec0651b --- /dev/null +++ b/demos/filter_records/run.py @@ -0,0 +1,24 @@ +import gradio as gr + + +def filter_records(records, gender): + return records[records["gender"] == gender] + + +demo = gr.Interface( + filter_records, + [ + gr.Dataframe( + headers=["name", "age", "gender"], + datatype=["str", "number", "str"], + row_count=5, + col_count=(3, "fixed") + ), + gr.Dropdown(["M", "F", "O"]), + ], + "dataframe", + description="Enter gender as 'M', 'F', or 'O' for other.", +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/filter_records/screenshot.png b/demos/filter_records/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..232735d8abbcb175991547291ac5ed637fa1bb3f --- /dev/null +++ b/demos/filter_records/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:02074331bd070dc0d4aa8012ffbde7d3a65f41244613d009b86406b5072b32f3 +size 31111 diff --git a/demos/fraud_detector/fraud.csv b/demos/fraud_detector/fraud.csv new file mode 100644 index 0000000000000000000000000000000000000000..8fe13def6c7e0fb86714908a04e8b1df2e0910d2 --- /dev/null +++ b/demos/fraud_detector/fraud.csv @@ -0,0 +1,11 @@ +time,retail,food,other +0,109,145,86 +1,35,87,43 +2,49,117,34 +3,127,66,17 +4,39,82,17 +5,101,56,79 +6,100,129,67 +7,17,88,97 +8,76,85,145 +9,111,106,35 diff --git a/demos/fraud_detector/requirements.txt b/demos/fraud_detector/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..1411a4a0b5ab886adfb744e685d150151ab10023 --- /dev/null +++ b/demos/fraud_detector/requirements.txt @@ -0,0 +1 @@ +pandas \ No newline at end of file diff --git a/demos/fraud_detector/run.py b/demos/fraud_detector/run.py new file mode 100644 index 0000000000000000000000000000000000000000..d5384b8ed05e8c2f2db4d0aaaf681e575afec4b0 --- /dev/null +++ b/demos/fraud_detector/run.py @@ -0,0 +1,39 @@ +import random +import os +import gradio as gr + + +def fraud_detector(card_activity, categories, sensitivity): + activity_range = random.randint(0, 100) + drop_columns = [ + column for column in ["retail", "food", "other"] if column not in categories + ] + if len(drop_columns): + card_activity.drop(columns=drop_columns, inplace=True) + return ( + card_activity, + card_activity, + {"fraud": activity_range / 100.0, "not fraud": 1 - activity_range / 100.0}, + ) + + +demo = gr.Interface( + fraud_detector, + [ + gr.Timeseries(x="time", y=["retail", "food", "other"]), + gr.CheckboxGroup( + ["retail", "food", "other"], value=["retail", "food", "other"] + ), + gr.Slider(1, 3), + ], + [ + "dataframe", + gr.Timeseries(x="time", y=["retail", "food", "other"]), + gr.Label(label="Fraud Level"), + ], + examples=[ + [os.path.join(os.path.dirname(__file__), "fraud.csv"), ["retail", "food", "other"], 1.0], + ], +) +if __name__ == "__main__": + demo.launch() diff --git a/demos/fraud_detector/screenshot.png b/demos/fraud_detector/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..fd028254b758a6cdcbc9a779bfd6bcc6b4452090 --- /dev/null +++ b/demos/fraud_detector/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:65e355d826dea756a380226c41d8b5839fb83aa8c07a5f98bceb0929e3d8742b +size 93323 diff --git a/demos/gender_sentence_custom_interpretation/run.py b/demos/gender_sentence_custom_interpretation/run.py new file mode 100644 index 0000000000000000000000000000000000000000..93a8f6c6cf485f2b22b6a15ef363b4f4fece03d6 --- /dev/null +++ b/demos/gender_sentence_custom_interpretation/run.py @@ -0,0 +1,46 @@ +import re + +import gradio as gr + +male_words, female_words = ["he", "his", "him"], ["she", "hers", "her"] + + +def gender_of_sentence(sentence): + male_count = len([word for word in sentence.split() if word.lower() in male_words]) + female_count = len( + [word for word in sentence.split() if word.lower() in female_words] + ) + total = max(male_count + female_count, 1) + return {"male": male_count / total, "female": female_count / total} + + +# Number of arguments to interpretation function must +# match number of inputs to prediction function +def interpret_gender(sentence): + result = gender_of_sentence(sentence) + is_male = result["male"] > result["female"] + interpretation = [] + for word in re.split("( )", sentence): + score = 0 + token = word.lower() + if (is_male and token in male_words) or (not is_male and token in female_words): + score = 1 + elif (is_male and token in female_words) or ( + not is_male and token in male_words + ): + score = -1 + interpretation.append((word, score)) + # Output must be a list of lists containing the same number of elements as inputs + # Each element corresponds to the interpretation scores for the given input + return [interpretation] + + +demo = gr.Interface( + fn=gender_of_sentence, + inputs=gr.Textbox(value="She went to his house to get her keys."), + outputs="label", + interpretation=interpret_gender, +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/gender_sentence_custom_interpretation/screenshot.gif b/demos/gender_sentence_custom_interpretation/screenshot.gif new file mode 100644 index 0000000000000000000000000000000000000000..a480e9951427b076384a8819a402a9cbff8caaf1 --- /dev/null +++ b/demos/gender_sentence_custom_interpretation/screenshot.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4e3b9a98e6612b9beb70a0ee8269e5def076c8df7c14d6e8a4afcd6f11a44250 +size 69585 diff --git a/demos/gender_sentence_default_interpretation/run.py b/demos/gender_sentence_default_interpretation/run.py new file mode 100644 index 0000000000000000000000000000000000000000..99312fda6c52f5ff754589cf99a8b7ef031d3f70 --- /dev/null +++ b/demos/gender_sentence_default_interpretation/run.py @@ -0,0 +1,23 @@ +import gradio as gr + +male_words, female_words = ["he", "his", "him"], ["she", "hers", "her"] + + +def gender_of_sentence(sentence): + male_count = len([word for word in sentence.split() if word.lower() in male_words]) + female_count = len( + [word for word in sentence.split() if word.lower() in female_words] + ) + total = max(male_count + female_count, 1) + return {"male": male_count / total, "female": female_count / total} + + +demo = gr.Interface( + fn=gender_of_sentence, + inputs=gr.Textbox(value="She went to his house to get her keys."), + outputs="label", + interpretation="default", +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/gender_sentence_default_interpretation/screenshot.gif b/demos/gender_sentence_default_interpretation/screenshot.gif new file mode 100644 index 0000000000000000000000000000000000000000..012e361179bb162592234e19406fc9e0366eab00 --- /dev/null +++ b/demos/gender_sentence_default_interpretation/screenshot.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:64b533d303e5c7f66394c637209f7d9208625ed7a816a02c88ef95f5dc0291f1 +size 77050 diff --git a/demos/generate_english_german/run.py b/demos/generate_english_german/run.py new file mode 100644 index 0000000000000000000000000000000000000000..4ae58afb88f5ad9998321ef9d0239a7c0f56e773 --- /dev/null +++ b/demos/generate_english_german/run.py @@ -0,0 +1,27 @@ +import gradio as gr + +from transformers import pipeline + +english_translator = gr.Blocks.load(name="spaces/freddyaboulton/english-translator") +english_generator = pipeline("text-generation", model="distilgpt2") + + +def generate_text(text): + english_text = english_generator(text)[0]["generated_text"] + german_text = english_translator(english_text) + return english_text, german_text + + +with gr.Blocks() as demo: + with gr.Row(): + with gr.Column(): + seed = gr.Text(label="Input Phrase") + with gr.Column(): + english = gr.Text(label="Generated English Text") + german = gr.Text(label="Generated German Text") + btn = gr.Button("Generate") + btn.click(generate_text, inputs=[seed], outputs=[english, german]) + gr.Examples(["My name is Clara and I am"], inputs=[seed]) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/generate_tone/requirements.txt b/demos/generate_tone/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..296d654528b719e554528b956c4bf5a1516e812c --- /dev/null +++ b/demos/generate_tone/requirements.txt @@ -0,0 +1 @@ +numpy \ No newline at end of file diff --git a/demos/generate_tone/run.py b/demos/generate_tone/run.py new file mode 100644 index 0000000000000000000000000000000000000000..47cc41399f2ebc322b50258f867897e036b949bf --- /dev/null +++ b/demos/generate_tone/run.py @@ -0,0 +1,25 @@ +import numpy as np +import gradio as gr + +notes = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"] + +def generate_tone(note, octave, duration): + sr = 48000 + a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9) + frequency = a4_freq * 2 ** (tones_from_a4 / 12) + duration = int(duration) + audio = np.linspace(0, duration, duration * sr) + audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16) + return sr, audio + +demo = gr.Interface( + generate_tone, + [ + gr.Dropdown(notes, type="index"), + gr.Slider(4, 6, step=1), + gr.Textbox(value=1, type="number", label="Duration in seconds"), + ], + "audio", +) +if __name__ == "__main__": + demo.launch() diff --git a/demos/generate_tone/screenshot.png b/demos/generate_tone/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..f141d3dc944e9c93d767f5ed92f1e29de1770489 --- /dev/null +++ b/demos/generate_tone/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:302cbf9c43fa6c58e93c2b0542180516d47af5ecaa2b5224e49ac21c6989c948 +size 23700 diff --git a/demos/gif_maker/requirements.txt b/demos/gif_maker/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..1db7aea116e2b2026e2b660df58af81d997599e6 --- /dev/null +++ b/demos/gif_maker/requirements.txt @@ -0,0 +1 @@ +opencv-python \ No newline at end of file diff --git a/demos/gif_maker/run.py b/demos/gif_maker/run.py new file mode 100644 index 0000000000000000000000000000000000000000..eed8120d71a2526ad8cff6a7b9b244d3a8b91623 --- /dev/null +++ b/demos/gif_maker/run.py @@ -0,0 +1,23 @@ +import cv2 +import gradio as gr +import tempfile + +def gif_maker(img_files): + img_array = [] + import os + for filename in img_files: + img = cv2.imread(filename.name) + height, width, _ = img.shape + size = (width,height) + img_array.append(img) + output_file = "test.mp4" + out = cv2.VideoWriter(output_file,cv2.VideoWriter_fourcc(*'h264'), 15, size) + for i in range(len(img_array)): + out.write(img_array[i]) + out.release() + return output_file + +demo = gr.Interface(gif_maker, inputs=gr.File(file_count="multiple"), outputs=gr.Video()) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/gpt_j/run.py b/demos/gpt_j/run.py new file mode 100644 index 0000000000000000000000000000000000000000..18dbd4b107f748f30e4c6a7f99e37042837af10c --- /dev/null +++ b/demos/gpt_j/run.py @@ -0,0 +1,19 @@ +import gradio as gr + +title = "GPT-J-6B" + +examples = [ + ["The tower is 324 metres (1,063 ft) tall,"], + ["The Moon's orbit around Earth has"], + ["The smooth Borealis basin in the Northern Hemisphere covers 40%"], +] + +demo = gr.Interface.load( + "huggingface/EleutherAI/gpt-j-6B", + inputs=gr.Textbox(lines=5, max_lines=6, label="Input Text"), + title=title, + examples=examples, +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/gpt_j_unified/run.py b/demos/gpt_j_unified/run.py new file mode 100644 index 0000000000000000000000000000000000000000..b561f89509172e151b9888cad3c937cf842e501f --- /dev/null +++ b/demos/gpt_j_unified/run.py @@ -0,0 +1,14 @@ +import gradio as gr + +component = gr.Textbox(lines=5, label="Text") +api = gr.Interface.load("huggingface/EleutherAI/gpt-j-6B") + +demo = gr.Interface( + fn=lambda x: x[:-50] + api(x[-50:]), + inputs=component, + outputs=component, + title="GPT-J-6B", +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/hangman/run.py b/demos/hangman/run.py new file mode 100644 index 0000000000000000000000000000000000000000..ddd638f60bc671d28ed773ec98bf12fe15e33f11 --- /dev/null +++ b/demos/hangman/run.py @@ -0,0 +1,36 @@ +import gradio as gr +import random + +secret_word = "gradio" + +with gr.Blocks() as demo: + used_letters_var = gr.Variable([]) + with gr.Row() as row: + with gr.Column(): + input_letter = gr.Textbox(label="Enter letter") + btn = gr.Button("Guess Letter") + with gr.Column(): + hangman = gr.Textbox( + label="Hangman", + value="_"*len(secret_word) + ) + used_letters_box = gr.Textbox(label="Used Letters") + + def guess_letter(letter, used_letters): + used_letters.append(letter) + answer = "".join([ + (letter if letter in used_letters else "_") + for letter in secret_word + ]) + return { + used_letters_var: used_letters, + used_letters_box: ", ".join(used_letters), + hangman: answer + } + btn.click( + guess_letter, + [input_letter, used_letters_var], + [used_letters_var, used_letters_box, hangman] + ) +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/hello_blocks/run.py b/demos/hello_blocks/run.py new file mode 100644 index 0000000000000000000000000000000000000000..23b96a760b6309835495bf548fb8530807516551 --- /dev/null +++ b/demos/hello_blocks/run.py @@ -0,0 +1,13 @@ +import gradio as gr + +def greet(name): + return "Hello " + name + "!" + +with gr.Blocks() as demo: + name = gr.Textbox(label="Name") + output = gr.Textbox(label="Output Box") + greet_btn = gr.Button("Greet") + greet_btn.click(fn=greet, inputs=name, outputs=output) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/hello_blocks/screenshot.gif b/demos/hello_blocks/screenshot.gif new file mode 100644 index 0000000000000000000000000000000000000000..04c9c7c033e28f161a0eb832e7f91e36cd6b9cf4 --- /dev/null +++ b/demos/hello_blocks/screenshot.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2ddf5136775297f274caf79bbd2beb8cf172a5d8941597cc82edb43588c54c65 +size 92391 diff --git a/demos/hello_login/run.py b/demos/hello_login/run.py new file mode 100644 index 0000000000000000000000000000000000000000..2483f95e316003acddb71c334ba57d5bb9c129bd --- /dev/null +++ b/demos/hello_login/run.py @@ -0,0 +1,14 @@ +import gradio as gr + +user_db = {"admin": "admin", "foo": "bar"} + + +def greet(name): + return "Hello " + name + "!!" + + +demo = gr.Interface(fn=greet, inputs="text", outputs="text") +if __name__ == "__main__": + demo.launch( + auth=lambda u, p: user_db.get(u) == p, + auth_message="This is a welcome message") diff --git a/demos/hello_login/screenshot.png b/demos/hello_login/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..9e3e678d3da6468bb343ff731e4aac291365abd9 --- /dev/null +++ b/demos/hello_login/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ab85deb9bec700c9169cd0f1dacea2a376164274152d0ae8f166afb2fac357c7 +size 16337 diff --git a/demos/hello_world/run.py b/demos/hello_world/run.py new file mode 100644 index 0000000000000000000000000000000000000000..1e8f4e5343ae99ae9f833158f31ff691b7869375 --- /dev/null +++ b/demos/hello_world/run.py @@ -0,0 +1,8 @@ +import gradio as gr + +def greet(name): + return "Hello " + name + "!" + +demo = gr.Interface(fn=greet, inputs="text", outputs="text") +if __name__ == "__main__": + demo.launch() diff --git a/demos/hello_world/screenshot.gif b/demos/hello_world/screenshot.gif new file mode 100644 index 0000000000000000000000000000000000000000..8a7a275722a845c73159593638fce4e913f84fc6 --- /dev/null +++ b/demos/hello_world/screenshot.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:78978f9fb71daf24685e7f2aba0060b2839613d22a4a06d0b95143316883deb6 +size 897877 diff --git a/demos/hello_world/screenshot.png b/demos/hello_world/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..e1faa87bd650ec8ac3cedb3ef0cbd481e647b5db --- /dev/null +++ b/demos/hello_world/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2a70b1f6047d48d2ecd0ed69b4db77884fc467e396cf03bd9789b59658abed70 +size 11527 diff --git a/demos/hello_world_2/run.py b/demos/hello_world_2/run.py new file mode 100644 index 0000000000000000000000000000000000000000..ac8493a5030415721c9e7182e5cdea8074eb17f7 --- /dev/null +++ b/demos/hello_world_2/run.py @@ -0,0 +1,12 @@ +import gradio as gr + +def greet(name): + return "Hello " + name + "!" + +demo = gr.Interface( + fn=greet, + inputs=gr.Textbox(lines=2, placeholder="Name Here..."), + outputs="text", +) +if __name__ == "__main__": + demo.launch() diff --git a/demos/hello_world_2/screenshot.gif b/demos/hello_world_2/screenshot.gif new file mode 100644 index 0000000000000000000000000000000000000000..d99ef184d9cf1ab18e86d6356a900f031d5c83f6 --- /dev/null +++ b/demos/hello_world_2/screenshot.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:97269daef7203ef7727cba3e0f5a90afa060219692ba535c06818fdc8f11fe06 +size 1121749 diff --git a/demos/hello_world_3/run.py b/demos/hello_world_3/run.py new file mode 100644 index 0000000000000000000000000000000000000000..fbf93b959140a14f39b818ec61e3db5eacc9739d --- /dev/null +++ b/demos/hello_world_3/run.py @@ -0,0 +1,15 @@ +import gradio as gr + +def greet(name, is_morning, temperature): + salutation = "Good morning" if is_morning else "Good evening" + greeting = f"{salutation} {name}. It is {temperature} degrees today" + celsius = (temperature - 32) * 5 / 9 + return greeting, round(celsius, 2) + +demo = gr.Interface( + fn=greet, + inputs=["text", "checkbox", gr.Slider(0, 100)], + outputs=["text", "number"], +) +if __name__ == "__main__": + demo.launch() diff --git a/demos/hello_world_3/screenshot.gif b/demos/hello_world_3/screenshot.gif new file mode 100644 index 0000000000000000000000000000000000000000..41c2292e362619aaaac375820a04f11837c0eaec --- /dev/null +++ b/demos/hello_world_3/screenshot.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0481bb668cc9269f7c680b971dd284f34b7aa822fa1ca530c520b1438e809bc8 +size 506714 diff --git a/demos/image_classifier/files/imagenet_labels.json b/demos/image_classifier/files/imagenet_labels.json new file mode 100644 index 0000000000000000000000000000000000000000..fa059ceeab5d18ab9882df2983b92e8270373e39 --- /dev/null +++ b/demos/image_classifier/files/imagenet_labels.json @@ -0,0 +1,1000 @@ +["tench", + "goldfish", + "great white shark", + "tiger shark", + "hammerhead shark", + "electric ray", + "stingray", + "cock", + "hen", + "ostrich", + "brambling", + "goldfinch", + "house finch", + "junco", + "indigo bunting", + "American robin", + "bulbul", + "jay", + "magpie", + "chickadee", + "American dipper", + "kite", + "bald eagle", + "vulture", + "great grey owl", + "fire salamander", + "smooth newt", + "newt", + "spotted salamander", + "axolotl", + "American bullfrog", + "tree frog", + "tailed frog", + "loggerhead sea turtle", + "leatherback sea turtle", + "mud turtle", + "terrapin", + "box turtle", + "banded gecko", + "green iguana", + "Carolina anole", + "desert grassland whiptail lizard", + "agama", + "frilled-necked lizard", + "alligator lizard", + "Gila monster", + "European green lizard", + "chameleon", + "Komodo dragon", + "Nile crocodile", + "American alligator", + "triceratops", + "worm snake", + "ring-necked snake", + "eastern hog-nosed snake", + "smooth green snake", + "kingsnake", + "garter snake", + "water snake", + "vine snake", + "night snake", + "boa constrictor", + "African rock python", + "Indian cobra", + "green mamba", + "sea snake", + "Saharan horned viper", + "eastern diamondback rattlesnake", + "sidewinder", + "trilobite", + "harvestman", + "scorpion", + "yellow garden spider", + "barn spider", + "European garden spider", + "southern black widow", + "tarantula", + "wolf spider", + "tick", + "centipede", + "black grouse", + "ptarmigan", + "ruffed grouse", + "prairie grouse", + "peacock", + "quail", + "partridge", + "grey parrot", + "macaw", + "sulphur-crested cockatoo", + "lorikeet", + "coucal", + "bee eater", + "hornbill", + "hummingbird", + "jacamar", + "toucan", + "duck", + "red-breasted merganser", + "goose", + "black swan", + "tusker", + "echidna", + "platypus", + "wallaby", + "koala", + "wombat", + "jellyfish", + "sea anemone", + "brain coral", + "flatworm", + "nematode", + "conch", + "snail", + "slug", + "sea slug", + "chiton", + "chambered nautilus", + "Dungeness crab", + "rock crab", + "fiddler crab", + "red king crab", + "American lobster", + "spiny lobster", + "crayfish", + "hermit crab", + "isopod", + "white stork", + "black stork", + "spoonbill", + "flamingo", + "little blue heron", + "great egret", + "bittern", + "crane (bird)", + "limpkin", + "common gallinule", + "American coot", + "bustard", + "ruddy turnstone", + "dunlin", + "common redshank", + "dowitcher", + "oystercatcher", + "pelican", + "king penguin", + "albatross", + "grey whale", + "killer whale", + "dugong", + "sea lion", + "Chihuahua", + "Japanese Chin", + "Maltese", + "Pekingese", + "Shih Tzu", + "King Charles Spaniel", + "Papillon", + "toy terrier", + "Rhodesian Ridgeback", + "Afghan Hound", + "Basset Hound", + "Beagle", + "Bloodhound", + "Bluetick Coonhound", + "Black and Tan Coonhound", + "Treeing Walker Coonhound", + "English foxhound", + "Redbone Coonhound", + "borzoi", + "Irish Wolfhound", + "Italian Greyhound", + "Whippet", + "Ibizan Hound", + "Norwegian Elkhound", + "Otterhound", + "Saluki", + "Scottish Deerhound", + "Weimaraner", + "Staffordshire Bull Terrier", + "American Staffordshire Terrier", + "Bedlington Terrier", + "Border Terrier", + "Kerry Blue Terrier", + "Irish Terrier", + "Norfolk Terrier", + "Norwich Terrier", + "Yorkshire Terrier", + "Wire Fox Terrier", + "Lakeland Terrier", + "Sealyham Terrier", + "Airedale Terrier", + "Cairn Terrier", + "Australian Terrier", + "Dandie Dinmont Terrier", + "Boston Terrier", + "Miniature Schnauzer", + "Giant Schnauzer", + "Standard Schnauzer", + "Scottish Terrier", + "Tibetan Terrier", + "Australian Silky Terrier", + "Soft-coated Wheaten Terrier", + "West Highland White Terrier", + "Lhasa Apso", + "Flat-Coated Retriever", + "Curly-coated Retriever", + "Golden Retriever", + "Labrador Retriever", + "Chesapeake Bay Retriever", + "German Shorthaired Pointer", + "Vizsla", + "English Setter", + "Irish Setter", + "Gordon Setter", + "Brittany", + "Clumber Spaniel", + "English Springer Spaniel", + "Welsh Springer Spaniel", + "Cocker Spaniels", + "Sussex Spaniel", + "Irish Water Spaniel", + "Kuvasz", + "Schipperke", + "Groenendael", + "Malinois", + "Briard", + "Australian Kelpie", + "Komondor", + "Old English Sheepdog", + "Shetland Sheepdog", + "collie", + "Border Collie", + "Bouvier des Flandres", + "Rottweiler", + "German Shepherd Dog", + "Dobermann", + "Miniature Pinscher", + "Greater Swiss Mountain Dog", + "Bernese Mountain Dog", + "Appenzeller Sennenhund", + "Entlebucher Sennenhund", + "Boxer", + "Bullmastiff", + "Tibetan Mastiff", + "French Bulldog", + "Great Dane", + "St. Bernard", + "husky", + "Alaskan Malamute", + "Siberian Husky", + "Dalmatian", + "Affenpinscher", + "Basenji", + "pug", + "Leonberger", + "Newfoundland", + "Pyrenean Mountain Dog", + "Samoyed", + "Pomeranian", + "Chow Chow", + "Keeshond", + "Griffon Bruxellois", + "Pembroke Welsh Corgi", + "Cardigan Welsh Corgi", + "Toy Poodle", + "Miniature Poodle", + "Standard Poodle", + "Mexican hairless dog", + "grey wolf", + "Alaskan tundra wolf", + "red wolf", + "coyote", + "dingo", + "dhole", + "African wild dog", + "hyena", + "red fox", + "kit fox", + "Arctic fox", + "grey fox", + "tabby cat", + "tiger cat", + "Persian cat", + "Siamese cat", + "Egyptian Mau", + "cougar", + "lynx", + "leopard", + "snow leopard", + "jaguar", + "lion", + "tiger", + "cheetah", + "brown bear", + "American black bear", + "polar bear", + "sloth bear", + "mongoose", + "meerkat", + "tiger beetle", + "ladybug", + "ground beetle", + "longhorn beetle", + "leaf beetle", + "dung beetle", + "rhinoceros beetle", + "weevil", + "fly", + "bee", + "ant", + "grasshopper", + "cricket", + "stick insect", + "cockroach", + "mantis", + "cicada", + "leafhopper", + "lacewing", + "dragonfly", + "damselfly", + "red admiral", + "ringlet", + "monarch butterfly", + "small white", + "sulphur butterfly", + "gossamer-winged butterfly", + "starfish", + "sea urchin", + "sea cucumber", + "cottontail rabbit", + "hare", + "Angora rabbit", + "hamster", + "porcupine", + "fox squirrel", + "marmot", + "beaver", + "guinea pig", + "common sorrel", + "zebra", + "pig", + "wild boar", + "warthog", + "hippopotamus", + "ox", + "water buffalo", + "bison", + "ram", + "bighorn sheep", + "Alpine ibex", + "hartebeest", + "impala", + "gazelle", + "dromedary", + "llama", + "weasel", + "mink", + "European polecat", + "black-footed ferret", + "otter", + "skunk", + "badger", + "armadillo", + "three-toed sloth", + "orangutan", + "gorilla", + "chimpanzee", + "gibbon", + "siamang", + "guenon", + "patas monkey", + "baboon", + "macaque", + "langur", + "black-and-white colobus", + "proboscis monkey", + "marmoset", + "white-headed capuchin", + "howler monkey", + "titi", + "Geoffroy's spider monkey", + "common squirrel monkey", + "ring-tailed lemur", + "indri", + "Asian elephant", + "African bush elephant", + "red panda", + "giant panda", + "snoek", + "eel", + "coho salmon", + "rock beauty", + "clownfish", + "sturgeon", + "garfish", + "lionfish", + "pufferfish", + "abacus", + "abaya", + "academic gown", + "accordion", + "acoustic guitar", + "aircraft carrier", + "airliner", + "airship", + "altar", + "ambulance", + "amphibious vehicle", + "analog clock", + "apiary", + "apron", + "waste container", + "assault rifle", + "backpack", + "bakery", + "balance beam", + "balloon", + "ballpoint pen", + "Band-Aid", + "banjo", + "baluster", + "barbell", + "barber chair", + "barbershop", + "barn", + "barometer", + "barrel", + "wheelbarrow", + "baseball", + "basketball", + "bassinet", + "bassoon", + "swimming cap", + "bath towel", + "bathtub", + "station wagon", + "lighthouse", + "beaker", + "military cap", + "beer bottle", + "beer glass", + "bell-cot", + "bib", + "tandem bicycle", + "bikini", + "ring binder", + "binoculars", + "birdhouse", + "boathouse", + "bobsleigh", + "bolo tie", + "poke bonnet", + "bookcase", + "bookstore", + "bottle cap", + "bow", + "bow tie", + "brass", + "bra", + "breakwater", + "breastplate", + "broom", + "bucket", + "buckle", + "bulletproof vest", + "high-speed train", + "butcher shop", + "taxicab", + "cauldron", + "candle", + "cannon", + "canoe", + "can opener", + "cardigan", + "car mirror", + "carousel", + "tool kit", + "carton", + "car wheel", + "automated teller machine", + "cassette", + "cassette player", + "castle", + "catamaran", + "CD player", + "cello", + "mobile phone", + "chain", + "chain-link fence", + "chain mail", + "chainsaw", + "chest", + "chiffonier", + "chime", + "china cabinet", + "Christmas stocking", + "church", + "movie theater", + "cleaver", + "cliff dwelling", + "cloak", + "clogs", + "cocktail shaker", + "coffee mug", + "coffeemaker", + "coil", + "combination lock", + "computer keyboard", + "confectionery store", + "container ship", + "convertible", + "corkscrew", + "cornet", + "cowboy boot", + "cowboy hat", + "cradle", + "crane (machine)", + "crash helmet", + "crate", + "infant bed", + "Crock Pot", + "croquet ball", + "crutch", + "cuirass", + "dam", + "desk", + "desktop computer", + "rotary dial telephone", + "diaper", + "digital clock", + "digital watch", + "dining table", + "dishcloth", + "dishwasher", + "disc brake", + "dock", + "dog sled", + "dome", + "doormat", + "drilling rig", + "drum", + "drumstick", + "dumbbell", + "Dutch oven", + "electric fan", + "electric guitar", + "electric locomotive", + "entertainment center", + "envelope", + "espresso machine", + "face powder", + "feather boa", + "filing cabinet", + "fireboat", + "fire engine", + "fire screen sheet", + "flagpole", + "flute", + "folding chair", + "football helmet", + "forklift", + "fountain", + "fountain pen", + "four-poster bed", + "freight car", + "French horn", + "frying pan", + "fur coat", + "garbage truck", + "gas mask", + "gas pump", + "goblet", + "go-kart", + "golf ball", + "golf cart", + "gondola", + "gong", + "gown", + "grand piano", + "greenhouse", + "grille", + "grocery store", + "guillotine", + "barrette", + "hair spray", + "half-track", + "hammer", + "hamper", + "hair dryer", + "hand-held computer", + "handkerchief", + "hard disk drive", + "harmonica", + "harp", + "harvester", + "hatchet", + "holster", + "home theater", + "honeycomb", + "hook", + "hoop skirt", + "horizontal bar", + "horse-drawn vehicle", + "hourglass", + "iPod", + "clothes iron", + "jack-o'-lantern", + "jeans", + "jeep", + "T-shirt", + "jigsaw puzzle", + "pulled rickshaw", + "joystick", + "kimono", + "knee pad", + "knot", + "lab coat", + "ladle", + "lampshade", + "laptop computer", + "lawn mower", + "lens cap", + "paper knife", + "library", + "lifeboat", + "lighter", + "limousine", + "ocean liner", + "lipstick", + "slip-on shoe", + "lotion", + "speaker", + "loupe", + "sawmill", + "magnetic compass", + "mail bag", + "mailbox", + "tights", + "tank suit", + "manhole cover", + "maraca", + "marimba", + "mask", + "match", + "maypole", + "maze", + "measuring cup", + "medicine chest", + "megalith", + "microphone", + "microwave oven", + "military uniform", + "milk can", + "minibus", + "miniskirt", + "minivan", + "missile", + "mitten", + "mixing bowl", + "mobile home", + "Model T", + "modem", + "monastery", + "monitor", + "moped", + "mortar", + "square academic cap", + "mosque", + "mosquito net", + "scooter", + "mountain bike", + "tent", + "computer mouse", + "mousetrap", + "moving van", + "muzzle", + "nail", + "neck brace", + "necklace", + "nipple", + "notebook computer", + "obelisk", + "oboe", + "ocarina", + "odometer", + "oil filter", + "organ", + "oscilloscope", + "overskirt", + "bullock cart", + "oxygen mask", + "packet", + "paddle", + "paddle wheel", + "padlock", + "paintbrush", + "pajamas", + "palace", + "pan flute", + "paper towel", + "parachute", + "parallel bars", + "park bench", + "parking meter", + "passenger car", + "patio", + "payphone", + "pedestal", + "pencil case", + "pencil sharpener", + "perfume", + "Petri dish", + "photocopier", + "plectrum", + "Pickelhaube", + "picket fence", + "pickup truck", + "pier", + "piggy bank", + "pill bottle", + "pillow", + "ping-pong ball", + "pinwheel", + "pirate ship", + "pitcher", + "hand plane", + "planetarium", + "plastic bag", + "plate rack", + "plow", + "plunger", + "Polaroid camera", + "pole", + "police van", + "poncho", + "billiard table", + "soda bottle", + "pot", + "potter's wheel", + "power drill", + "prayer rug", + "printer", + "prison", + "projectile", + "projector", + "hockey puck", + "punching bag", + "purse", + "quill", + "quilt", + "race car", + "racket", + "radiator", + "radio", + "radio telescope", + "rain barrel", + "recreational vehicle", + "reel", + "reflex camera", + "refrigerator", + "remote control", + "restaurant", + "revolver", + "rifle", + "rocking chair", + "rotisserie", + "eraser", + "rugby ball", + "ruler", + "running shoe", + "safe", + "safety pin", + "salt shaker", + "sandal", + "sarong", + "saxophone", + "scabbard", + "weighing scale", + "school bus", + "schooner", + "scoreboard", + "CRT screen", + "screw", + "screwdriver", + "seat belt", + "sewing machine", + "shield", + "shoe store", + "shoji", + "shopping basket", + "shopping cart", + "shovel", + "shower cap", + "shower curtain", + "ski", + "ski mask", + "sleeping bag", + "slide rule", + "sliding door", + "slot machine", + "snorkel", + "snowmobile", + "snowplow", + "soap dispenser", + "soccer ball", + "sock", + "solar thermal collector", + "sombrero", + "soup bowl", + "space bar", + "space heater", + "space shuttle", + "spatula", + "motorboat", + "spider web", + "spindle", + "sports car", + "spotlight", + "stage", + "steam locomotive", + "through arch bridge", + "steel drum", + "stethoscope", + "scarf", + "stone wall", + "stopwatch", + "stove", + "strainer", + "tram", + "stretcher", + "couch", + "stupa", + "submarine", + "suit", + "sundial", + "sunglass", + "sunglasses", + "sunscreen", + "suspension bridge", + "mop", + "sweatshirt", + "swimsuit", + "swing", + "switch", + "syringe", + "table lamp", + "tank", + "tape player", + "teapot", + "teddy bear", + "television", + "tennis ball", + "thatched roof", + "front curtain", + "thimble", + "threshing machine", + "throne", + "tile roof", + "toaster", + "tobacco shop", + "toilet seat", + "torch", + "totem pole", + "tow truck", + "toy store", + "tractor", + "semi-trailer truck", + "tray", + "trench coat", + "tricycle", + "trimaran", + "tripod", + "triumphal arch", + "trolleybus", + "trombone", + "tub", + "turnstile", + "typewriter keyboard", + "umbrella", + "unicycle", + "upright piano", + "vacuum cleaner", + "vase", + "vault", + "velvet", + "vending machine", + "vestment", + "viaduct", + "violin", + "volleyball", + "waffle iron", + "wall clock", + "wallet", + "wardrobe", + "military aircraft", + "sink", + "washing machine", + "water bottle", + "water jug", + "water tower", + "whiskey jug", + "whistle", + "wig", + "window screen", + "window shade", + "Windsor tie", + "wine bottle", + "wing", + "wok", + "wooden spoon", + "wool", + "split-rail fence", + "shipwreck", + "yawl", + "yurt", + "website", + "comic book", + "crossword", + "traffic sign", + "traffic light", + "dust jacket", + "menu", + "plate", + "guacamole", + "consomme", + "hot pot", + "trifle", + "ice cream", + "ice pop", + "baguette", + "bagel", + "pretzel", + "cheeseburger", + "hot dog", + "mashed potato", + "cabbage", + "broccoli", + "cauliflower", + "zucchini", + "spaghetti squash", + "acorn squash", + "butternut squash", + "cucumber", + "artichoke", + "bell pepper", + "cardoon", + "mushroom", + "Granny Smith", + "strawberry", + "orange", + "lemon", + "fig", + "pineapple", + "banana", + "jackfruit", + "custard apple", + "pomegranate", + "hay", + "carbonara", + "chocolate syrup", + "dough", + "meatloaf", + "pizza", + "pot pie", + "burrito", + "red wine", + "espresso", + "cup", + "eggnog", + "alp", + "bubble", + "cliff", + "coral reef", + "geyser", + "lakeshore", + "promontory", + "shoal", + "seashore", + "valley", + "volcano", + "baseball player", + "bridegroom", + "scuba diver", + "rapeseed", + "daisy", + "yellow lady's slipper", + "corn", + "acorn", + "rose hip", + "horse chestnut seed", + "coral fungus", + "agaric", + "gyromitra", + "stinkhorn mushroom", + "earth star", + "hen-of-the-woods", + "bolete", + "ear", + "toilet paper"] \ No newline at end of file diff --git a/demos/image_classifier/images.zip b/demos/image_classifier/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..c58f463aa8ba38c63d81ec5f5d90aea71418d86f --- /dev/null +++ b/demos/image_classifier/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0a3d798e936de549178fa680d496f39d068374f6c3851118cf3997e71eb5b2e5 +size 39415 diff --git a/demos/image_classifier/images/cheetah1.jpeg b/demos/image_classifier/images/cheetah1.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..c510ff30e09c1ce410afa499f0bfc3a63c751134 Binary files /dev/null and b/demos/image_classifier/images/cheetah1.jpeg differ diff --git a/demos/image_classifier/images/cheetah1.jpg b/demos/image_classifier/images/cheetah1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..66d3b48fd19cd8cd8d8437b6f33183b3d3d42589 --- /dev/null +++ b/demos/image_classifier/images/cheetah1.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:35550bfbba996e59c242af00f6a14a9c0d055dfbc52ad069a1a4e8c1c39ca095 +size 20552 diff --git a/demos/image_classifier/images/lion.jpg b/demos/image_classifier/images/lion.jpg new file mode 100644 index 0000000000000000000000000000000000000000..2cf5afb1f0bfe6dac09b7fd6bfeb68e5e80dbe33 --- /dev/null +++ b/demos/image_classifier/images/lion.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c45ece00075f152cb2e6cfd5f1dfd7dc8e83042264685b4f470026240eff3ef +size 18489 diff --git a/demos/image_classifier/requirements.txt b/demos/image_classifier/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..c2bbfa1f2b3c72184f27937009c87c54c826806c --- /dev/null +++ b/demos/image_classifier/requirements.txt @@ -0,0 +1,2 @@ +numpy +tensorflow \ No newline at end of file diff --git a/demos/image_classifier/run.py b/demos/image_classifier/run.py new file mode 100644 index 0000000000000000000000000000000000000000..e96de59317712ccb41d44e7cb184d639d66e0fb9 --- /dev/null +++ b/demos/image_classifier/run.py @@ -0,0 +1,36 @@ +import os +import requests +import tensorflow as tf + +import gradio as gr + +inception_net = tf.keras.applications.MobileNetV2() # load the model + +# Download human-readable labels for ImageNet. +response = requests.get("https://git.io/JJkYN") +labels = response.text.split("\n") + + +def classify_image(inp): + inp = inp.reshape((-1, 224, 224, 3)) + inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp) + prediction = inception_net.predict(inp).flatten() + return {labels[i]: float(prediction[i]) for i in range(1000)} + + +image = gr.Image(shape=(224, 224)) +label = gr.Label(num_top_classes=3) + +demo = gr.Interface( + fn=classify_image, + inputs=image, + outputs=label, + examples=[ + os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg"), + os.path.join(os.path.dirname(__file__), "images/lion.jpg") + ] + ) + +if __name__ == "__main__": + demo.launch() + diff --git a/demos/image_classifier/screenshot.gif b/demos/image_classifier/screenshot.gif new file mode 100644 index 0000000000000000000000000000000000000000..54612cf0f57059d7ab3e2a52c51b5f7b5680db96 --- /dev/null +++ b/demos/image_classifier/screenshot.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:97cc57f521c8b2d482fffeb04da7b452da6d3abb0ced50ed544d22a2456a4a20 +size 421819 diff --git a/demos/image_classifier/screenshot.png b/demos/image_classifier/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..2ec6074bf79d2bc1012831184d83fe2d684ede23 --- /dev/null +++ b/demos/image_classifier/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:53b089dcb36451d6ff3c10592be52bf26109908f27905e83ab905767efc41659 +size 553635 diff --git a/demos/image_classifier_2/files/imagenet_labels.json b/demos/image_classifier_2/files/imagenet_labels.json new file mode 100644 index 0000000000000000000000000000000000000000..fa059ceeab5d18ab9882df2983b92e8270373e39 --- /dev/null +++ b/demos/image_classifier_2/files/imagenet_labels.json @@ -0,0 +1,1000 @@ +["tench", + "goldfish", + "great white shark", + "tiger shark", + "hammerhead shark", + "electric ray", + "stingray", + "cock", + "hen", + "ostrich", + "brambling", + "goldfinch", + "house finch", + "junco", + "indigo bunting", + "American robin", + "bulbul", + "jay", + "magpie", + "chickadee", + "American dipper", + "kite", + "bald eagle", + "vulture", + "great grey owl", + "fire salamander", + "smooth newt", + "newt", + "spotted salamander", + "axolotl", + "American bullfrog", + "tree frog", + "tailed frog", + "loggerhead sea turtle", + "leatherback sea turtle", + "mud turtle", + "terrapin", + "box turtle", + "banded gecko", + "green iguana", + "Carolina anole", + "desert grassland whiptail lizard", + "agama", + "frilled-necked lizard", + "alligator lizard", + "Gila monster", + "European green lizard", + "chameleon", + "Komodo dragon", + "Nile crocodile", + "American alligator", + "triceratops", + "worm snake", + "ring-necked snake", + "eastern hog-nosed snake", + "smooth green snake", + "kingsnake", + "garter snake", + "water snake", + "vine snake", + "night snake", + "boa constrictor", + "African rock python", + "Indian cobra", + "green mamba", + "sea snake", + "Saharan horned viper", + "eastern diamondback rattlesnake", + "sidewinder", + "trilobite", + "harvestman", + "scorpion", + "yellow garden spider", + "barn spider", + "European garden spider", + "southern black widow", + "tarantula", + "wolf spider", + "tick", + "centipede", + "black grouse", + "ptarmigan", + "ruffed grouse", + "prairie grouse", + "peacock", + "quail", + "partridge", + "grey parrot", + "macaw", + "sulphur-crested cockatoo", + "lorikeet", + "coucal", + "bee eater", + "hornbill", + "hummingbird", + "jacamar", + "toucan", + "duck", + "red-breasted merganser", + "goose", + "black swan", + "tusker", + "echidna", + "platypus", + "wallaby", + "koala", + "wombat", + "jellyfish", + "sea anemone", + "brain coral", + "flatworm", + "nematode", + "conch", + "snail", + "slug", + "sea slug", + "chiton", + "chambered nautilus", + "Dungeness crab", + "rock crab", + "fiddler crab", + "red king crab", + "American lobster", + "spiny lobster", + "crayfish", + "hermit crab", + "isopod", + "white stork", + "black stork", + "spoonbill", + "flamingo", + "little blue heron", + "great egret", + "bittern", + "crane (bird)", + "limpkin", + "common gallinule", + "American coot", + "bustard", + "ruddy turnstone", + "dunlin", + "common redshank", + "dowitcher", + "oystercatcher", + "pelican", + "king penguin", + "albatross", + "grey whale", + "killer whale", + "dugong", + "sea lion", + "Chihuahua", + "Japanese Chin", + "Maltese", + "Pekingese", + "Shih Tzu", + "King Charles Spaniel", + "Papillon", + "toy terrier", + "Rhodesian Ridgeback", + "Afghan Hound", + "Basset Hound", + "Beagle", + "Bloodhound", + "Bluetick Coonhound", + "Black and Tan Coonhound", + "Treeing Walker Coonhound", + "English foxhound", + "Redbone Coonhound", + "borzoi", + "Irish Wolfhound", + "Italian Greyhound", + "Whippet", + "Ibizan Hound", + "Norwegian Elkhound", + "Otterhound", + "Saluki", + "Scottish Deerhound", + "Weimaraner", + "Staffordshire Bull Terrier", + "American Staffordshire Terrier", + "Bedlington Terrier", + "Border Terrier", + "Kerry Blue Terrier", + "Irish Terrier", + "Norfolk Terrier", + "Norwich Terrier", + "Yorkshire Terrier", + "Wire Fox Terrier", + "Lakeland Terrier", + "Sealyham Terrier", + "Airedale Terrier", + "Cairn Terrier", + "Australian Terrier", + "Dandie Dinmont Terrier", + "Boston Terrier", + "Miniature Schnauzer", + "Giant Schnauzer", + "Standard Schnauzer", + "Scottish Terrier", + "Tibetan Terrier", + "Australian Silky Terrier", + "Soft-coated Wheaten Terrier", + "West Highland White Terrier", + "Lhasa Apso", + "Flat-Coated Retriever", + "Curly-coated Retriever", + "Golden Retriever", + "Labrador Retriever", + "Chesapeake Bay Retriever", + "German Shorthaired Pointer", + "Vizsla", + "English Setter", + "Irish Setter", + "Gordon Setter", + "Brittany", + "Clumber Spaniel", + "English Springer Spaniel", + "Welsh Springer Spaniel", + "Cocker Spaniels", + "Sussex Spaniel", + "Irish Water Spaniel", + "Kuvasz", + "Schipperke", + "Groenendael", + "Malinois", + "Briard", + "Australian Kelpie", + "Komondor", + "Old English Sheepdog", + "Shetland Sheepdog", + "collie", + "Border Collie", + "Bouvier des Flandres", + "Rottweiler", + "German Shepherd Dog", + "Dobermann", + "Miniature Pinscher", + "Greater Swiss Mountain Dog", + "Bernese Mountain Dog", + "Appenzeller Sennenhund", + "Entlebucher Sennenhund", + "Boxer", + "Bullmastiff", + "Tibetan Mastiff", + "French Bulldog", + "Great Dane", + "St. Bernard", + "husky", + "Alaskan Malamute", + "Siberian Husky", + "Dalmatian", + "Affenpinscher", + "Basenji", + "pug", + "Leonberger", + "Newfoundland", + "Pyrenean Mountain Dog", + "Samoyed", + "Pomeranian", + "Chow Chow", + "Keeshond", + "Griffon Bruxellois", + "Pembroke Welsh Corgi", + "Cardigan Welsh Corgi", + "Toy Poodle", + "Miniature Poodle", + "Standard Poodle", + "Mexican hairless dog", + "grey wolf", + "Alaskan tundra wolf", + "red wolf", + "coyote", + "dingo", + "dhole", + "African wild dog", + "hyena", + "red fox", + "kit fox", + "Arctic fox", + "grey fox", + "tabby cat", + "tiger cat", + "Persian cat", + "Siamese cat", + "Egyptian Mau", + "cougar", + "lynx", + "leopard", + "snow leopard", + "jaguar", + "lion", + "tiger", + "cheetah", + "brown bear", + "American black bear", + "polar bear", + "sloth bear", + "mongoose", + "meerkat", + "tiger beetle", + "ladybug", + "ground beetle", + "longhorn beetle", + "leaf beetle", + "dung beetle", + "rhinoceros beetle", + "weevil", + "fly", + "bee", + "ant", + "grasshopper", + "cricket", + "stick insect", + "cockroach", + "mantis", + "cicada", + "leafhopper", + "lacewing", + "dragonfly", + "damselfly", + "red admiral", + "ringlet", + "monarch butterfly", + "small white", + "sulphur butterfly", + "gossamer-winged butterfly", + "starfish", + "sea urchin", + "sea cucumber", + "cottontail rabbit", + "hare", + "Angora rabbit", + "hamster", + "porcupine", + "fox squirrel", + "marmot", + "beaver", + "guinea pig", + "common sorrel", + "zebra", + "pig", + "wild boar", + "warthog", + "hippopotamus", + "ox", + "water buffalo", + "bison", + "ram", + "bighorn sheep", + "Alpine ibex", + "hartebeest", + "impala", + "gazelle", + "dromedary", + "llama", + "weasel", + "mink", + "European polecat", + "black-footed ferret", + "otter", + "skunk", + "badger", + "armadillo", + "three-toed sloth", + "orangutan", + "gorilla", + "chimpanzee", + "gibbon", + "siamang", + "guenon", + "patas monkey", + "baboon", + "macaque", + "langur", + "black-and-white colobus", + "proboscis monkey", + "marmoset", + "white-headed capuchin", + "howler monkey", + "titi", + "Geoffroy's spider monkey", + "common squirrel monkey", + "ring-tailed lemur", + "indri", + "Asian elephant", + "African bush elephant", + "red panda", + "giant panda", + "snoek", + "eel", + "coho salmon", + "rock beauty", + "clownfish", + "sturgeon", + "garfish", + "lionfish", + "pufferfish", + "abacus", + "abaya", + "academic gown", + "accordion", + "acoustic guitar", + "aircraft carrier", + "airliner", + "airship", + "altar", + "ambulance", + "amphibious vehicle", + "analog clock", + "apiary", + "apron", + "waste container", + "assault rifle", + "backpack", + "bakery", + "balance beam", + "balloon", + "ballpoint pen", + "Band-Aid", + "banjo", + "baluster", + "barbell", + "barber chair", + "barbershop", + "barn", + "barometer", + "barrel", + "wheelbarrow", + "baseball", + "basketball", + "bassinet", + "bassoon", + "swimming cap", + "bath towel", + "bathtub", + "station wagon", + "lighthouse", + "beaker", + "military cap", + "beer bottle", + "beer glass", + "bell-cot", + "bib", + "tandem bicycle", + "bikini", + "ring binder", + "binoculars", + "birdhouse", + "boathouse", + "bobsleigh", + "bolo tie", + "poke bonnet", + "bookcase", + "bookstore", + "bottle cap", + "bow", + "bow tie", + "brass", + "bra", + "breakwater", + "breastplate", + "broom", + "bucket", + "buckle", + "bulletproof vest", + "high-speed train", + "butcher shop", + "taxicab", + "cauldron", + "candle", + "cannon", + "canoe", + "can opener", + "cardigan", + "car mirror", + "carousel", + "tool kit", + "carton", + "car wheel", + "automated teller machine", + "cassette", + "cassette player", + "castle", + "catamaran", + "CD player", + "cello", + "mobile phone", + "chain", + "chain-link fence", + "chain mail", + "chainsaw", + "chest", + "chiffonier", + "chime", + "china cabinet", + "Christmas stocking", + "church", + "movie theater", + "cleaver", + "cliff dwelling", + "cloak", + "clogs", + "cocktail shaker", + "coffee mug", + "coffeemaker", + "coil", + "combination lock", + "computer keyboard", + "confectionery store", + "container ship", + "convertible", + "corkscrew", + "cornet", + "cowboy boot", + "cowboy hat", + "cradle", + "crane (machine)", + "crash helmet", + "crate", + "infant bed", + "Crock Pot", + "croquet ball", + "crutch", + "cuirass", + "dam", + "desk", + "desktop computer", + "rotary dial telephone", + "diaper", + "digital clock", + "digital watch", + "dining table", + "dishcloth", + "dishwasher", + "disc brake", + "dock", + "dog sled", + "dome", + "doormat", + "drilling rig", + "drum", + "drumstick", + "dumbbell", + "Dutch oven", + "electric fan", + "electric guitar", + "electric locomotive", + "entertainment center", + "envelope", + "espresso machine", + "face powder", + "feather boa", + "filing cabinet", + "fireboat", + "fire engine", + "fire screen sheet", + "flagpole", + "flute", + "folding chair", + "football helmet", + "forklift", + "fountain", + "fountain pen", + "four-poster bed", + "freight car", + "French horn", + "frying pan", + "fur coat", + "garbage truck", + "gas mask", + "gas pump", + "goblet", + "go-kart", + "golf ball", + "golf cart", + "gondola", + "gong", + "gown", + "grand piano", + "greenhouse", + "grille", + "grocery store", + "guillotine", + "barrette", + "hair spray", + "half-track", + "hammer", + "hamper", + "hair dryer", + "hand-held computer", + "handkerchief", + "hard disk drive", + "harmonica", + "harp", + "harvester", + "hatchet", + "holster", + "home theater", + "honeycomb", + "hook", + "hoop skirt", + "horizontal bar", + "horse-drawn vehicle", + "hourglass", + "iPod", + "clothes iron", + "jack-o'-lantern", + "jeans", + "jeep", + "T-shirt", + "jigsaw puzzle", + "pulled rickshaw", + "joystick", + "kimono", + "knee pad", + "knot", + "lab coat", + "ladle", + "lampshade", + "laptop computer", + "lawn mower", + "lens cap", + "paper knife", + "library", + "lifeboat", + "lighter", + "limousine", + "ocean liner", + "lipstick", + "slip-on shoe", + "lotion", + "speaker", + "loupe", + "sawmill", + "magnetic compass", + "mail bag", + "mailbox", + "tights", + "tank suit", + "manhole cover", + "maraca", + "marimba", + "mask", + "match", + "maypole", + "maze", + "measuring cup", + "medicine chest", + "megalith", + "microphone", + "microwave oven", + "military uniform", + "milk can", + "minibus", + "miniskirt", + "minivan", + "missile", + "mitten", + "mixing bowl", + "mobile home", + "Model T", + "modem", + "monastery", + "monitor", + "moped", + "mortar", + "square academic cap", + "mosque", + "mosquito net", + "scooter", + "mountain bike", + "tent", + "computer mouse", + "mousetrap", + "moving van", + "muzzle", + "nail", + "neck brace", + "necklace", + "nipple", + "notebook computer", + "obelisk", + "oboe", + "ocarina", + "odometer", + "oil filter", + "organ", + "oscilloscope", + "overskirt", + "bullock cart", + "oxygen mask", + "packet", + "paddle", + "paddle wheel", + "padlock", + "paintbrush", + "pajamas", + "palace", + "pan flute", + "paper towel", + "parachute", + "parallel bars", + "park bench", + "parking meter", + "passenger car", + "patio", + "payphone", + "pedestal", + "pencil case", + "pencil sharpener", + "perfume", + "Petri dish", + "photocopier", + "plectrum", + "Pickelhaube", + "picket fence", + "pickup truck", + "pier", + "piggy bank", + "pill bottle", + "pillow", + "ping-pong ball", + "pinwheel", + "pirate ship", + "pitcher", + "hand plane", + "planetarium", + "plastic bag", + "plate rack", + "plow", + "plunger", + "Polaroid camera", + "pole", + "police van", + "poncho", + "billiard table", + "soda bottle", + "pot", + "potter's wheel", + "power drill", + "prayer rug", + "printer", + "prison", + "projectile", + "projector", + "hockey puck", + "punching bag", + "purse", + "quill", + "quilt", + "race car", + "racket", + "radiator", + "radio", + "radio telescope", + "rain barrel", + "recreational vehicle", + "reel", + "reflex camera", + "refrigerator", + "remote control", + "restaurant", + "revolver", + "rifle", + "rocking chair", + "rotisserie", + "eraser", + "rugby ball", + "ruler", + "running shoe", + "safe", + "safety pin", + "salt shaker", + "sandal", + "sarong", + "saxophone", + "scabbard", + "weighing scale", + "school bus", + "schooner", + "scoreboard", + "CRT screen", + "screw", + "screwdriver", + "seat belt", + "sewing machine", + "shield", + "shoe store", + "shoji", + "shopping basket", + "shopping cart", + "shovel", + "shower cap", + "shower curtain", + "ski", + "ski mask", + "sleeping bag", + "slide rule", + "sliding door", + "slot machine", + "snorkel", + "snowmobile", + "snowplow", + "soap dispenser", + "soccer ball", + "sock", + "solar thermal collector", + "sombrero", + "soup bowl", + "space bar", + "space heater", + "space shuttle", + "spatula", + "motorboat", + "spider web", + "spindle", + "sports car", + "spotlight", + "stage", + "steam locomotive", + "through arch bridge", + "steel drum", + "stethoscope", + "scarf", + "stone wall", + "stopwatch", + "stove", + "strainer", + "tram", + "stretcher", + "couch", + "stupa", + "submarine", + "suit", + "sundial", + "sunglass", + "sunglasses", + "sunscreen", + "suspension bridge", + "mop", + "sweatshirt", + "swimsuit", + "swing", + "switch", + "syringe", + "table lamp", + "tank", + "tape player", + "teapot", + "teddy bear", + "television", + "tennis ball", + "thatched roof", + "front curtain", + "thimble", + "threshing machine", + "throne", + "tile roof", + "toaster", + "tobacco shop", + "toilet seat", + "torch", + "totem pole", + "tow truck", + "toy store", + "tractor", + "semi-trailer truck", + "tray", + "trench coat", + "tricycle", + "trimaran", + "tripod", + "triumphal arch", + "trolleybus", + "trombone", + "tub", + "turnstile", + "typewriter keyboard", + "umbrella", + "unicycle", + "upright piano", + "vacuum cleaner", + "vase", + "vault", + "velvet", + "vending machine", + "vestment", + "viaduct", + "violin", + "volleyball", + "waffle iron", + "wall clock", + "wallet", + "wardrobe", + "military aircraft", + "sink", + "washing machine", + "water bottle", + "water jug", + "water tower", + "whiskey jug", + "whistle", + "wig", + "window screen", + "window shade", + "Windsor tie", + "wine bottle", + "wing", + "wok", + "wooden spoon", + "wool", + "split-rail fence", + "shipwreck", + "yawl", + "yurt", + "website", + "comic book", + "crossword", + "traffic sign", + "traffic light", + "dust jacket", + "menu", + "plate", + "guacamole", + "consomme", + "hot pot", + "trifle", + "ice cream", + "ice pop", + "baguette", + "bagel", + "pretzel", + "cheeseburger", + "hot dog", + "mashed potato", + "cabbage", + "broccoli", + "cauliflower", + "zucchini", + "spaghetti squash", + "acorn squash", + "butternut squash", + "cucumber", + "artichoke", + "bell pepper", + "cardoon", + "mushroom", + "Granny Smith", + "strawberry", + "orange", + "lemon", + "fig", + "pineapple", + "banana", + "jackfruit", + "custard apple", + "pomegranate", + "hay", + "carbonara", + "chocolate syrup", + "dough", + "meatloaf", + "pizza", + "pot pie", + "burrito", + "red wine", + "espresso", + "cup", + "eggnog", + "alp", + "bubble", + "cliff", + "coral reef", + "geyser", + "lakeshore", + "promontory", + "shoal", + "seashore", + "valley", + "volcano", + "baseball player", + "bridegroom", + "scuba diver", + "rapeseed", + "daisy", + "yellow lady's slipper", + "corn", + "acorn", + "rose hip", + "horse chestnut seed", + "coral fungus", + "agaric", + "gyromitra", + "stinkhorn mushroom", + "earth star", + "hen-of-the-woods", + "bolete", + "ear", + "toilet paper"] \ No newline at end of file diff --git a/demos/image_classifier_2/requirements.txt b/demos/image_classifier_2/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..2ab187b1e35179bc3f56ab7cdd30df60b7b95a54 --- /dev/null +++ b/demos/image_classifier_2/requirements.txt @@ -0,0 +1,3 @@ +pillow +torch +torchvision \ No newline at end of file diff --git a/demos/image_classifier_2/run.py b/demos/image_classifier_2/run.py new file mode 100644 index 0000000000000000000000000000000000000000..d378b8fe44af4d8f1d875db8399e61e928c2eaa9 --- /dev/null +++ b/demos/image_classifier_2/run.py @@ -0,0 +1,29 @@ +import requests +import torch +from PIL import Image +from torchvision import transforms + +import gradio as gr + +model = torch.hub.load("pytorch/vision:v0.6.0", "resnet18", pretrained=True).eval() + +# Download human-readable labels for ImageNet. +response = requests.get("https://git.io/JJkYN") +labels = response.text.split("\n") + + +def predict(inp): + inp = Image.fromarray(inp.astype("uint8"), "RGB") + inp = transforms.ToTensor()(inp).unsqueeze(0) + with torch.no_grad(): + prediction = torch.nn.functional.softmax(model(inp)[0], dim=0) + return {labels[i]: float(prediction[i]) for i in range(1000)} + + +inputs = gr.Image() +outputs = gr.Label(num_top_classes=3) + +demo = gr.Interface(fn=predict, inputs=inputs, outputs=outputs) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/image_classifier_interface_load/cheetah1.jpeg b/demos/image_classifier_interface_load/cheetah1.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..c510ff30e09c1ce410afa499f0bfc3a63c751134 Binary files /dev/null and b/demos/image_classifier_interface_load/cheetah1.jpeg differ diff --git a/demos/image_classifier_interface_load/cheetah1.jpg b/demos/image_classifier_interface_load/cheetah1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..66d3b48fd19cd8cd8d8437b6f33183b3d3d42589 --- /dev/null +++ b/demos/image_classifier_interface_load/cheetah1.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:35550bfbba996e59c242af00f6a14a9c0d055dfbc52ad069a1a4e8c1c39ca095 +size 20552 diff --git a/demos/image_classifier_interface_load/lion.jpg b/demos/image_classifier_interface_load/lion.jpg new file mode 100644 index 0000000000000000000000000000000000000000..2cf5afb1f0bfe6dac09b7fd6bfeb68e5e80dbe33 --- /dev/null +++ b/demos/image_classifier_interface_load/lion.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c45ece00075f152cb2e6cfd5f1dfd7dc8e83042264685b4f470026240eff3ef +size 18489 diff --git a/demos/image_classifier_interface_load/run.py b/demos/image_classifier_interface_load/run.py new file mode 100644 index 0000000000000000000000000000000000000000..c7dd053309b2a51cc6c1be5628eb19141cacf8fc --- /dev/null +++ b/demos/image_classifier_interface_load/run.py @@ -0,0 +1,32 @@ +import gradio as gr +import pathlib + +current_dir = pathlib.Path(__file__) + +images = [current_dir / "cheetah1.jpeg", current_dir / "cheetah1.jpg", current_dir / "lion.jpg"] + + +img_classifier = gr.Interface.load( + "models/google/vit-base-patch16-224", examples=images, cache_examples=False +) + + +def func(img, text): + return img_classifier(img), text + + +using_img_classifier_as_function = gr.Interface( + func, + [gr.Image(type="filepath"), "text"], + ["label", "text"], + examples=[ + [current_dir / "cheetah1.jpeg", None], + [current_dir / "cheetah1.jpg", "cheetah"], + [current_dir / "lion.jpg", "lion"], + ], + cache_examples=True, +) +demo = gr.TabbedInterface([using_img_classifier_as_function, img_classifier]) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/image_classifier_interpretation/files/imagenet_labels.json b/demos/image_classifier_interpretation/files/imagenet_labels.json new file mode 100644 index 0000000000000000000000000000000000000000..fa059ceeab5d18ab9882df2983b92e8270373e39 --- /dev/null +++ b/demos/image_classifier_interpretation/files/imagenet_labels.json @@ -0,0 +1,1000 @@ +["tench", + "goldfish", + "great white shark", + "tiger shark", + "hammerhead shark", + "electric ray", + "stingray", + "cock", + "hen", + "ostrich", + "brambling", + "goldfinch", + "house finch", + "junco", + "indigo bunting", + "American robin", + "bulbul", + "jay", + "magpie", + "chickadee", + "American dipper", + "kite", + "bald eagle", + "vulture", + "great grey owl", + "fire salamander", + "smooth newt", + "newt", + "spotted salamander", + "axolotl", + "American bullfrog", + "tree frog", + "tailed frog", + "loggerhead sea turtle", + "leatherback sea turtle", + "mud turtle", + "terrapin", + "box turtle", + "banded gecko", + "green iguana", + "Carolina anole", + "desert grassland whiptail lizard", + "agama", + "frilled-necked lizard", + "alligator lizard", + "Gila monster", + "European green lizard", + "chameleon", + "Komodo dragon", + "Nile crocodile", + "American alligator", + "triceratops", + "worm snake", + "ring-necked snake", + "eastern hog-nosed snake", + "smooth green snake", + "kingsnake", + "garter snake", + "water snake", + "vine snake", + "night snake", + "boa constrictor", + "African rock python", + "Indian cobra", + "green mamba", + "sea snake", + "Saharan horned viper", + "eastern diamondback rattlesnake", + "sidewinder", + "trilobite", + "harvestman", + "scorpion", + "yellow garden spider", + "barn spider", + "European garden spider", + "southern black widow", + "tarantula", + "wolf spider", + "tick", + "centipede", + "black grouse", + "ptarmigan", + "ruffed grouse", + "prairie grouse", + "peacock", + "quail", + "partridge", + "grey parrot", + "macaw", + "sulphur-crested cockatoo", + "lorikeet", + "coucal", + "bee eater", + "hornbill", + "hummingbird", + "jacamar", + "toucan", + "duck", + "red-breasted merganser", + "goose", + "black swan", + "tusker", + "echidna", + "platypus", + "wallaby", + "koala", + "wombat", + "jellyfish", + "sea anemone", + "brain coral", + "flatworm", + "nematode", + "conch", + "snail", + "slug", + "sea slug", + "chiton", + "chambered nautilus", + "Dungeness crab", + "rock crab", + "fiddler crab", + "red king crab", + "American lobster", + "spiny lobster", + "crayfish", + "hermit crab", + "isopod", + "white stork", + "black stork", + "spoonbill", + "flamingo", + "little blue heron", + "great egret", + "bittern", + "crane (bird)", + "limpkin", + "common gallinule", + "American coot", + "bustard", + "ruddy turnstone", + "dunlin", + "common redshank", + "dowitcher", + "oystercatcher", + "pelican", + "king penguin", + "albatross", + "grey whale", + "killer whale", + "dugong", + "sea lion", + "Chihuahua", + "Japanese Chin", + "Maltese", + "Pekingese", + "Shih Tzu", + "King Charles Spaniel", + "Papillon", + "toy terrier", + "Rhodesian Ridgeback", + "Afghan Hound", + "Basset Hound", + "Beagle", + "Bloodhound", + "Bluetick Coonhound", + "Black and Tan Coonhound", + "Treeing Walker Coonhound", + "English foxhound", + "Redbone Coonhound", + "borzoi", + "Irish Wolfhound", + "Italian Greyhound", + "Whippet", + "Ibizan Hound", + "Norwegian Elkhound", + "Otterhound", + "Saluki", + "Scottish Deerhound", + "Weimaraner", + "Staffordshire Bull Terrier", + "American Staffordshire Terrier", + "Bedlington Terrier", + "Border Terrier", + "Kerry Blue Terrier", + "Irish Terrier", + "Norfolk Terrier", + "Norwich Terrier", + "Yorkshire Terrier", + "Wire Fox Terrier", + "Lakeland Terrier", + "Sealyham Terrier", + "Airedale Terrier", + "Cairn Terrier", + "Australian Terrier", + "Dandie Dinmont Terrier", + "Boston Terrier", + "Miniature Schnauzer", + "Giant Schnauzer", + "Standard Schnauzer", + "Scottish Terrier", + "Tibetan Terrier", + "Australian Silky Terrier", + "Soft-coated Wheaten Terrier", + "West Highland White Terrier", + "Lhasa Apso", + "Flat-Coated Retriever", + "Curly-coated Retriever", + "Golden Retriever", + "Labrador Retriever", + "Chesapeake Bay Retriever", + "German Shorthaired Pointer", + "Vizsla", + "English Setter", + "Irish Setter", + "Gordon Setter", + "Brittany", + "Clumber Spaniel", + "English Springer Spaniel", + "Welsh Springer Spaniel", + "Cocker Spaniels", + "Sussex Spaniel", + "Irish Water Spaniel", + "Kuvasz", + "Schipperke", + "Groenendael", + "Malinois", + "Briard", + "Australian Kelpie", + "Komondor", + "Old English Sheepdog", + "Shetland Sheepdog", + "collie", + "Border Collie", + "Bouvier des Flandres", + "Rottweiler", + "German Shepherd Dog", + "Dobermann", + "Miniature Pinscher", + "Greater Swiss Mountain Dog", + "Bernese Mountain Dog", + "Appenzeller Sennenhund", + "Entlebucher Sennenhund", + "Boxer", + "Bullmastiff", + "Tibetan Mastiff", + "French Bulldog", + "Great Dane", + "St. Bernard", + "husky", + "Alaskan Malamute", + "Siberian Husky", + "Dalmatian", + "Affenpinscher", + "Basenji", + "pug", + "Leonberger", + "Newfoundland", + "Pyrenean Mountain Dog", + "Samoyed", + "Pomeranian", + "Chow Chow", + "Keeshond", + "Griffon Bruxellois", + "Pembroke Welsh Corgi", + "Cardigan Welsh Corgi", + "Toy Poodle", + "Miniature Poodle", + "Standard Poodle", + "Mexican hairless dog", + "grey wolf", + "Alaskan tundra wolf", + "red wolf", + "coyote", + "dingo", + "dhole", + "African wild dog", + "hyena", + "red fox", + "kit fox", + "Arctic fox", + "grey fox", + "tabby cat", + "tiger cat", + "Persian cat", + "Siamese cat", + "Egyptian Mau", + "cougar", + "lynx", + "leopard", + "snow leopard", + "jaguar", + "lion", + "tiger", + "cheetah", + "brown bear", + "American black bear", + "polar bear", + "sloth bear", + "mongoose", + "meerkat", + "tiger beetle", + "ladybug", + "ground beetle", + "longhorn beetle", + "leaf beetle", + "dung beetle", + "rhinoceros beetle", + "weevil", + "fly", + "bee", + "ant", + "grasshopper", + "cricket", + "stick insect", + "cockroach", + "mantis", + "cicada", + "leafhopper", + "lacewing", + "dragonfly", + "damselfly", + "red admiral", + "ringlet", + "monarch butterfly", + "small white", + "sulphur butterfly", + "gossamer-winged butterfly", + "starfish", + "sea urchin", + "sea cucumber", + "cottontail rabbit", + "hare", + "Angora rabbit", + "hamster", + "porcupine", + "fox squirrel", + "marmot", + "beaver", + "guinea pig", + "common sorrel", + "zebra", + "pig", + "wild boar", + "warthog", + "hippopotamus", + "ox", + "water buffalo", + "bison", + "ram", + "bighorn sheep", + "Alpine ibex", + "hartebeest", + "impala", + "gazelle", + "dromedary", + "llama", + "weasel", + "mink", + "European polecat", + "black-footed ferret", + "otter", + "skunk", + "badger", + "armadillo", + "three-toed sloth", + "orangutan", + "gorilla", + "chimpanzee", + "gibbon", + "siamang", + "guenon", + "patas monkey", + "baboon", + "macaque", + "langur", + "black-and-white colobus", + "proboscis monkey", + "marmoset", + "white-headed capuchin", + "howler monkey", + "titi", + "Geoffroy's spider monkey", + "common squirrel monkey", + "ring-tailed lemur", + "indri", + "Asian elephant", + "African bush elephant", + "red panda", + "giant panda", + "snoek", + "eel", + "coho salmon", + "rock beauty", + "clownfish", + "sturgeon", + "garfish", + "lionfish", + "pufferfish", + "abacus", + "abaya", + "academic gown", + "accordion", + "acoustic guitar", + "aircraft carrier", + "airliner", + "airship", + "altar", + "ambulance", + "amphibious vehicle", + "analog clock", + "apiary", + "apron", + "waste container", + "assault rifle", + "backpack", + "bakery", + "balance beam", + "balloon", + "ballpoint pen", + "Band-Aid", + "banjo", + "baluster", + "barbell", + "barber chair", + "barbershop", + "barn", + "barometer", + "barrel", + "wheelbarrow", + "baseball", + "basketball", + "bassinet", + "bassoon", + "swimming cap", + "bath towel", + "bathtub", + "station wagon", + "lighthouse", + "beaker", + "military cap", + "beer bottle", + "beer glass", + "bell-cot", + "bib", + "tandem bicycle", + "bikini", + "ring binder", + "binoculars", + "birdhouse", + "boathouse", + "bobsleigh", + "bolo tie", + "poke bonnet", + "bookcase", + "bookstore", + "bottle cap", + "bow", + "bow tie", + "brass", + "bra", + "breakwater", + "breastplate", + "broom", + "bucket", + "buckle", + "bulletproof vest", + "high-speed train", + "butcher shop", + "taxicab", + "cauldron", + "candle", + "cannon", + "canoe", + "can opener", + "cardigan", + "car mirror", + "carousel", + "tool kit", + "carton", + "car wheel", + "automated teller machine", + "cassette", + "cassette player", + "castle", + "catamaran", + "CD player", + "cello", + "mobile phone", + "chain", + "chain-link fence", + "chain mail", + "chainsaw", + "chest", + "chiffonier", + "chime", + "china cabinet", + "Christmas stocking", + "church", + "movie theater", + "cleaver", + "cliff dwelling", + "cloak", + "clogs", + "cocktail shaker", + "coffee mug", + "coffeemaker", + "coil", + "combination lock", + "computer keyboard", + "confectionery store", + "container ship", + "convertible", + "corkscrew", + "cornet", + "cowboy boot", + "cowboy hat", + "cradle", + "crane (machine)", + "crash helmet", + "crate", + "infant bed", + "Crock Pot", + "croquet ball", + "crutch", + "cuirass", + "dam", + "desk", + "desktop computer", + "rotary dial telephone", + "diaper", + "digital clock", + "digital watch", + "dining table", + "dishcloth", + "dishwasher", + "disc brake", + "dock", + "dog sled", + "dome", + "doormat", + "drilling rig", + "drum", + "drumstick", + "dumbbell", + "Dutch oven", + "electric fan", + "electric guitar", + "electric locomotive", + "entertainment center", + "envelope", + "espresso machine", + "face powder", + "feather boa", + "filing cabinet", + "fireboat", + "fire engine", + "fire screen sheet", + "flagpole", + "flute", + "folding chair", + "football helmet", + "forklift", + "fountain", + "fountain pen", + "four-poster bed", + "freight car", + "French horn", + "frying pan", + "fur coat", + "garbage truck", + "gas mask", + "gas pump", + "goblet", + "go-kart", + "golf ball", + "golf cart", + "gondola", + "gong", + "gown", + "grand piano", + "greenhouse", + "grille", + "grocery store", + "guillotine", + "barrette", + "hair spray", + "half-track", + "hammer", + "hamper", + "hair dryer", + "hand-held computer", + "handkerchief", + "hard disk drive", + "harmonica", + "harp", + "harvester", + "hatchet", + "holster", + "home theater", + "honeycomb", + "hook", + "hoop skirt", + "horizontal bar", + "horse-drawn vehicle", + "hourglass", + "iPod", + "clothes iron", + "jack-o'-lantern", + "jeans", + "jeep", + "T-shirt", + "jigsaw puzzle", + "pulled rickshaw", + "joystick", + "kimono", + "knee pad", + "knot", + "lab coat", + "ladle", + "lampshade", + "laptop computer", + "lawn mower", + "lens cap", + "paper knife", + "library", + "lifeboat", + "lighter", + "limousine", + "ocean liner", + "lipstick", + "slip-on shoe", + "lotion", + "speaker", + "loupe", + "sawmill", + "magnetic compass", + "mail bag", + "mailbox", + "tights", + "tank suit", + "manhole cover", + "maraca", + "marimba", + "mask", + "match", + "maypole", + "maze", + "measuring cup", + "medicine chest", + "megalith", + "microphone", + "microwave oven", + "military uniform", + "milk can", + "minibus", + "miniskirt", + "minivan", + "missile", + "mitten", + "mixing bowl", + "mobile home", + "Model T", + "modem", + "monastery", + "monitor", + "moped", + "mortar", + "square academic cap", + "mosque", + "mosquito net", + "scooter", + "mountain bike", + "tent", + "computer mouse", + "mousetrap", + "moving van", + "muzzle", + "nail", + "neck brace", + "necklace", + "nipple", + "notebook computer", + "obelisk", + "oboe", + "ocarina", + "odometer", + "oil filter", + "organ", + "oscilloscope", + "overskirt", + "bullock cart", + "oxygen mask", + "packet", + "paddle", + "paddle wheel", + "padlock", + "paintbrush", + "pajamas", + "palace", + "pan flute", + "paper towel", + "parachute", + "parallel bars", + "park bench", + "parking meter", + "passenger car", + "patio", + "payphone", + "pedestal", + "pencil case", + "pencil sharpener", + "perfume", + "Petri dish", + "photocopier", + "plectrum", + "Pickelhaube", + "picket fence", + "pickup truck", + "pier", + "piggy bank", + "pill bottle", + "pillow", + "ping-pong ball", + "pinwheel", + "pirate ship", + "pitcher", + "hand plane", + "planetarium", + "plastic bag", + "plate rack", + "plow", + "plunger", + "Polaroid camera", + "pole", + "police van", + "poncho", + "billiard table", + "soda bottle", + "pot", + "potter's wheel", + "power drill", + "prayer rug", + "printer", + "prison", + "projectile", + "projector", + "hockey puck", + "punching bag", + "purse", + "quill", + "quilt", + "race car", + "racket", + "radiator", + "radio", + "radio telescope", + "rain barrel", + "recreational vehicle", + "reel", + "reflex camera", + "refrigerator", + "remote control", + "restaurant", + "revolver", + "rifle", + "rocking chair", + "rotisserie", + "eraser", + "rugby ball", + "ruler", + "running shoe", + "safe", + "safety pin", + "salt shaker", + "sandal", + "sarong", + "saxophone", + "scabbard", + "weighing scale", + "school bus", + "schooner", + "scoreboard", + "CRT screen", + "screw", + "screwdriver", + "seat belt", + "sewing machine", + "shield", + "shoe store", + "shoji", + "shopping basket", + "shopping cart", + "shovel", + "shower cap", + "shower curtain", + "ski", + "ski mask", + "sleeping bag", + "slide rule", + "sliding door", + "slot machine", + "snorkel", + "snowmobile", + "snowplow", + "soap dispenser", + "soccer ball", + "sock", + "solar thermal collector", + "sombrero", + "soup bowl", + "space bar", + "space heater", + "space shuttle", + "spatula", + "motorboat", + "spider web", + "spindle", + "sports car", + "spotlight", + "stage", + "steam locomotive", + "through arch bridge", + "steel drum", + "stethoscope", + "scarf", + "stone wall", + "stopwatch", + "stove", + "strainer", + "tram", + "stretcher", + "couch", + "stupa", + "submarine", + "suit", + "sundial", + "sunglass", + "sunglasses", + "sunscreen", + "suspension bridge", + "mop", + "sweatshirt", + "swimsuit", + "swing", + "switch", + "syringe", + "table lamp", + "tank", + "tape player", + "teapot", + "teddy bear", + "television", + "tennis ball", + "thatched roof", + "front curtain", + "thimble", + "threshing machine", + "throne", + "tile roof", + "toaster", + "tobacco shop", + "toilet seat", + "torch", + "totem pole", + "tow truck", + "toy store", + "tractor", + "semi-trailer truck", + "tray", + "trench coat", + "tricycle", + "trimaran", + "tripod", + "triumphal arch", + "trolleybus", + "trombone", + "tub", + "turnstile", + "typewriter keyboard", + "umbrella", + "unicycle", + "upright piano", + "vacuum cleaner", + "vase", + "vault", + "velvet", + "vending machine", + "vestment", + "viaduct", + "violin", + "volleyball", + "waffle iron", + "wall clock", + "wallet", + "wardrobe", + "military aircraft", + "sink", + "washing machine", + "water bottle", + "water jug", + "water tower", + "whiskey jug", + "whistle", + "wig", + "window screen", + "window shade", + "Windsor tie", + "wine bottle", + "wing", + "wok", + "wooden spoon", + "wool", + "split-rail fence", + "shipwreck", + "yawl", + "yurt", + "website", + "comic book", + "crossword", + "traffic sign", + "traffic light", + "dust jacket", + "menu", + "plate", + "guacamole", + "consomme", + "hot pot", + "trifle", + "ice cream", + "ice pop", + "baguette", + "bagel", + "pretzel", + "cheeseburger", + "hot dog", + "mashed potato", + "cabbage", + "broccoli", + "cauliflower", + "zucchini", + "spaghetti squash", + "acorn squash", + "butternut squash", + "cucumber", + "artichoke", + "bell pepper", + "cardoon", + "mushroom", + "Granny Smith", + "strawberry", + "orange", + "lemon", + "fig", + "pineapple", + "banana", + "jackfruit", + "custard apple", + "pomegranate", + "hay", + "carbonara", + "chocolate syrup", + "dough", + "meatloaf", + "pizza", + "pot pie", + "burrito", + "red wine", + "espresso", + "cup", + "eggnog", + "alp", + "bubble", + "cliff", + "coral reef", + "geyser", + "lakeshore", + "promontory", + "shoal", + "seashore", + "valley", + "volcano", + "baseball player", + "bridegroom", + "scuba diver", + "rapeseed", + "daisy", + "yellow lady's slipper", + "corn", + "acorn", + "rose hip", + "horse chestnut seed", + "coral fungus", + "agaric", + "gyromitra", + "stinkhorn mushroom", + "earth star", + "hen-of-the-woods", + "bolete", + "ear", + "toilet paper"] \ No newline at end of file diff --git a/demos/image_classifier_interpretation/images/cheetah1.jpg b/demos/image_classifier_interpretation/images/cheetah1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..66d3b48fd19cd8cd8d8437b6f33183b3d3d42589 --- /dev/null +++ b/demos/image_classifier_interpretation/images/cheetah1.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:35550bfbba996e59c242af00f6a14a9c0d055dfbc52ad069a1a4e8c1c39ca095 +size 20552 diff --git a/demos/image_classifier_interpretation/images/lion.jpg b/demos/image_classifier_interpretation/images/lion.jpg new file mode 100644 index 0000000000000000000000000000000000000000..2cf5afb1f0bfe6dac09b7fd6bfeb68e5e80dbe33 --- /dev/null +++ b/demos/image_classifier_interpretation/images/lion.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c45ece00075f152cb2e6cfd5f1dfd7dc8e83042264685b4f470026240eff3ef +size 18489 diff --git a/demos/image_classifier_interpretation/requirements.txt b/demos/image_classifier_interpretation/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..c2bbfa1f2b3c72184f27937009c87c54c826806c --- /dev/null +++ b/demos/image_classifier_interpretation/requirements.txt @@ -0,0 +1,2 @@ +numpy +tensorflow \ No newline at end of file diff --git a/demos/image_classifier_interpretation/run.py b/demos/image_classifier_interpretation/run.py new file mode 100644 index 0000000000000000000000000000000000000000..f052b3ec80ace747429aa99e3a8e3aa37e465121 --- /dev/null +++ b/demos/image_classifier_interpretation/run.py @@ -0,0 +1,28 @@ +import requests +import tensorflow as tf + +import gradio as gr + +inception_net = tf.keras.applications.MobileNetV2() # load the model + +# Download human-readable labels for ImageNet. +response = requests.get("https://git.io/JJkYN") +labels = response.text.split("\n") + + +def classify_image(inp): + inp = inp.reshape((-1, 224, 224, 3)) + inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp) + prediction = inception_net.predict(inp).flatten() + return {labels[i]: float(prediction[i]) for i in range(1000)} + + +image = gr.Image(shape=(224, 224)) +label = gr.Label(num_top_classes=3) + +demo = gr.Interface( + fn=classify_image, inputs=image, outputs=label, interpretation="default" +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/image_classifier_interpretation/screenshot.gif b/demos/image_classifier_interpretation/screenshot.gif new file mode 100644 index 0000000000000000000000000000000000000000..54612cf0f57059d7ab3e2a52c51b5f7b5680db96 --- /dev/null +++ b/demos/image_classifier_interpretation/screenshot.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:97cc57f521c8b2d482fffeb04da7b452da6d3abb0ced50ed544d22a2456a4a20 +size 421819 diff --git a/demos/image_classifier_interpretation/screenshot.png b/demos/image_classifier_interpretation/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..2ec6074bf79d2bc1012831184d83fe2d684ede23 --- /dev/null +++ b/demos/image_classifier_interpretation/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:53b089dcb36451d6ff3c10592be52bf26109908f27905e83ab905767efc41659 +size 553635 diff --git a/demos/image_mod/images/cheetah1.jpg b/demos/image_mod/images/cheetah1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..66d3b48fd19cd8cd8d8437b6f33183b3d3d42589 --- /dev/null +++ b/demos/image_mod/images/cheetah1.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:35550bfbba996e59c242af00f6a14a9c0d055dfbc52ad069a1a4e8c1c39ca095 +size 20552 diff --git a/demos/image_mod/images/lion.jpg b/demos/image_mod/images/lion.jpg new file mode 100644 index 0000000000000000000000000000000000000000..2cf5afb1f0bfe6dac09b7fd6bfeb68e5e80dbe33 --- /dev/null +++ b/demos/image_mod/images/lion.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c45ece00075f152cb2e6cfd5f1dfd7dc8e83042264685b4f470026240eff3ef +size 18489 diff --git a/demos/image_mod/images/logo.png b/demos/image_mod/images/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..b15ea0336b8c14631f77f00f72695d14eb76f698 --- /dev/null +++ b/demos/image_mod/images/logo.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:92ba3bf0faed5ab591ec5ad3d06fa89409eea4de694b2b6cea3cc989e08ecc79 +size 5328 diff --git a/demos/image_mod/run.py b/demos/image_mod/run.py new file mode 100644 index 0000000000000000000000000000000000000000..df891fc0ac63f6f593b02322385e5498b472aa3a --- /dev/null +++ b/demos/image_mod/run.py @@ -0,0 +1,17 @@ +import gradio as gr +import os + + +def image_mod(image): + return image.rotate(45) + + +demo = gr.Interface(image_mod, gr.Image(type="pil"), "image", + flagging_options=["blurry", "incorrect", "other"], examples=[ + os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg"), + os.path.join(os.path.dirname(__file__), "images/lion.jpg"), + os.path.join(os.path.dirname(__file__), "images/logo.png") + ]) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/image_mod/screenshot.png b/demos/image_mod/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..07939d98debb9d571a75ba02e2948cf75afeb65a --- /dev/null +++ b/demos/image_mod/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9e12c6742252fc970c3983e6691050d55034d40d1b553b93a94eb9b5c94c6e9 +size 1108299 diff --git a/demos/image_mod_default_image/images/cheetah1.jpg b/demos/image_mod_default_image/images/cheetah1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..66d3b48fd19cd8cd8d8437b6f33183b3d3d42589 --- /dev/null +++ b/demos/image_mod_default_image/images/cheetah1.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:35550bfbba996e59c242af00f6a14a9c0d055dfbc52ad069a1a4e8c1c39ca095 +size 20552 diff --git a/demos/image_mod_default_image/images/lion.jpg b/demos/image_mod_default_image/images/lion.jpg new file mode 100644 index 0000000000000000000000000000000000000000..2cf5afb1f0bfe6dac09b7fd6bfeb68e5e80dbe33 --- /dev/null +++ b/demos/image_mod_default_image/images/lion.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c45ece00075f152cb2e6cfd5f1dfd7dc8e83042264685b4f470026240eff3ef +size 18489 diff --git a/demos/image_mod_default_image/images/logo.png b/demos/image_mod_default_image/images/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..b15ea0336b8c14631f77f00f72695d14eb76f698 --- /dev/null +++ b/demos/image_mod_default_image/images/logo.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:92ba3bf0faed5ab591ec5ad3d06fa89409eea4de694b2b6cea3cc989e08ecc79 +size 5328 diff --git a/demos/image_mod_default_image/run.py b/demos/image_mod_default_image/run.py new file mode 100644 index 0000000000000000000000000000000000000000..c2ad1f8be43b53d179254cb9a0cadcb4c11378b3 --- /dev/null +++ b/demos/image_mod_default_image/run.py @@ -0,0 +1,18 @@ +import gradio as gr +import os + + +def image_mod(image): + return image.rotate(45) + + +cheetah = os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg") + +demo = gr.Interface(image_mod, gr.Image(type="pil", value=cheetah), "image", + flagging_options=["blurry", "incorrect", "other"], examples=[ + os.path.join(os.path.dirname(__file__), "images/lion.jpg"), + os.path.join(os.path.dirname(__file__), "images/logo.png") + ]) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/input-output/config.json b/demos/input-output/config.json new file mode 100644 index 0000000000000000000000000000000000000000..f264101b261efdc3d3325ed808d043072e98cad5 --- /dev/null +++ b/demos/input-output/config.json @@ -0,0 +1,50 @@ +{ + "mode": "blocks", + "components": [ + { + "id": 1, + "type": "textbox", + "props": { + "lines": 1, + "placeholder": null, + "value": "", + "name": "textbox", + "label": "Input-Output", + "css": {}, + "interactive": null + } + }, + { + "id": 2, + "type": "button", + "props": { + "value": "Run", + "name": "button", + "label": null, + "css": {}, + "interactive": null + } + } + ], + "theme": "default", + "layout": { + "id": 0, + "children": [ + { + "id": 1 + }, + { + "id": 2 + } + ] + }, + "dependencies": [ + { + "targets": [2], + "trigger": "click", + "inputs": [1], + "outputs": [1], + "queue": false + } + ] +} diff --git a/demos/input-output/run.py b/demos/input-output/run.py new file mode 100644 index 0000000000000000000000000000000000000000..a09c05fc514335919f2cfa7330b15cebf10bbe82 --- /dev/null +++ b/demos/input-output/run.py @@ -0,0 +1,16 @@ +import gradio as gr + + +def image_mod(text): + return text[::-1] + + +demo = gr.Blocks() + +with demo: + text = gr.Textbox(label="Input-Output") + btn = gr.Button("Run") + btn.click(image_mod, text, text) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/interface_parallel/run.py b/demos/interface_parallel/run.py new file mode 100644 index 0000000000000000000000000000000000000000..8ac946bd166cc889554294c8ea4a0d74bcb240bf --- /dev/null +++ b/demos/interface_parallel/run.py @@ -0,0 +1,8 @@ +import gradio as gr + +greeter_1 = gr.Interface(lambda name: f"Hello {name}!", inputs="textbox", outputs=gr.Textbox(label="Greeter 1")) +greeter_2 = gr.Interface(lambda name: f"Greetings {name}!", inputs="textbox", outputs=gr.Textbox(label="Greeter 2")) +demo = gr.Parallel(greeter_1, greeter_2) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/interface_parallel_load/run.py b/demos/interface_parallel_load/run.py new file mode 100644 index 0000000000000000000000000000000000000000..cae63973294842b75f6759537c1d01d861e3574c --- /dev/null +++ b/demos/interface_parallel_load/run.py @@ -0,0 +1,10 @@ +import gradio as gr + +generator1 = gr.Interface.load("huggingface/gpt2") +generator2 = gr.Interface.load("huggingface/EleutherAI/gpt-neo-2.7B") +generator3 = gr.Interface.load("huggingface/EleutherAI/gpt-j-6B") + +demo = gr.Parallel(generator1, generator2, generator3) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/interface_random_slider/run.py b/demos/interface_random_slider/run.py new file mode 100644 index 0000000000000000000000000000000000000000..9965e1f35ac6df15fedb5205aaecfe3d46b09209 --- /dev/null +++ b/demos/interface_random_slider/run.py @@ -0,0 +1,23 @@ +import gradio as gr + + +def func(slider_1, slider_2): + return slider_1 + slider_2 * 5 + + +demo = gr.Interface( + func, + [ + gr.Slider(minimum=1.5, maximum=250000.89, randomize=True, label="Random Big Range"), + gr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label="Random only multiple of 0.05 allowed"), + gr.Slider(minimum=0, maximum=1, randomize=True, step=0.25, label="Random only multiples of 0.25 allowed"), + gr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label="Random between -100 and 100 step 3"), + gr.Slider(minimum=-100, maximum=100, randomize=True, label="Random between -100 and 100"), + gr.Slider(value=0.25, minimum=5, maximum=30, step=-1), + ], + "number", + interpretation="default" +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/interface_series/run.py b/demos/interface_series/run.py new file mode 100644 index 0000000000000000000000000000000000000000..ac942ff94b23e605f75948797f796208e11e3a9a --- /dev/null +++ b/demos/interface_series/run.py @@ -0,0 +1,10 @@ +import gradio as gr + +get_name = gr.Interface(lambda name: name, inputs="textbox", outputs="textbox") +prepend_hello = gr.Interface(lambda name: f"Hello {name}!", inputs="textbox", outputs="textbox") +append_nice = gr.Interface(lambda greeting: f"{greeting} Nice to meet you!", + inputs="textbox", outputs=gr.Textbox(label="Greeting")) +demo = gr.Series(get_name, prepend_hello, append_nice) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/interface_series_load/run.py b/demos/interface_series_load/run.py new file mode 100644 index 0000000000000000000000000000000000000000..38e694c42c36860b98ce204aa816a18a94b02e4a --- /dev/null +++ b/demos/interface_series_load/run.py @@ -0,0 +1,9 @@ +import gradio as gr + +generator = gr.Interface.load("huggingface/gpt2") +translator = gr.Interface.load("huggingface/t5-small") + +demo = gr.Series(generator, translator) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/kitchen_sink/config.json b/demos/kitchen_sink/config.json new file mode 100644 index 0000000000000000000000000000000000000000..416a4ed813b377e1ec953bb54c95481692371d1e --- /dev/null +++ b/demos/kitchen_sink/config.json @@ -0,0 +1,941 @@ +{ + "version": "3.0.16\n", + "mode": "blocks", + "dev_mode": true, + "components": [ + { + "id": 26, + "type": "markdown", + "props": { + "value": "

Kitchen Sink

", + "name": "markdown", + "visible": true, + "style": {} + } + }, + { + "id": 27, + "type": "markdown", + "props": { + "value": "

Try out all the components!

", + "name": "markdown", + "visible": true, + "style": {} + } + }, + { + "id": 28, + "type": "row", + "props": { + "type": "row", + "visible": true, + "style": { + "equal_height": false, + "mobile_collapse": true + } + } + }, + { + "id": 29, + "type": "column", + "props": { + "type": "column", + "variant": "panel", + "visible": true, + "style": {} + } + }, + { + "id": 30, + "type": "column", + "props": { + "type": "column", + "variant": "default", + "visible": true, + "style": {} + } + }, + { + "id": 0, + "type": "textbox", + "props": { + "lines": 1, + "max_lines": 20, + "value": "Lorem ipsum", + "label": "Textbox", + "show_label": true, + "name": "textbox", + "visible": true, + "style": {} + } + }, + { + "id": 1, + "type": "textbox", + "props": { + "lines": 3, + "max_lines": 20, + "placeholder": "Type here..", + "value": "", + "label": "Textbox 2", + "show_label": true, + "name": "textbox", + "visible": true, + "style": {} + } + }, + { + "id": 2, + "type": "number", + "props": { + "value": 42.0, + "label": "Number", + "show_label": true, + "name": "number", + "visible": true, + "style": {} + } + }, + { + "id": 3, + "type": "slider", + "props": { + "minimum": 10, + "maximum": 20, + "step": 0.1, + "value": 15, + "label": "Slider: 10 - 20", + "show_label": true, + "name": "slider", + "visible": true, + "style": {} + } + }, + { + "id": 4, + "type": "slider", + "props": { + "minimum": 0, + "maximum": 20, + "step": 0.04, + "value": 0, + "label": "Slider: step @ 0.04", + "show_label": true, + "name": "slider", + "visible": true, + "style": {} + } + }, + { + "id": 5, + "type": "checkbox", + "props": { + "value": false, + "label": "Checkbox", + "show_label": true, + "name": "checkbox", + "visible": true, + "style": {} + } + }, + { + "id": 6, + "type": "checkboxgroup", + "props": { + "choices": [ + "foo", + "bar", + "baz" + ], + "value": [ + "foo", + "bar" + ], + "label": "CheckboxGroup", + "show_label": true, + "name": "checkboxgroup", + "visible": true, + "style": {} + } + }, + { + "id": 7, + "type": "radio", + "props": { + "choices": [ + "foo", + "bar", + "baz" + ], + "value": "baz", + "label": "Radio", + "show_label": true, + "name": "radio", + "visible": true, + "style": {} + } + }, + { + "id": 8, + "type": "dropdown", + "props": { + "choices": [ + "foo", + "bar", + "baz" + ], + "value": "foo", + "label": "Dropdown", + "show_label": true, + "name": "dropdown", + "visible": true, + "style": {} + } + }, + { + "id": 9, + "type": "image", + "props": { + "image_mode": "RGB", + "source": "upload", + "tool": "editor", + "streaming": false, + "label": "Image", + "show_label": true, + "name": "image", + "visible": true, + "style": {} + } + }, + { + "id": 10, + "type": "image", + "props": { + "image_mode": "RGB", + "source": "upload", + "tool": "select", + "streaming": false, + "label": "Image w/ Cropper", + "show_label": true, + "name": "image", + "visible": true, + "style": {} + } + }, + { + "id": 11, + "type": "image", + "props": { + "image_mode": "RGB", 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Learn more about Gradio

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Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S +890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C +891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q diff --git a/demos/kitchen_sink/files/world.mp4 b/demos/kitchen_sink/files/world.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..9bce44c33e275d6107240a1101032a7835fd8eed --- /dev/null +++ b/demos/kitchen_sink/files/world.mp4 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:71944d7430c461f0cd6e7fd10cee7eb72786352a3678fc7bc0ae3d410f72aece +size 1570024 diff --git a/demos/kitchen_sink/run.py b/demos/kitchen_sink/run.py new file mode 100755 index 0000000000000000000000000000000000000000..6b9e5147bed408a7d827f65d559545fc9a1ae596 --- /dev/null +++ b/demos/kitchen_sink/run.py @@ -0,0 +1,161 @@ +import os +import json + +import numpy as np + +import gradio as gr + +CHOICES = ["foo", "bar", "baz"] +JSONOBJ = """{"items":{"item":[{"id": "0001","type": null,"is_good": false,"ppu": 0.55,"batters":{"batter":[{ "id": "1001", "type": "Regular" },{ "id": "1002", "type": "Chocolate" },{ "id": "1003", "type": "Blueberry" },{ "id": "1004", "type": "Devil's Food" }]},"topping":[{ "id": "5001", "type": "None" },{ "id": "5002", "type": "Glazed" },{ "id": "5005", "type": "Sugar" },{ "id": "5007", "type": "Powdered Sugar" },{ "id": "5006", "type": "Chocolate with Sprinkles" },{ "id": "5003", "type": "Chocolate" },{ "id": "5004", "type": "Maple" }]}]}}""" + + +def fn( + text1, + text2, + num, + slider1, + slider2, + single_checkbox, + checkboxes, + radio, + dropdown, + im1, + im2, + im3, + im4, + video, + audio1, + audio2, + file, + df1, + df2, +): + return ( + (text1 if single_checkbox else text2) + + ", selected:" + + ", ".join(checkboxes), # Text + { + "positive": num / (num + slider1 + slider2), + "negative": slider1 / (num + slider1 + slider2), + "neutral": slider2 / (num + slider1 + slider2), + }, # Label + (audio1[0], np.flipud(audio1[1])) + if audio1 is not None + else os.path.join(os.path.dirname(__file__), "files/cantina.wav"), # Audio + np.flipud(im1) + if im1 is not None + else os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"), # Image + video + if video is not None + else os.path.join(os.path.dirname(__file__), "files/world.mp4"), # Video + [ + ("The", "art"), + ("quick brown", "adj"), + ("fox", "nn"), + ("jumped", "vrb"), + ("testing testing testing", None), + ("over", "prp"), + ("the", "art"), + ("testing", None), + ("lazy", "adj"), + ("dogs", "nn"), + (".", "punc"), + ] + + [(f"test {x}", f"test {x}") for x in range(10)], # HighlightedText + # [("The testing testing testing", None), ("quick brown", 0.2), ("fox", 1), ("jumped", -1), ("testing testing testing", 0), ("over", 0), ("the", 0), ("testing", 0), ("lazy", 1), ("dogs", 0), (".", 1)] + [(f"test {x}", x/10) for x in range(-10, 10)], # HighlightedText + [ + ("The testing testing testing", None), + ("over", 0.6), + ("the", 0.2), + ("testing", None), + ("lazy", -0.1), + ("dogs", 0.4), + (".", 0), + ] + + [(f"test", x / 10) for x in range(-10, 10)], # HighlightedText + json.loads(JSONOBJ), # JSON + "", # HTML + os.path.join(os.path.dirname(__file__), "files/titanic.csv"), + df1, # Dataframe + np.random.randint(0, 10, (4, 4)), # Dataframe + df2, # Timeseries + ) + + +demo = gr.Interface( + fn, + inputs=[ + gr.Textbox(value="Lorem ipsum", label="Textbox"), + gr.Textbox(lines=3, placeholder="Type here..", label="Textbox 2"), + gr.Number(label="Number", value=42), + gr.Slider(10, 20, value=15, label="Slider: 10 - 20"), + gr.Slider(maximum=20, step=0.04, label="Slider: step @ 0.04"), + gr.Checkbox(label="Checkbox"), + gr.CheckboxGroup( + label="CheckboxGroup", choices=CHOICES, value=CHOICES[0:2] + ), + gr.Radio(label="Radio", choices=CHOICES, value=CHOICES[2]), + gr.Dropdown(label="Dropdown", choices=CHOICES), + gr.Image(label="Image"), + gr.Image(label="Image w/ Cropper", tool="select"), + gr.Image(label="Sketchpad", source="canvas"), + gr.Image(label="Webcam", source="webcam"), + gr.Video(label="Video"), + gr.Audio(label="Audio"), + gr.Audio(label="Microphone", source="microphone"), + gr.File(label="File"), + gr.Dataframe(label="Dataframe", headers=["Name", "Age", "Gender"]), + gr.Timeseries(x="time", y=["price", "value"], colors=["pink", "purple"]), + ], + outputs=[ + gr.Textbox(label="Textbox"), + gr.Label(label="Label"), + gr.Audio(label="Audio"), + gr.Image(label="Image"), + gr.Video(label="Video"), + gr.HighlightedText( + label="HighlightedText", color_map={"punc": "pink", "test 0": "blue"} + ), + gr.HighlightedText(label="HighlightedText", show_legend=True), + gr.JSON(label="JSON"), + gr.HTML(label="HTML"), + gr.File(label="File"), + gr.Dataframe(label="Dataframe"), + gr.Dataframe(label="Numpy"), + gr.Timeseries(x="time", y=["price", "value"], label="Timeseries"), + ], + examples=[ + [ + "the quick brown fox", + "jumps over the lazy dog", + 10, + 12, + 4, + True, + ["foo", "baz"], + "baz", + "bar", + os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"), + os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"), + os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"), + os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"), + os.path.join(os.path.dirname(__file__), "files/world.mp4"), + os.path.join(os.path.dirname(__file__), "files/cantina.wav"), + os.path.join(os.path.dirname(__file__), "files/cantina.wav"), + os.path.join(os.path.dirname(__file__), "files/titanic.csv"), + [[1, 2, 3], [3, 4, 5]], + os.path.join(os.path.dirname(__file__), "files/time.csv"), + ] + ] + * 3, + theme="default", + title="Kitchen Sink", + cache_examples=False, + description="Try out all the components!", + article="Learn more about [Gradio](http://gradio.app)", +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/kitchen_sink_random/__init__.py b/demos/kitchen_sink_random/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demos/kitchen_sink_random/constants.py b/demos/kitchen_sink_random/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..5edd7b065a7e667aae918dc16107af50d30aa03e --- /dev/null +++ b/demos/kitchen_sink_random/constants.py @@ -0,0 +1,71 @@ +import numpy as np +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import random +import os + + +def random_plot(): + start_year = 2020 + x = np.arange(start_year, start_year + random.randint(0, 10)) + year_count = x.shape[0] + plt_format = "-" + fig = plt.figure() + ax = fig.add_subplot(111) + series = np.arange(0, year_count, dtype=float) + series = series**2 + series += np.random.rand(year_count) + ax.plot(x, series, plt_format) + return fig + + +img_dir = os.path.join(os.path.dirname(__file__), "..", "image_classifier", "images") +file_dir = os.path.join(os.path.dirname(__file__), "..", "kitchen_sink", "files") +model3d_dir = os.path.join(os.path.dirname(__file__), "..", "model3D", "files") +highlighted_text_output_1 = [ + { + "entity": "I-LOC", + "score": 0.9988978, + "index": 2, + "word": "Chicago", + "start": 5, + "end": 12, + }, + { + "entity": "I-MISC", + "score": 0.9958592, + "index": 5, + "word": "Pakistani", + "start": 22, + "end": 31, + }, +] +highlighted_text_output_2 = [ + { + "entity": "I-LOC", + "score": 0.9988978, + "index": 2, + "word": "Chicago", + "start": 5, + "end": 12, + }, + { + "entity": "I-LOC", + "score": 0.9958592, + "index": 5, + "word": "Pakistan", + "start": 22, + "end": 30, + }, +] + +highlighted_text = "Does Chicago have any Pakistani restaurants" + + +def random_model3d(): + model_3d = random.choice( + [os.path.join(model3d_dir, model) for model in os.listdir(model3d_dir) if model != "source.txt"] + ) + return model_3d diff --git a/demos/kitchen_sink_random/run.py b/demos/kitchen_sink_random/run.py new file mode 100644 index 0000000000000000000000000000000000000000..42718218133c515b48849369f8042de9570def94 --- /dev/null +++ b/demos/kitchen_sink_random/run.py @@ -0,0 +1,98 @@ +import gradio as gr +from datetime import datetime +import random +import string +import os +import pandas as pd + +from constants import ( + file_dir, + img_dir, + highlighted_text, + highlighted_text_output_2, + highlighted_text_output_1, + random_plot, + random_model3d, +) + + +demo = gr.Interface( + lambda x: x, + inputs=[ + gr.Textbox(value=lambda: datetime.now(), label="Current Time"), + gr.Number(value=lambda: random.random(), label="Ranom Percentage"), + gr.Slider(minimum=-1, maximum=1, randomize=True, label="Slider with randomize"), + gr.Slider( + minimum=0, + maximum=1, + value=lambda: random.random(), + label="Slider with value func", + ), + gr.Checkbox(value=lambda: random.random() > 0.5, label="Random Checkbox"), + gr.CheckboxGroup( + choices=["a", "b", "c", "d"], + value=lambda: random.choice(["a", "b", "c", "d"]), + label="Random CheckboxGroup", + ), + gr.Radio( + choices=list(string.ascii_lowercase), + value=lambda: random.choice(string.ascii_lowercase), + ), + gr.Dropdown( + choices=["a", "b", "c", "d", "e"], + value=lambda: random.choice(["a", "b", "c"]), + ), + gr.Image( + value=lambda: random.choice( + [os.path.join(img_dir, img) for img in os.listdir(img_dir)] + ) + ), + gr.Video(value=lambda: os.path.join(file_dir, "world.mp4")), + gr.Audio(value=lambda: os.path.join(file_dir, "cantina.wav")), + gr.File( + value=lambda: random.choice( + [os.path.join(file_dir, img) for img in os.listdir(file_dir)] + ) + ), + gr.Dataframe( + value=lambda: pd.DataFrame( + {"random_number_rows": range(random.randint(0, 10))} + ) + ), + gr.Timeseries(value=lambda: os.path.join(file_dir, "time.csv")), + gr.Variable(value=lambda: random.choice(string.ascii_lowercase)), + gr.Button(value=lambda: random.choice(["Run", "Go", "predict"])), + gr.ColorPicker(value=lambda: random.choice(["#000000", "#ff0000", "#0000FF"])), + gr.Label(value=lambda: random.choice(["Pedestrian", "Car", "Cyclist"])), + gr.HighlightedText( + value=lambda: random.choice( + [ + {"text": highlighted_text, "entities": highlighted_text_output_1}, + {"text": highlighted_text, "entities": highlighted_text_output_2}, + ] + ), + ), + gr.JSON(value=lambda: random.choice([{"a": 1}, {"b": 2}])), + gr.HTML( + value=lambda: random.choice( + [ + '

I am red

', + '

I am blue

', + ] + ) + ), + gr.Gallery( + value=lambda: [os.path.join(img_dir, img) for img in os.listdir(img_dir)] + ), + gr.Chatbot( + value=lambda: random.choice([[("hello", "hi!")], [("bye", "goodbye!")]]) + ), + gr.Model3D(value=random_model3d), + gr.Plot(value=random_plot), + gr.Markdown(value=lambda: f"### {random.choice(['Hello', 'Hi', 'Goodbye!'])}"), + ], + outputs=None, +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/live_with_vars/run.py b/demos/live_with_vars/run.py new file mode 100644 index 0000000000000000000000000000000000000000..70c39b36ccbfa1e1ad3239c4efadcdcb8fccbf14 --- /dev/null +++ b/demos/live_with_vars/run.py @@ -0,0 +1,9 @@ +import gradio as gr + +demo = gr.Interface( + lambda x, y: (x + y if y is not None else x, x + y if y is not None else x), + ["textbox", "state"], + ["textbox", "state"], live=True) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/longest_word/run.py b/demos/longest_word/run.py new file mode 100644 index 0000000000000000000000000000000000000000..4fef82a328c6152c79a52c25ddde70db796c335e --- /dev/null +++ b/demos/longest_word/run.py @@ -0,0 +1,18 @@ +import gradio as gr + + +def longest_word(text): + words = text.split(" ") + lengths = [len(word) for word in words] + return max(lengths) + + +ex = "The quick brown fox jumped over the lazy dog." + +demo = gr.Interface( + longest_word, "textbox", "label", interpretation="default", examples=[[ex]] +) + + +if __name__ == "__main__": + demo.launch() diff --git a/demos/main_note/audio/cantina.wav b/demos/main_note/audio/cantina.wav new file mode 100644 index 0000000000000000000000000000000000000000..83651968c382d3c17ad48d84995c9b71753ba694 --- /dev/null +++ b/demos/main_note/audio/cantina.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e5f73001b324e413bdcf658fca5485057c333f4198e51e7e86bb2e772cd0973 +size 132344 diff --git a/demos/main_note/audio/recording1.wav b/demos/main_note/audio/recording1.wav new file mode 100644 index 0000000000000000000000000000000000000000..305c419a090c2c195531467ecd8a8704438fe9c8 --- /dev/null +++ b/demos/main_note/audio/recording1.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1d087876795ff2bc8b6e1a872eb9a9b2cca44db1866e9c9fa00df5a2556919ff +size 639020 diff --git a/demos/main_note/requirements.txt b/demos/main_note/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..ca6034584e0ec2dde9b16b581bab6d6a8327a59a --- /dev/null +++ b/demos/main_note/requirements.txt @@ -0,0 +1,3 @@ +scipy +numpy +matplotlib \ No newline at end of file diff --git a/demos/main_note/run.py b/demos/main_note/run.py new file mode 100644 index 0000000000000000000000000000000000000000..1e02c99c753b7463d4bb8e3559b440e60872e7a7 --- /dev/null +++ b/demos/main_note/run.py @@ -0,0 +1,56 @@ +from math import log2, pow +import os + +import numpy as np +from scipy.fftpack import fft + +import gradio as gr + +A4 = 440 +C0 = A4 * pow(2, -4.75) +name = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"] + + +def get_pitch(freq): + h = round(12 * log2(freq / C0)) + n = h % 12 + return name[n] + + +def main_note(audio): + rate, y = audio + if len(y.shape) == 2: + y = y.T[0] + N = len(y) + T = 1.0 / rate + x = np.linspace(0.0, N * T, N) + yf = fft(y) + yf2 = 2.0 / N * np.abs(yf[0 : N // 2]) + xf = np.linspace(0.0, 1.0 / (2.0 * T), N // 2) + + volume_per_pitch = {} + total_volume = np.sum(yf2) + for freq, volume in zip(xf, yf2): + if freq == 0: + continue + pitch = get_pitch(freq) + if pitch not in volume_per_pitch: + volume_per_pitch[pitch] = 0 + volume_per_pitch[pitch] += 1.0 * volume / total_volume + volume_per_pitch = {k: float(v) for k, v in volume_per_pitch.items()} + return volume_per_pitch + + +demo = gr.Interface( + main_note, + gr.Audio(source="microphone"), + gr.Label(num_top_classes=4), + examples=[ + [os.path.join(os.path.dirname(__file__),"audio/recording1.wav")], + [os.path.join(os.path.dirname(__file__),"audio/cantina.wav")], + ], + interpretation="default", +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/main_note/screenshot.png b/demos/main_note/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..8737f808157a6a8885edb770fb1f0175028030fc --- /dev/null +++ b/demos/main_note/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eadd6d523e8e5a5828e54b2c5981d5e95fa6e6778685933ebc2323c4213df45a +size 35164 diff --git a/demos/matrix_transpose/run.py b/demos/matrix_transpose/run.py new file mode 100644 index 0000000000000000000000000000000000000000..1fa9ed34184ec6c6063305cf71b2a662222d5207 --- /dev/null +++ b/demos/matrix_transpose/run.py @@ -0,0 +1,24 @@ +import numpy as np + +import gradio as gr + + +def transpose(matrix): + return matrix.T + + +demo = gr.Interface( + transpose, + gr.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3), + "numpy", + examples=[ + [np.zeros((3, 3)).tolist()], + [np.ones((2, 2)).tolist()], + [np.random.randint(0, 10, (3, 10)).tolist()], + [np.random.randint(0, 10, (10, 3)).tolist()], + [np.random.randint(0, 10, (10, 10)).tolist()], + ], +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/matrix_transpose/screenshot.png b/demos/matrix_transpose/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..42d75711036776a327c100f2d676491dc8f0025d --- /dev/null +++ b/demos/matrix_transpose/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:292486e5bb355609c587f8de5c28309edaa7b1b212368a7ba01bf221ace88c81 +size 35292 diff --git a/demos/model3D/files/Bunny.obj b/demos/model3D/files/Bunny.obj new file mode 100644 index 0000000000000000000000000000000000000000..9baeb363cce8feb5dd62ecaf8d64a14b6c50ce37 --- /dev/null +++ b/demos/model3D/files/Bunny.obj @@ -0,0 +1,7474 @@ +# OBJ file format with ext .obj +# vertex count = 2503 +# face 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", 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792 603 601 +f 793 601 843 +f 844 669 794 +f 599 843 601 +f 598 844 843 +f 597 845 844 +f 845 675 669 +f 845 596 591 +f 780 783 778 +# 1610 faces, 0 coords texture + +# End of File diff --git a/demos/model3D/files/source.txt b/demos/model3D/files/source.txt new file mode 100644 index 0000000000000000000000000000000000000000..6d164a4f1659327a6cd6faacdf7afbb840457342 --- /dev/null +++ b/demos/model3D/files/source.txt @@ -0,0 +1,9 @@ +Stanford Bunny: +https://graphics.stanford.edu/data/3Dscanrep/ +https://graphics.stanford.edu/~mdfisher/Data/Meshes/bunny.obj + +Duck & Fox: +https://github.com/KhronosGroup/glTF-Sample-Models + +Face: +https://github.com/mikedh/trimesh/tree/main/models \ No newline at end of file diff --git a/demos/model3D/run.py b/demos/model3D/run.py new file mode 100644 index 0000000000000000000000000000000000000000..40438e48d203a9053720e1017c2a620029d743fc --- /dev/null +++ b/demos/model3D/run.py @@ -0,0 +1,29 @@ +import time +import gradio as gr +import os + + +def load_mesh(mesh_file_name): + time.sleep(2) + return mesh_file_name, mesh_file_name + + +demo = gr.Interface( + fn=load_mesh, + inputs=gr.Model3D(), + outputs=[ + gr.Model3D( + clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model"), + gr.File(label="Download 3D Model") + ], + examples=[ + [os.path.join(os.path.dirname(__file__), "files/Bunny.obj")], + [os.path.join(os.path.dirname(__file__), "files/Duck.glb")], + [os.path.join(os.path.dirname(__file__), "files/Fox.gltf")], + [os.path.join(os.path.dirname(__file__), "files/face.obj")], + ], + cache_examples=True, +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/ner_pipeline/requirements.txt b/demos/ner_pipeline/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..39dab0fdd98d55da5ce06ddf1dacbdbda14b1372 --- /dev/null +++ b/demos/ner_pipeline/requirements.txt @@ -0,0 +1,2 @@ +torch +transformers \ No newline at end of file diff --git a/demos/ner_pipeline/run.py b/demos/ner_pipeline/run.py new file mode 100644 index 0000000000000000000000000000000000000000..f131632aec5a38a6e8b3839ac50979d29779fe3b --- /dev/null +++ b/demos/ner_pipeline/run.py @@ -0,0 +1,21 @@ +from transformers import pipeline + +import gradio as gr + +ner_pipeline = pipeline("ner") + +examples = [ + "Does Chicago have any stores and does Joe live here?", +] + +def ner(text): + output = ner_pipeline(text) + return {"text": text, "entities": output} + +demo = gr.Interface(ner, + gr.Textbox(placeholder="Enter sentence here..."), + gr.HighlightedText(), + examples=examples) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/no_input/run.py b/demos/no_input/run.py new file mode 100644 index 0000000000000000000000000000000000000000..c1d9344d7902f7673af289d5f04ab706f924178b --- /dev/null +++ b/demos/no_input/run.py @@ -0,0 +1,18 @@ +import gradio as gr +import random + +sentence_list = [ + "Good morning!", + "Prayers are with you, have a safe day!", + "I love you!" +] + + +def random_sentence(): + return sentence_list[random.randint(0, 2)] + + +demo = gr.Interface(fn=random_sentence, inputs=None, outputs="text") + +if __name__ == "__main__": + demo.launch() diff --git a/demos/outbreak_forecast/config.json b/demos/outbreak_forecast/config.json new file mode 100644 index 0000000000000000000000000000000000000000..64bf6b23213953885e8c4710c55892ba3d667208 --- /dev/null +++ b/demos/outbreak_forecast/config.json @@ -0,0 +1,442 @@ +{ + "version": "3.1.1\n", + "mode": "interface", + "dev_mode": true, + "components": [ + { + "id": 7, + "type": "row", + "props": { + "type": "row", + "visible": true, + "style": { + "equal_height": false, + "mobile_collapse": true + } + } + }, + { + "id": 8, + "type": "column", + "props": { + "type": "column", + "variant": "panel", + "visible": true, + "style": {} + } + }, + { + "id": 9, + "type": "column", + "props": { + "type": "column", + "variant": "default", + "visible": true, + "style": {} + } + }, + { + "id": 0, + "type": "dropdown", + "props": { + 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{\"variant\": null, \"visible\": true, \"__type__\": \"update\"}]\n ", + "status_tracker": null, + "queue": null, + "api_name": null, + "scroll_to_output": false, + "show_progress": true + }, + { + "targets": [ + 16 + ], + "trigger": "click", + "inputs": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "outputs": [], + "backend_fn": true, + "js": null, + "status_tracker": null, + "queue": false, + "api_name": null, + "scroll_to_output": false, + "show_progress": true + }, + { + "targets": [ + 17 + ], + "trigger": "click", + "inputs": [ + 17 + ], + "outputs": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "backend_fn": true, + "js": null, + "status_tracker": null, + "queue": false, + "api_name": null, + "scroll_to_output": false, + "show_progress": true + } + ] +} \ No newline at end of file diff --git a/demos/outbreak_forecast/requirements.txt b/demos/outbreak_forecast/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..aad0724c8d9833f6dbd603041b4f9c158411411f --- /dev/null +++ b/demos/outbreak_forecast/requirements.txt @@ -0,0 +1,4 @@ +numpy +matplotlib +bokeh +plotly \ No newline at end of file diff --git a/demos/outbreak_forecast/run.py b/demos/outbreak_forecast/run.py new file mode 100644 index 0000000000000000000000000000000000000000..9d51b21c344ef152a6dfa6ba8759fb7f43f4e8ca --- /dev/null +++ b/demos/outbreak_forecast/run.py @@ -0,0 +1,70 @@ +from math import sqrt + +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +import numpy as np +import plotly.express as px +import pandas as pd +import bokeh.plotting as bk +from bokeh.models import ColumnDataSource +from bokeh.embed import json_item + +import gradio as gr + + +def outbreak(plot_type, r, month, countries, social_distancing): + months = ["January", "February", "March", "April", "May"] + m = months.index(month) + start_day = 30 * m + final_day = 30 * (m + 1) + x = np.arange(start_day, final_day + 1) + pop_count = {"USA": 350, "Canada": 40, "Mexico": 300, "UK": 120} + if social_distancing: + r = sqrt(r) + df = pd.DataFrame({'day': x}) + for country in countries: + df[country] = ( x ** (r) * (pop_count[country] + 1)) + + + if plot_type == "Matplotlib": + fig = plt.figure() + plt.plot(df['day'], df[countries].to_numpy()) + plt.title("Outbreak in " + month) + plt.ylabel("Cases") + plt.xlabel("Days since Day 0") + plt.legend(countries) + return fig + elif plot_type == "Plotly": + fig = px.line(df, x='day', y=countries) + fig.update_layout(title="Outbreak in " + month, + xaxis_title="Cases", + yaxis_title="Days Since Day 0") + return fig + else: + source = ColumnDataSource(df) + p = bk.figure(title="Outbreak in " + month, x_axis_label="Cases", y_axis_label="Days Since Day 0") + for country in countries: + p.line(x='day', y=country, line_width=2, source=source) + item_text = json_item(p, "plotDiv") + return item_text + +inputs = [ + gr.Dropdown(["Matplotlib", "Plotly", "Bokeh"], label="Plot Type"), + gr.Slider(1, 4, 3.2, label="R"), + gr.Dropdown(["January", "February", "March", "April", "May"], label="Month"), + gr.CheckboxGroup(["USA", "Canada", "Mexico", "UK"], label="Countries", + value=["USA", "Canada"]), + gr.Checkbox(label="Social Distancing?"), + ] +outputs = gr.Plot() + +demo = gr.Interface(fn=outbreak, inputs=inputs, outputs=outputs, examples=[ + ["Matplotlib", 2, "March", ["Mexico", "UK"], True], + ["Plotly", 3.6, "February", ["Canada", "Mexico", "UK"], False], + ["Bokeh", 1.2, "May", ["UK"], True] + ], cache_examples=True) + + +if __name__ == "__main__": + demo.launch() diff --git a/demos/question_answer/files/bert.py b/demos/question_answer/files/bert.py new file mode 100644 index 0000000000000000000000000000000000000000..d76611575954b4af5e321e1b7d860711f001a1b3 --- /dev/null +++ b/demos/question_answer/files/bert.py @@ -0,0 +1,105 @@ +from __future__ import absolute_import, division, print_function + +import collections +import logging +import math + +import numpy as np +import torch +from pytorch_transformers import ( + WEIGHTS_NAME, + BertConfig, + BertForQuestionAnswering, + BertTokenizer, +) +from torch.utils.data import DataLoader, SequentialSampler, TensorDataset +from utils import ( + get_answer, + input_to_squad_example, + squad_examples_to_features, + to_list, +) + +RawResult = collections.namedtuple( + "RawResult", ["unique_id", "start_logits", "end_logits"] +) + + +class QA: + def __init__(self, model_path: str): + self.max_seq_length = 384 + self.doc_stride = 128 + self.do_lower_case = True + self.max_query_length = 64 + self.n_best_size = 20 + self.max_answer_length = 30 + self.model, self.tokenizer = self.load_model(model_path) + if torch.cuda.is_available(): + self.device = "cuda" + else: + self.device = "cpu" + self.model.to(self.device) + self.model.eval() + + def load_model(self, model_path: str, do_lower_case=False): + config = BertConfig.from_pretrained(model_path + "/bert_config.json") + tokenizer = BertTokenizer.from_pretrained( + model_path, do_lower_case=do_lower_case + ) + model = BertForQuestionAnswering.from_pretrained( + model_path, from_tf=False, config=config + ) + return model, tokenizer + + def predict(self, passage: str, question: str): + example = input_to_squad_example(passage, question) + features = squad_examples_to_features( + example, + self.tokenizer, + self.max_seq_length, + self.doc_stride, + self.max_query_length, + ) + all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) + all_input_mask = torch.tensor( + [f.input_mask for f in features], dtype=torch.long + ) + all_segment_ids = torch.tensor( + [f.segment_ids for f in features], dtype=torch.long + ) + all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long) + dataset = TensorDataset( + all_input_ids, all_input_mask, all_segment_ids, all_example_index + ) + eval_sampler = SequentialSampler(dataset) + eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=1) + all_results = [] + for batch in eval_dataloader: + batch = tuple(t.to(self.device) for t in batch) + with torch.no_grad(): + inputs = { + "input_ids": batch[0], + "attention_mask": batch[1], + "token_type_ids": batch[2], + } + example_indices = batch[3] + outputs = self.model(**inputs) + + for i, example_index in enumerate(example_indices): + eval_feature = features[example_index.item()] + unique_id = int(eval_feature.unique_id) + result = RawResult( + unique_id=unique_id, + start_logits=to_list(outputs[0][i]), + end_logits=to_list(outputs[1][i]), + ) + all_results.append(result) + answer = get_answer( + example, + features, + all_results, + self.n_best_size, + self.max_answer_length, + self.do_lower_case, + ) + return answer diff --git a/demos/question_answer/files/utils.py b/demos/question_answer/files/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..297afcc0caf81b14776ee182d4d289c7ed3eb632 --- /dev/null +++ b/demos/question_answer/files/utils.py @@ -0,0 +1,563 @@ +from __future__ import absolute_import, division, print_function + +import collections +import math + +import numpy as np +import torch +from pytorch_transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize +from torch.utils.data import DataLoader, SequentialSampler, TensorDataset + + +class SquadExample(object): + """ + A single training/test example for the Squad dataset. + For examples without an answer, the start and end position are -1. + """ + + def __init__( + self, + qas_id, + question_text, + doc_tokens, + orig_answer_text=None, + start_position=None, + end_position=None, + ): + self.qas_id = qas_id + self.question_text = question_text + self.doc_tokens = doc_tokens + self.orig_answer_text = orig_answer_text + self.start_position = start_position + self.end_position = end_position + + def __str__(self): + return self.__repr__() + + def __repr__(self): + s = "" + s += "qas_id: %s" % (self.qas_id) + s += ", question_text: %s" % (self.question_text) + s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens)) + if self.start_position: + s += ", start_position: %d" % (self.start_position) + if self.end_position: + s += ", end_position: %d" % (self.end_position) + return s + + +class InputFeatures(object): + """A single set of features of data.""" + + def __init__( + self, + unique_id, + example_index, + doc_span_index, + tokens, + token_to_orig_map, + token_is_max_context, + input_ids, + input_mask, + segment_ids, + paragraph_len, + start_position=None, + end_position=None, + ): + self.unique_id = unique_id + self.example_index = example_index + self.doc_span_index = doc_span_index + self.tokens = tokens + self.token_to_orig_map = token_to_orig_map + self.token_is_max_context = token_is_max_context + self.input_ids = input_ids + self.input_mask = input_mask + self.segment_ids = segment_ids + self.paragraph_len = paragraph_len + self.start_position = start_position + self.end_position = end_position + + +def input_to_squad_example(passage, question): + """Convert input passage and question into a SquadExample.""" + + def is_whitespace(c): + if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F: + return True + return False + + paragraph_text = passage + doc_tokens = [] + char_to_word_offset = [] + prev_is_whitespace = True + for c in paragraph_text: + if is_whitespace(c): + prev_is_whitespace = True + else: + if prev_is_whitespace: + doc_tokens.append(c) + else: + doc_tokens[-1] += c + prev_is_whitespace = False + char_to_word_offset.append(len(doc_tokens) - 1) + + qas_id = 0 + question_text = question + start_position = None + end_position = None + orig_answer_text = None + + example = SquadExample( + qas_id=qas_id, + question_text=question_text, + doc_tokens=doc_tokens, + orig_answer_text=orig_answer_text, + start_position=start_position, + end_position=end_position, + ) + + return example + + +def _check_is_max_context(doc_spans, cur_span_index, position): + """Check if this is the 'max context' doc span for the token.""" + + # Because of the sliding window approach taken to scoring documents, a single + # token can appear in multiple documents. E.g. + # Doc: the man went to the store and bought a gallon of milk + # Span A: the man went to the + # Span B: to the store and bought + # Span C: and bought a gallon of + # ... + # + # Now the word 'bought' will have two scores from spans B and C. We only + # want to consider the score with "maximum context", which we define as + # the *minimum* of its left and right context (the *sum* of left and + # right context will always be the same, of course). + # + # In the example the maximum context for 'bought' would be span C since + # it has 1 left context and 3 right context, while span B has 4 left context + # and 0 right context. + best_score = None + best_span_index = None + for (span_index, doc_span) in enumerate(doc_spans): + end = doc_span.start + doc_span.length - 1 + if position < doc_span.start: + continue + if position > end: + continue + num_left_context = position - doc_span.start + num_right_context = end - position + score = min(num_left_context, num_right_context) + 0.01 * doc_span.length + if best_score is None or score > best_score: + best_score = score + best_span_index = span_index + + return cur_span_index == best_span_index + + +def squad_examples_to_features( + example, + tokenizer, + max_seq_length, + doc_stride, + max_query_length, + cls_token_at_end=False, + cls_token="[CLS]", + sep_token="[SEP]", + pad_token=0, + sequence_a_segment_id=0, + sequence_b_segment_id=1, + cls_token_segment_id=0, + pad_token_segment_id=0, + mask_padding_with_zero=True, +): + """Loads a data file into a list of `InputBatch`s.""" + + unique_id = 1000000000 + # cnt_pos, cnt_neg = 0, 0 + # max_N, max_M = 1024, 1024 + # f = np.zeros((max_N, max_M), dtype=np.float32) + example_index = 0 + features = [] + # if example_index % 100 == 0: + # logger.info('Converting %s/%s pos %s neg %s', example_index, len(examples), cnt_pos, cnt_neg) + + query_tokens = tokenizer.tokenize(example.question_text) + + if len(query_tokens) > max_query_length: + query_tokens = query_tokens[0:max_query_length] + + tok_to_orig_index = [] + orig_to_tok_index = [] + all_doc_tokens = [] + for (i, token) in enumerate(example.doc_tokens): + orig_to_tok_index.append(len(all_doc_tokens)) + sub_tokens = tokenizer.tokenize(token) + for sub_token in sub_tokens: + tok_to_orig_index.append(i) + all_doc_tokens.append(sub_token) + + # The -3 accounts for [CLS], [SEP] and [SEP] + max_tokens_for_doc = max_seq_length - len(query_tokens) - 3 + + # We can have documents that are longer than the maximum sequence length. + # To deal with this we do a sliding window approach, where we take chunks + # of the up to our max length with a stride of `doc_stride`. + _DocSpan = collections.namedtuple( # pylint: disable=invalid-name + "DocSpan", ["start", "length"] + ) + doc_spans = [] + start_offset = 0 + while start_offset < len(all_doc_tokens): + length = len(all_doc_tokens) - start_offset + if length > max_tokens_for_doc: + length = max_tokens_for_doc + doc_spans.append(_DocSpan(start=start_offset, length=length)) + if start_offset + length == len(all_doc_tokens): + break + start_offset += min(length, doc_stride) + + for (doc_span_index, doc_span) in enumerate(doc_spans): + tokens = [] + token_to_orig_map = {} + token_is_max_context = {} + segment_ids = [] + + # CLS token at the beginning + if not cls_token_at_end: + tokens.append(cls_token) + segment_ids.append(cls_token_segment_id) + + # Query + for token in query_tokens: + tokens.append(token) + segment_ids.append(sequence_a_segment_id) + + # SEP token + tokens.append(sep_token) + segment_ids.append(sequence_a_segment_id) + + # Paragraph + for i in range(doc_span.length): + split_token_index = doc_span.start + i + token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index] + + is_max_context = _check_is_max_context( + doc_spans, doc_span_index, split_token_index + ) + token_is_max_context[len(tokens)] = is_max_context + tokens.append(all_doc_tokens[split_token_index]) + segment_ids.append(sequence_b_segment_id) + paragraph_len = doc_span.length + + # SEP token + tokens.append(sep_token) + segment_ids.append(sequence_b_segment_id) + + # CLS token at the end + if cls_token_at_end: + tokens.append(cls_token) + segment_ids.append(cls_token_segment_id) + + input_ids = tokenizer.convert_tokens_to_ids(tokens) + + # The mask has 1 for real tokens and 0 for padding tokens. Only real + # tokens are attended to. + input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids) + + # Zero-pad up to the sequence length. + while len(input_ids) < max_seq_length: + input_ids.append(pad_token) + input_mask.append(0 if mask_padding_with_zero else 1) + segment_ids.append(pad_token_segment_id) + + assert len(input_ids) == max_seq_length + assert len(input_mask) == max_seq_length + assert len(segment_ids) == max_seq_length + + start_position = None + end_position = None + + features.append( + InputFeatures( + unique_id=unique_id, + example_index=example_index, + doc_span_index=doc_span_index, + tokens=tokens, + token_to_orig_map=token_to_orig_map, + token_is_max_context=token_is_max_context, + input_ids=input_ids, + input_mask=input_mask, + segment_ids=segment_ids, + paragraph_len=paragraph_len, + start_position=start_position, + end_position=end_position, + ) + ) + unique_id += 1 + + return features + + +def to_list(tensor): + return tensor.detach().cpu().tolist() + + +def _get_best_indexes(logits, n_best_size): + """Get the n-best logits from a list.""" + index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True) + + best_indexes = [] + for i in range(len(index_and_score)): + if i >= n_best_size: + break + best_indexes.append(index_and_score[i][0]) + return best_indexes + + +RawResult = collections.namedtuple( + "RawResult", ["unique_id", "start_logits", "end_logits"] +) + + +def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False): + """Project the tokenized prediction back to the original text.""" + + # When we created the data, we kept track of the alignment between original + # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So + # now `orig_text` contains the span of our original text corresponding to the + # span that we predicted. + # + # However, `orig_text` may contain extra characters that we don't want in + # our prediction. + # + # For example, let's say: + # pred_text = steve smith + # orig_text = Steve Smith's + # + # We don't want to return `orig_text` because it contains the extra "'s". + # + # We don't want to return `pred_text` because it's already been normalized + # (the SQuAD eval script also does punctuation stripping/lower casing but + # our tokenizer does additional normalization like stripping accent + # characters). + # + # What we really want to return is "Steve Smith". + # + # Therefore, we have to apply a semi-complicated alignment heuristic between + # `pred_text` and `orig_text` to get a character-to-character alignment. This + # can fail in certain cases in which case we just return `orig_text`. + + def _strip_spaces(text): + ns_chars = [] + ns_to_s_map = collections.OrderedDict() + for (i, c) in enumerate(text): + if c == " ": + continue + ns_to_s_map[len(ns_chars)] = i + ns_chars.append(c) + ns_text = "".join(ns_chars) + return (ns_text, ns_to_s_map) + + # We first tokenize `orig_text`, strip whitespace from the result + # and `pred_text`, and check if they are the same length. If they are + # NOT the same length, the heuristic has failed. If they are the same + # length, we assume the characters are one-to-one aligned. + tokenizer = BasicTokenizer(do_lower_case=do_lower_case) + + tok_text = " ".join(tokenizer.tokenize(orig_text)) + + start_position = tok_text.find(pred_text) + if start_position == -1: + return orig_text + end_position = start_position + len(pred_text) - 1 + + (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text) + (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text) + + if len(orig_ns_text) != len(tok_ns_text): + return orig_text + + # We then project the characters in `pred_text` back to `orig_text` using + # the character-to-character alignment. + tok_s_to_ns_map = {} + for (i, tok_index) in tok_ns_to_s_map.items(): + tok_s_to_ns_map[tok_index] = i + + orig_start_position = None + if start_position in tok_s_to_ns_map: + ns_start_position = tok_s_to_ns_map[start_position] + if ns_start_position in orig_ns_to_s_map: + orig_start_position = orig_ns_to_s_map[ns_start_position] + + if orig_start_position is None: + return orig_text + + orig_end_position = None + if end_position in tok_s_to_ns_map: + ns_end_position = tok_s_to_ns_map[end_position] + if ns_end_position in orig_ns_to_s_map: + orig_end_position = orig_ns_to_s_map[ns_end_position] + + if orig_end_position is None: + return orig_text + + output_text = orig_text[orig_start_position : (orig_end_position + 1)] + return output_text + + +def _compute_softmax(scores): + """Compute softmax probability over raw logits.""" + if not scores: + return [] + + max_score = None + for score in scores: + if max_score is None or score > max_score: + max_score = score + + exp_scores = [] + total_sum = 0.0 + for score in scores: + x = math.exp(score - max_score) + exp_scores.append(x) + total_sum += x + + probs = [] + for score in exp_scores: + probs.append(score / total_sum) + return probs + + +def get_answer( + example, features, all_results, n_best_size, max_answer_length, do_lower_case +): + example_index_to_features = collections.defaultdict(list) + for feature in features: + example_index_to_features[feature.example_index].append(feature) + + unique_id_to_result = {} + for result in all_results: + unique_id_to_result[result.unique_id] = result + + _PrelimPrediction = collections.namedtuple( + "PrelimPrediction", + ["feature_index", "start_index", "end_index", "start_logit", "end_logit"], + ) + + example_index = 0 + features = example_index_to_features[example_index] + + prelim_predictions = [] + + for (feature_index, feature) in enumerate(features): + result = unique_id_to_result[feature.unique_id] + start_indexes = _get_best_indexes(result.start_logits, n_best_size) + end_indexes = _get_best_indexes(result.end_logits, n_best_size) + for start_index in start_indexes: + for end_index in end_indexes: + # We could hypothetically create invalid predictions, e.g., predict + # that the start of the span is in the question. We throw out all + # invalid predictions. + if start_index >= len(feature.tokens): + continue + if end_index >= len(feature.tokens): + continue + if start_index not in feature.token_to_orig_map: + continue + if end_index not in feature.token_to_orig_map: + continue + if not feature.token_is_max_context.get(start_index, False): + continue + if end_index < start_index: + continue + length = end_index - start_index + 1 + if length > max_answer_length: + continue + prelim_predictions.append( + _PrelimPrediction( + feature_index=feature_index, + start_index=start_index, + end_index=end_index, + start_logit=result.start_logits[start_index], + end_logit=result.end_logits[end_index], + ) + ) + prelim_predictions = sorted( + prelim_predictions, key=lambda x: (x.start_logit + x.end_logit), reverse=True + ) + _NbestPrediction = collections.namedtuple( + "NbestPrediction", + ["text", "start_logit", "end_logit", "start_index", "end_index"], + ) + seen_predictions = {} + nbest = [] + for pred in prelim_predictions: + if len(nbest) >= n_best_size: + break + feature = features[pred.feature_index] + orig_doc_start = -1 + orig_doc_end = -1 + if pred.start_index > 0: # this is a non-null prediction + tok_tokens = feature.tokens[pred.start_index : (pred.end_index + 1)] + orig_doc_start = feature.token_to_orig_map[pred.start_index] + orig_doc_end = feature.token_to_orig_map[pred.end_index] + orig_tokens = example.doc_tokens[orig_doc_start : (orig_doc_end + 1)] + tok_text = " ".join(tok_tokens) + + # De-tokenize WordPieces that have been split off. + tok_text = tok_text.replace(" ##", "") + tok_text = tok_text.replace("##", "") + + # Clean whitespace + tok_text = tok_text.strip() + tok_text = " ".join(tok_text.split()) + orig_text = " ".join(orig_tokens) + + final_text = get_final_text(tok_text, orig_text, do_lower_case) + if final_text in seen_predictions: + continue + + seen_predictions[final_text] = True + else: + final_text = "" + seen_predictions[final_text] = True + + nbest.append( + _NbestPrediction( + text=final_text, + start_logit=pred.start_logit, + end_logit=pred.end_logit, + start_index=orig_doc_start, + end_index=orig_doc_end, + ) + ) + + if not nbest: + nbest.append( + _NbestPrediction( + text="empty", + start_logit=0.0, + end_logit=0.0, + start_index=-1, + end_index=-1, + ) + ) + + assert len(nbest) >= 1 + + total_scores = [] + for entry in nbest: + total_scores.append(entry.start_logit + entry.end_logit) + + probs = _compute_softmax(total_scores) + + answer = { + "answer": nbest[0].text, + "start": nbest[0].start_index, + "end": nbest[0].end_index, + "confidence": probs[0], + "document": example.doc_tokens, + } + return answer diff --git a/demos/question_answer/requirements.txt b/demos/question_answer/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..c91c8d8f3222aaffeb4cc14154c0db3720de2e4f --- /dev/null +++ b/demos/question_answer/requirements.txt @@ -0,0 +1 @@ +pytorch-transformers==1.0.0 diff --git a/demos/question_answer/run.py b/demos/question_answer/run.py new file mode 100644 index 0000000000000000000000000000000000000000..bf6b06cb658ae956a37df2027eed074fbfed7a47 --- /dev/null +++ b/demos/question_answer/run.py @@ -0,0 +1,27 @@ +import gradio as gr + +examples = [ + [ + "The Amazon rainforest is a moist broadleaf forest that covers most of the Amazon basin of South America", + "Which continent is the Amazon rainforest in?", + ] +] + +demo = gr.Interface.load( + "huggingface/deepset/roberta-base-squad2", + inputs=[ + gr.Textbox( + lines=5, label="Context", placeholder="Type a sentence or paragraph here." + ), + gr.Textbox( + lines=2, + label="Question", + placeholder="Ask a question based on the context.", + ), + ], + outputs=[gr.Textbox(label="Answer"), gr.Label(label="Probability")], + examples=examples, +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/reverse_audio/audio/cantina.wav b/demos/reverse_audio/audio/cantina.wav new file mode 100644 index 0000000000000000000000000000000000000000..83651968c382d3c17ad48d84995c9b71753ba694 --- /dev/null +++ b/demos/reverse_audio/audio/cantina.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e5f73001b324e413bdcf658fca5485057c333f4198e51e7e86bb2e772cd0973 +size 132344 diff --git a/demos/reverse_audio/audio/recording1.wav b/demos/reverse_audio/audio/recording1.wav new file mode 100644 index 0000000000000000000000000000000000000000..305c419a090c2c195531467ecd8a8704438fe9c8 --- /dev/null +++ b/demos/reverse_audio/audio/recording1.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1d087876795ff2bc8b6e1a872eb9a9b2cca44db1866e9c9fa00df5a2556919ff +size 639020 diff --git a/demos/reverse_audio/run.py b/demos/reverse_audio/run.py new file mode 100644 index 0000000000000000000000000000000000000000..f58e82f855e2a39cb38f37870e5322e2bcea1363 --- /dev/null +++ b/demos/reverse_audio/run.py @@ -0,0 +1,22 @@ +import os + +import numpy as np + +import gradio as gr + + +def reverse_audio(audio): + sr, data = audio + return (sr, np.flipud(data)) + + +demo = gr.Interface(fn=reverse_audio, + inputs="microphone", + outputs="audio", + examples=[ + os.path.join(os.path.dirname(__file__), "audio/cantina.wav"), + os.path.join(os.path.dirname(__file__), "audio/recording1.wav") + ], cache_examples=True) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/reverse_audio/screenshot.png b/demos/reverse_audio/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..e3dff47937e2698ba4b3dc393bb1f8b900eca60d --- /dev/null +++ b/demos/reverse_audio/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dc9ff858f10cc46c59cebff375b36a46c035ab9b60fd2a428c545b5d76583b56 +size 18387 diff --git a/demos/reversible_flow/run.py b/demos/reversible_flow/run.py new file mode 100644 index 0000000000000000000000000000000000000000..c73fce91eed02e082dbb5b4a38348c19c0f769ac --- /dev/null +++ b/demos/reversible_flow/run.py @@ -0,0 +1,15 @@ +import gradio as gr + +def increase(num): + return num + 1 + +with gr.Blocks() as demo: + a = gr.Number(label="a") + b = gr.Number(label="b") + btoa = gr.Button("a > b") + atob = gr.Button("b > a") + atob.click(increase, a, b) + btoa.click(increase, b, a) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/rows_and_columns/images/cheetah.jpg b/demos/rows_and_columns/images/cheetah.jpg new file mode 100644 index 0000000000000000000000000000000000000000..66d3b48fd19cd8cd8d8437b6f33183b3d3d42589 --- /dev/null +++ b/demos/rows_and_columns/images/cheetah.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:35550bfbba996e59c242af00f6a14a9c0d055dfbc52ad069a1a4e8c1c39ca095 +size 20552 diff --git a/demos/rows_and_columns/run.py b/demos/rows_and_columns/run.py new file mode 100644 index 0000000000000000000000000000000000000000..a8403a51051c894bc5334adc2cc96802af97d8ff --- /dev/null +++ b/demos/rows_and_columns/run.py @@ -0,0 +1,12 @@ +import gradio as gr + +with gr.Blocks() as demo: + with gr.Row(): + with gr.Column(): + text1 = gr.Textbox(label="prompt 1") + text2 = gr.Textbox(label="prompt 2") + with gr.Column(): + img1 = gr.Image("images/cheetah.jpg") + btn = gr.Button("Go").style(full_width=True) +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/sales_projections/requirements.txt b/demos/sales_projections/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..44974cf43c7d87ca1f9cc74aba8d87b4dd23e9b8 --- /dev/null +++ b/demos/sales_projections/requirements.txt @@ -0,0 +1,3 @@ +pandas +numpy +matplotlib \ No newline at end of file diff --git a/demos/sales_projections/run.py b/demos/sales_projections/run.py new file mode 100644 index 0000000000000000000000000000000000000000..4e07c8235a6bf4ecba1273cd28df5ea2ae545f2c --- /dev/null +++ b/demos/sales_projections/run.py @@ -0,0 +1,38 @@ +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +import numpy as np + +import gradio as gr + + +def sales_projections(employee_data): + sales_data = employee_data.iloc[:, 1:4].astype("int").to_numpy() + regression_values = np.apply_along_axis( + lambda row: np.array(np.poly1d(np.polyfit([0, 1, 2], row, 2))), 0, sales_data + ) + projected_months = np.repeat( + np.expand_dims(np.arange(3, 12), 0), len(sales_data), axis=0 + ) + projected_values = np.array( + [ + month * month * regression[0] + month * regression[1] + regression[2] + for month, regression in zip(projected_months, regression_values) + ] + ) + plt.plot(projected_values.T) + plt.legend(employee_data["Name"]) + return employee_data, plt.gcf(), regression_values + + +demo = gr.Interface( + sales_projections, + gr.Dataframe( + headers=["Name", "Jan Sales", "Feb Sales", "Mar Sales"], + value=[["Jon", 12, 14, 18], ["Alice", 14, 17, 2], ["Sana", 8, 9.5, 12]], + ), + ["dataframe", "plot", "numpy"], + description="Enter sales figures for employees to predict sales trajectory over year.", +) +if __name__ == "__main__": + demo.launch() diff --git a/demos/sales_projections/screenshot.gif b/demos/sales_projections/screenshot.gif new file mode 100644 index 0000000000000000000000000000000000000000..2460d235a20e02d6a46e00903f330c80ee873334 --- /dev/null +++ b/demos/sales_projections/screenshot.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bf5c0ef79446b0cb270d556225ef8866084226bdf79c888f359183c2a19e7245 +size 5872200 diff --git a/demos/score_tracker/run.py b/demos/score_tracker/run.py new file mode 100644 index 0000000000000000000000000000000000000000..b024faf61739119dbcad381e8456ba4cfb97d82d --- /dev/null +++ b/demos/score_tracker/run.py @@ -0,0 +1,16 @@ +import gradio as gr + +scores = [] + +def track_score(score): + scores.append(score) + top_scores = sorted(scores, reverse=True)[:3] + return top_scores + +demo = gr.Interface( + track_score, + gr.Number(label="Score"), + gr.JSON(label="Top Scores") +) +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/sentence_builder/run.py b/demos/sentence_builder/run.py new file mode 100644 index 0000000000000000000000000000000000000000..2b1385deca619dcffcaa002aa985465cca6fc7c6 --- /dev/null +++ b/demos/sentence_builder/run.py @@ -0,0 +1,27 @@ +import gradio as gr + + +def sentence_builder(quantity, animal, place, activity_list, morning): + return f"""The {quantity} {animal}s went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}""" + + +demo = gr.Interface( + sentence_builder, + [ + gr.Slider(2, 20, value=4), + gr.Dropdown(["cat", "dog", "bird"]), + gr.Radio(["park", "zoo", "road"]), + gr.CheckboxGroup(["ran", "swam", "ate", "slept"]), + gr.Checkbox(label="Is it the morning?"), + ], + "text", + examples=[ + [2, "cat", "park", ["ran", "swam"], True], + [4, "dog", "zoo", ["ate", "swam"], False], + [10, "bird", "road", ["ran"], False], + [8, "cat", "zoo", ["ate"], True], + ], +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/sentence_builder/screenshot.png b/demos/sentence_builder/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..8bddd3a47e9208d67e21d3cad17db93e2c342149 --- /dev/null +++ b/demos/sentence_builder/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ba7df068296826ffc11d3b0b6f59aa2246de70893724f4d5cd8a1d9f19ea67bb +size 50450 diff --git a/demos/sentiment_analysis/requirements.txt b/demos/sentiment_analysis/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..6fa2de44417ce85838334618e6430c5e1abc6a5d --- /dev/null +++ b/demos/sentiment_analysis/requirements.txt @@ -0,0 +1 @@ +nltk \ No newline at end of file diff --git a/demos/sentiment_analysis/run.py b/demos/sentiment_analysis/run.py new file mode 100644 index 0000000000000000000000000000000000000000..35f6a292d93026e6b5e73b7649902ea7d3121359 --- /dev/null +++ b/demos/sentiment_analysis/run.py @@ -0,0 +1,23 @@ +import nltk +from nltk.sentiment.vader import SentimentIntensityAnalyzer + +import gradio as gr + +nltk.download("vader_lexicon") +sid = SentimentIntensityAnalyzer() + + +def sentiment_analysis(text): + scores = sid.polarity_scores(text) + del scores["compound"] + return scores + + +demo = gr.Interface( + sentiment_analysis, + gr.Textbox(placeholder="Enter a positive or negative sentence here..."), + "label", + interpretation="default") + +if __name__ == "__main__": + demo.launch() diff --git a/demos/sepia_filter/run.py b/demos/sepia_filter/run.py new file mode 100644 index 0000000000000000000000000000000000000000..b48eb6a5e10eae25b25b7ee49364810b54aaf88c --- /dev/null +++ b/demos/sepia_filter/run.py @@ -0,0 +1,16 @@ +import numpy as np +import gradio as gr + +def sepia(input_img): + sepia_filter = np.array([ + [0.393, 0.769, 0.189], + [0.349, 0.686, 0.168], + [0.272, 0.534, 0.131] + ]) + sepia_img = input_img.dot(sepia_filter.T) + sepia_img /= sepia_img.max() + return sepia_img + +demo = gr.Interface(sepia, gr.Image(shape=(200, 200)), "image") +if __name__ == "__main__": + demo.launch() diff --git a/demos/sepia_filter/screenshot.gif b/demos/sepia_filter/screenshot.gif new file mode 100644 index 0000000000000000000000000000000000000000..6169b786aa203bcfab5e8b0da023ff646716a084 --- /dev/null +++ b/demos/sepia_filter/screenshot.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9e7091eff30c08a023ba18c44181e743637afe20b9e2111ba0b657c1c2817648 +size 3726270 diff --git a/demos/spectogram/requirements.txt b/demos/spectogram/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..ca6034584e0ec2dde9b16b581bab6d6a8327a59a --- /dev/null +++ b/demos/spectogram/requirements.txt @@ -0,0 +1,3 @@ +scipy +numpy +matplotlib \ No newline at end of file diff --git a/demos/spectogram/run.py b/demos/spectogram/run.py new file mode 100644 index 0000000000000000000000000000000000000000..599fba4cc2f395acfa1cf47888ffb3443a6176df --- /dev/null +++ b/demos/spectogram/run.py @@ -0,0 +1,22 @@ +import matplotlib.pyplot as plt +import numpy as np +from scipy import signal + +import gradio as gr + + +def spectrogram(audio): + sr, data = audio + if len(data.shape) == 2: + data = np.mean(data, axis=0) + frequencies, times, spectrogram_data = signal.spectrogram( + data, sr, window="hamming" + ) + plt.pcolormesh(times, frequencies, np.log10(spectrogram_data)) + return plt + + +demo = gr.Interface(spectrogram, "audio", "plot") + +if __name__ == "__main__": + demo.launch() diff --git a/demos/spectogram/screenshot.png b/demos/spectogram/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..ace2078e536eb4d2ce3e69bf80dde1a88867a9a9 --- /dev/null +++ b/demos/spectogram/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:76bce2db2b53227282774fff1bb429e9791a533d2ea83f458b5839fd3b374cd3 +size 309199 diff --git a/demos/sst_or_tts/run.py b/demos/sst_or_tts/run.py new file mode 100644 index 0000000000000000000000000000000000000000..55f86ce01829c1440cd9a3b6f855e9fd227d4314 --- /dev/null +++ b/demos/sst_or_tts/run.py @@ -0,0 +1,27 @@ +import gradio as gr + +title = "GPT-J-6B" + +tts_examples = [ + "I love learning machine learning", + "How do you do?", +] + +tts_demo = gr.Interface.load( + "huggingface/facebook/fastspeech2-en-ljspeech", + title=None, + examples=tts_examples, + description="Give me something to say!", +) + +stt_demo = gr.Interface.load( + "huggingface/facebook/wav2vec2-base-960h", + title=None, + inputs="mic", + description="Let me try to guess what you're saying!", +) + +demo = gr.TabbedInterface([tts_demo, stt_demo], ["Text-to-speech", "Speech-to-text"]) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/stock_forecast/requirements.txt b/demos/stock_forecast/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..806f22116faa6610345f0899927f952ae5dfedcd --- /dev/null +++ b/demos/stock_forecast/requirements.txt @@ -0,0 +1,2 @@ +numpy +matplotlib \ No newline at end of file diff --git a/demos/stock_forecast/run.py b/demos/stock_forecast/run.py new file mode 100644 index 0000000000000000000000000000000000000000..e6758abcfd5067688cb3eb50d2a5e032588bb5e3 --- /dev/null +++ b/demos/stock_forecast/run.py @@ -0,0 +1,39 @@ +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +import numpy as np + +import gradio as gr + + +def plot_forecast(final_year, companies, noise, show_legend, point_style): + start_year = 2020 + x = np.arange(start_year, final_year + 1) + year_count = x.shape[0] + plt_format = ({"cross": "X", "line": "-", "circle": "o--"})[point_style] + fig = plt.figure() + ax = fig.add_subplot(111) + for i, company in enumerate(companies): + series = np.arange(0, year_count, dtype=float) + series = series**2 * (i + 1) + series += np.random.rand(year_count) * noise + ax.plot(x, series, plt_format) + if show_legend: + plt.legend(companies) + return fig + + +demo = gr.Interface( + plot_forecast, + [ + gr.Radio([2025, 2030, 2035, 2040], label="Project to:"), + gr.CheckboxGroup(["Google", "Microsoft", "Gradio"], label="Company Selection"), + gr.Slider(1, 100, label="Noise Level"), + gr.Checkbox(label="Show Legend"), + gr.Dropdown(["cross", "line", "circle"], label="Style"), + ], + gr.Plot(label="forecast"), +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/stock_forecast/screenshot.png b/demos/stock_forecast/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..242a60b242bc89d6f71175bfd091553103c8a2ed --- /dev/null +++ b/demos/stock_forecast/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:229b2943fef0d16a69a5359709c48c480f9a699d426d20a1f7500c095b024e80 +size 116350 diff --git a/demos/stream_audio/run.py b/demos/stream_audio/run.py new file mode 100644 index 0000000000000000000000000000000000000000..8fcd3c2affc12c6e60c58ac3a9ea56c70c243f3a --- /dev/null +++ b/demos/stream_audio/run.py @@ -0,0 +1,20 @@ +import gradio as gr +import numpy as np + +with gr.Blocks() as demo: + inp = gr.Audio(source="microphone") + out = gr.Audio() + stream = gr.Variable() + + def add_to_stream(audio, instream): + if audio is None: + return gr.update(), instream + if instream is None: + ret = audio + else: + ret = (audio[0], np.concatenate((instream[1], audio[1]))) + return ret, ret + inp.stream(add_to_stream, [inp, stream], [out, stream]) + +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/stream_frames/run.py b/demos/stream_frames/run.py new file mode 100644 index 0000000000000000000000000000000000000000..f6120d81dd92961e120fb933a1195386fbe04a9c --- /dev/null +++ b/demos/stream_frames/run.py @@ -0,0 +1,14 @@ +import gradio as gr +import numpy as np + +def flip(im): + return np.flipud(im) + +demo = gr.Interface( + flip, + gr.Image(source="webcam", streaming=True), + "image", + live=True +) +if __name__ == "__main__": + demo.launch() \ No newline at end of file diff --git a/demos/streaming_stt/.gitignore b/demos/streaming_stt/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..4a7244336b108f13599567a1d36d54c04f02c488 --- /dev/null +++ b/demos/streaming_stt/.gitignore @@ -0,0 +1,2 @@ +*.pbmm +*.scorer \ No newline at end of file diff --git a/demos/streaming_stt/requirements.txt b/demos/streaming_stt/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..404e2017f909f5552e880697eae0458ec6d912bf --- /dev/null +++ b/demos/streaming_stt/requirements.txt @@ -0,0 +1 @@ +deepspeech==0.9.3 \ No newline at end of file diff --git a/demos/streaming_stt/run.py b/demos/streaming_stt/run.py new file mode 100644 index 0000000000000000000000000000000000000000..ec68e6b1d69cbc5c4f9782df64141c5074dceca1 --- /dev/null +++ b/demos/streaming_stt/run.py @@ -0,0 +1,57 @@ +from deepspeech import Model +import gradio as gr +import numpy as np +import urllib.request + +model_file_path = "deepspeech-0.9.3-models.pbmm" +lm_file_path = "deepspeech-0.9.3-models.scorer" +url = "https://github.com/mozilla/DeepSpeech/releases/download/v0.9.3/" + +urllib.request.urlretrieve(url + model_file_path, filename=model_file_path) +urllib.request.urlretrieve(url + lm_file_path, filename=lm_file_path) + +beam_width = 100 +lm_alpha = 0.93 +lm_beta = 1.18 + +model = Model(model_file_path) +model.enableExternalScorer(lm_file_path) +model.setScorerAlphaBeta(lm_alpha, lm_beta) +model.setBeamWidth(beam_width) + + +def reformat_freq(sr, y): + if sr not in ( + 48000, + 16000, + ): # Deepspeech only supports 16k, (we convert 48k -> 16k) + raise ValueError("Unsupported rate", sr) + if sr == 48000: + y = ( + ((y / max(np.max(y), 1)) * 32767) + .reshape((-1, 3)) + .mean(axis=1) + .astype("int16") + ) + sr = 16000 + return sr, y + + +def transcribe(speech, stream): + _, y = reformat_freq(*speech) + if stream is None: + stream = model.createStream() + stream.feedAudioContent(y) + text = stream.intermediateDecode() + return text, stream + + +demo = gr.Interface( + transcribe, + [gr.Audio(source="microphone", streaming=True), "state"], + ["text", "state"], + live=True, +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/streaming_stt/setup.sh b/demos/streaming_stt/setup.sh new file mode 100644 index 0000000000000000000000000000000000000000..bf8358848e843de3d76bb38278ca1fa30c134506 --- /dev/null +++ b/demos/streaming_stt/setup.sh @@ -0,0 +1,3 @@ +wget https://github.com/mozilla/DeepSpeech/releases/download/v0.8.2/deepspeech-0.8.2-models.pbmm +wget https://github.com/mozilla/DeepSpeech/releases/download/v0.8.2/deepspeech-0.8.2-models.scorer +apt install libasound2-dev portaudio19-dev libportaudio2 libportaudiocpp0 ffmpeg \ No newline at end of file diff --git a/demos/streaming_wav2vec/requirements.txt b/demos/streaming_wav2vec/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..19c989b6cd7cc159d040c41989d460c2338d728d --- /dev/null +++ b/demos/streaming_wav2vec/requirements.txt @@ -0,0 +1 @@ +deepspeech==0.8.2 diff --git a/demos/streaming_wav2vec/run.py b/demos/streaming_wav2vec/run.py new file mode 100644 index 0000000000000000000000000000000000000000..e0a576bb21301e7ac9541f18ffe512aa2abd7db3 --- /dev/null +++ b/demos/streaming_wav2vec/run.py @@ -0,0 +1,47 @@ +from deepspeech import Model +import gradio as gr +import scipy.io.wavfile +import numpy as np + +model_file_path = "deepspeech-0.8.2-models.pbmm" +lm_file_path = "deepspeech-0.8.2-models.scorer" +beam_width = 100 +lm_alpha = 0.93 +lm_beta = 1.18 + +model = Model(model_file_path) +model.enableExternalScorer(lm_file_path) +model.setScorerAlphaBeta(lm_alpha, lm_beta) +model.setBeamWidth(beam_width) + + +def reformat_freq(sr, y): + if sr not in ( + 48000, + 16000, + ): # Deepspeech only supports 16k, (we convert 48k -> 16k) + raise ValueError("Unsupported rate", sr) + if sr == 48000: + y = ( + ((y / max(np.max(y), 1)) * 32767) + .reshape((-1, 3)) + .mean(axis=1) + .astype("int16") + ) + sr = 16000 + return sr, y + + +def transcribe(speech, stream): + _, y = reformat_freq(*speech) + if stream is None: + stream = model.createStream() + stream.feedAudioContent(y) + text = stream.intermediateDecode() + return text, stream + +demo = gr.Interface(transcribe, ["microphone", "state"], ["text", "state"], live=True) + +if __name__ == "__main__": + demo.launch() + diff --git a/demos/tax_calculator/run.py b/demos/tax_calculator/run.py new file mode 100644 index 0000000000000000000000000000000000000000..aebb841b0e84d011df42429eee1592b868acd30d --- /dev/null +++ b/demos/tax_calculator/run.py @@ -0,0 +1,41 @@ +import gradio as gr + + +def tax_calculator(income, marital_status, assets): + tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)] + total_deductible = sum(assets["Cost"]) + taxable_income = income - total_deductible + + total_tax = 0 + for bracket, rate in tax_brackets: + if taxable_income > bracket: + total_tax += (taxable_income - bracket) * rate / 100 + + if marital_status == "Married": + total_tax *= 0.75 + elif marital_status == "Divorced": + total_tax *= 0.8 + + return round(total_tax) + + +demo = gr.Interface( + tax_calculator, + [ + "number", + gr.Radio(["Single", "Married", "Divorced"]), + gr.Dataframe( + headers=["Item", "Cost"], + datatype=["str", "number"], + label="Assets Purchased this Year", + ), + ], + "number", + examples=[ + [10000, "Married", [["Suit", 5000], ["Laptop", 800], ["Car", 1800]]], + [80000, "Single", [["Suit", 800], ["Watch", 1800], ["Car", 800]]], + ], +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/tax_calculator/screenshot.png b/demos/tax_calculator/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..279a830eedb7e66b8807d228db0c955100bbd428 --- /dev/null +++ b/demos/tax_calculator/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1e94d1bcfd484211f1ae8338fbd0bb4ff8fd15c52007908305fd78c03ee5ed24 +size 42004 diff --git a/demos/text_analysis/requirements.txt b/demos/text_analysis/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..33ea94c42fe4087ae42c8b2bf66509e23a68e834 --- /dev/null +++ b/demos/text_analysis/requirements.txt @@ -0,0 +1 @@ +spacy \ No newline at end of file diff --git a/demos/text_analysis/run.py b/demos/text_analysis/run.py new file mode 100644 index 0000000000000000000000000000000000000000..7b499a4d6432d84fdb10083b77a5d36645501b98 --- /dev/null +++ b/demos/text_analysis/run.py @@ -0,0 +1,40 @@ +import spacy +from spacy import displacy + +import gradio as gr + +nlp = spacy.load("en_core_web_sm") + + +def text_analysis(text): + doc = nlp(text) + html = displacy.render(doc, style="dep", page=True) + html = ( + "
" + + html + + "
" + ) + pos_count = { + "char_count": len(text), + "token_count": 0, + } + pos_tokens = [] + + for token in doc: + pos_tokens.extend([(token.text, token.pos_), (" ", None)]) + + return pos_tokens, pos_count, html + + +demo = gr.Interface( + text_analysis, + gr.Textbox(placeholder="Enter sentence here..."), + ["highlight", "json", "html"], + examples=[ + ["What a beautiful morning for a walk!"], + ["It was the best of times, it was the worst of times."], + ], +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/text_analysis/screenshot.png b/demos/text_analysis/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..dc34dff7ca322b4860b7db718eef61e75991ef87 --- /dev/null +++ b/demos/text_analysis/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6038770643c1f067e0ddc83ad0d36b69722a4bf6255fb78fe619c267ba88faf7 +size 52263 diff --git a/demos/text_analysis/setup.sh b/demos/text_analysis/setup.sh new file mode 100644 index 0000000000000000000000000000000000000000..ad8bb8ee847c128cbc233e57fa8f1b0d62c84d4e --- /dev/null +++ b/demos/text_analysis/setup.sh @@ -0,0 +1 @@ +python -m spacy download en_core_web_sm \ No newline at end of file diff --git a/demos/titanic_survival/files/titanic.csv b/demos/titanic_survival/files/titanic.csv new file mode 100644 index 0000000000000000000000000000000000000000..63b68ab0ba98c667f515c52f08c0bbd5573d5330 --- /dev/null +++ b/demos/titanic_survival/files/titanic.csv @@ -0,0 +1,892 @@ +PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked +1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S +2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C +3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S +4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S +5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S +6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q +7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S +8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S +9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S +10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C +11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S +12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S +13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S +14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S +15,0,3,"Vestrom, Miss. 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Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S +890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C +891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q diff --git a/demos/titanic_survival/requirements.txt b/demos/titanic_survival/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..2bfe97f7ef9b9d9e5e32aa07ba282bca0c6f5b29 --- /dev/null +++ b/demos/titanic_survival/requirements.txt @@ -0,0 +1,3 @@ +scikit-learn +numpy +pandas \ No newline at end of file diff --git a/demos/titanic_survival/run.py b/demos/titanic_survival/run.py new file mode 100644 index 0000000000000000000000000000000000000000..a9a08da189d311748b472d35672e38aa3c18f1e6 --- /dev/null +++ b/demos/titanic_survival/run.py @@ -0,0 +1,113 @@ +import os + +import pandas as pd +from sklearn.ensemble import RandomForestClassifier +from sklearn.model_selection import train_test_split + +import gradio as gr + +current_dir = os.path.dirname(os.path.realpath(__file__)) +data = pd.read_csv(os.path.join(current_dir, "files/titanic.csv")) + + +def encode_age(df): + df.Age = df.Age.fillna(-0.5) + bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120) + categories = pd.cut(df.Age, bins, labels=False) + df.Age = categories + return df + + +def encode_fare(df): + df.Fare = df.Fare.fillna(-0.5) + bins = (-1, 0, 8, 15, 31, 1000) + categories = pd.cut(df.Fare, bins, labels=False) + df.Fare = categories + return df + + +def encode_df(df): + df = encode_age(df) + df = encode_fare(df) + sex_mapping = {"male": 0, "female": 1} + df = df.replace({"Sex": sex_mapping}) + embark_mapping = {"S": 1, "C": 2, "Q": 3} + df = df.replace({"Embarked": embark_mapping}) + df.Embarked = df.Embarked.fillna(0) + df["Company"] = 0 + df.loc[(df["SibSp"] > 0), "Company"] = 1 + df.loc[(df["Parch"] > 0), "Company"] = 2 + df.loc[(df["SibSp"] > 0) & (df["Parch"] > 0), "Company"] = 3 + df = df[ + [ + "PassengerId", + "Pclass", + "Sex", + "Age", + "Fare", + "Embarked", + "Company", + "Survived", + ] + ] + return df + + +train = encode_df(data) + +X_all = train.drop(["Survived", "PassengerId"], axis=1) +y_all = train["Survived"] + +num_test = 0.20 +X_train, X_test, y_train, y_test = train_test_split( + X_all, y_all, test_size=num_test, random_state=23 +) + +clf = RandomForestClassifier() +clf.fit(X_train, y_train) +predictions = clf.predict(X_test) + + +def predict_survival(passenger_class, is_male, age, company, fare, embark_point): + if passenger_class is None or embark_point is None: + return None + df = pd.DataFrame.from_dict( + { + "Pclass": [passenger_class + 1], + "Sex": [0 if is_male else 1], + "Age": [age], + "Company": [ + (1 if "Sibling" in company else 0) + (2 if "Child" in company else 0) + ], + "Fare": [fare], + "Embarked": [embark_point + 1], + } + ) + df = encode_age(df) + df = encode_fare(df) + pred = clf.predict_proba(df)[0] + return {"Perishes": float(pred[0]), "Survives": float(pred[1])} + + +demo = gr.Interface( + predict_survival, + [ + gr.Dropdown(["first", "second", "third"], type="index"), + "checkbox", + gr.Slider(0, 80, value=25), + gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"), + gr.Number(value=20), + gr.Radio(["S", "C", "Q"], type="index"), + ], + "label", + examples=[ + ["first", True, 30, [], 50, "S"], + ["second", False, 40, ["Sibling", "Child"], 10, "Q"], + ["third", True, 30, ["Child"], 20, "S"], + ], + interpretation="default", + live=True, +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/titanic_survival/screenshot.png b/demos/titanic_survival/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..b34c5f1a0cd920242949601c440412d2a6d31ba5 --- /dev/null +++ b/demos/titanic_survival/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8b4c52f5ef255b7ee57594b92c97ec69726a2cda49ac2e3c341908214b2384b2 +size 51010 diff --git a/demos/unispeech-speaker-verification/requirements.txt b/demos/unispeech-speaker-verification/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..baca89850c284e4dc6477356274e433f91c2308b --- /dev/null +++ b/demos/unispeech-speaker-verification/requirements.txt @@ -0,0 +1,2 @@ +git+https://github.com/huggingface/transformers +torchaudio diff --git a/demos/unispeech-speaker-verification/run.py b/demos/unispeech-speaker-verification/run.py new file mode 100644 index 0000000000000000000000000000000000000000..74adebc11204e0fd55765dabaa13358de9787d59 --- /dev/null +++ b/demos/unispeech-speaker-verification/run.py @@ -0,0 +1,120 @@ +import gradio as gr +import torch +from torchaudio.sox_effects import apply_effects_file +from transformers import AutoFeatureExtractor, AutoModelForAudioXVector + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +STYLE = """ + +""" +OUTPUT_OK = ( + STYLE + + """ +
+

The speakers are

+

{:.1f}%

+

similar

+

Welcome, human!

+
(You must get at least 85% to be considered the same person)
+
+""" +) +OUTPUT_FAIL = ( + STYLE + + """ +
+

The speakers are

+

{:.1f}%

+

similar

+

You shall not pass!

+
(You must get at least 85% to be considered the same person)
+
+""" +) + +EFFECTS = [ + ["remix", "-"], + ["channels", "1"], + ["rate", "16000"], + ["gain", "-1.0"], + ["silence", "1", "0.1", "0.1%", "-1", "0.1", "0.1%"], + ["trim", "0", "10"], +] + +THRESHOLD = 0.85 + +model_name = "microsoft/unispeech-sat-base-plus-sv" +feature_extractor = AutoFeatureExtractor.from_pretrained(model_name) +model = AutoModelForAudioXVector.from_pretrained(model_name).to(device) +cosine_sim = torch.nn.CosineSimilarity(dim=-1) + + +def similarity_fn(path1, path2): + if not (path1 and path2): + return 'ERROR: Please record audio for *both* speakers!' + wav1, _ = apply_effects_file(path1, EFFECTS) + wav2, _ = apply_effects_file(path2, EFFECTS) + print(wav1.shape, wav2.shape) + + input1 = feature_extractor(wav1.squeeze(0), return_tensors="pt", sampling_rate=16000).input_values.to(device) + input2 = feature_extractor(wav2.squeeze(0), return_tensors="pt", sampling_rate=16000).input_values.to(device) + + with torch.no_grad(): + emb1 = model(input1).embeddings + emb2 = model(input2).embeddings + emb1 = torch.nn.functional.normalize(emb1, dim=-1).cpu() + emb2 = torch.nn.functional.normalize(emb2, dim=-1).cpu() + similarity = cosine_sim(emb1, emb2).numpy()[0] + + if similarity >= THRESHOLD: + output = OUTPUT_OK.format(similarity * 100) + else: + output = OUTPUT_FAIL.format(similarity * 100) + + return output + + +inputs = [ + gr.Audio(source="microphone", type="filepath", optional=True, label="Speaker #1"), + gr.Audio(source="microphone", type="filepath", optional=True, label="Speaker #2"), +] +output = gr.HTML(label="") + + +description = ( + "This demo will compare two speech samples and determine if they are from the same speaker. " + "Try it with your own voice!" +) +article = ( + "

" + "🎙️ Learn more about UniSpeech-SAT | " + "📚 UniSpeech-SAT paper | " + "📚 X-Vector paper" + "

" +) +examples = [ + ["samples/cate_blanch.mp3", "samples/cate_blanch_2.mp3"], + ["samples/cate_blanch.mp3", "samples/cate_blanch_3.mp3"], + ["samples/cate_blanch_2.mp3", "samples/cate_blanch_3.mp3"], + ["samples/heath_ledger.mp3", "samples/heath_ledger_2.mp3"], + ["samples/cate_blanch.mp3", "samples/kirsten_dunst.wav"], +] + +demo = gr.Interface( + fn=similarity_fn, + inputs=inputs, + outputs=output, + title="Voice Authentication with UniSpeech-SAT + X-Vectors", + description=description, + article=article, + layout="horizontal", + theme="huggingface", + allow_flagging="never", + live=False, + examples=examples, +) + +if __name__ == "__main__": + demo.launch() + diff --git a/demos/unispeech-speaker-verification/samples/cate_blanch.mp3 b/demos/unispeech-speaker-verification/samples/cate_blanch.mp3 new file mode 100644 index 0000000000000000000000000000000000000000..4beee74653732ae01f47089374954d77666d69bc Binary files /dev/null and b/demos/unispeech-speaker-verification/samples/cate_blanch.mp3 differ diff --git a/demos/unispeech-speaker-verification/samples/cate_blanch_2.mp3 b/demos/unispeech-speaker-verification/samples/cate_blanch_2.mp3 new file mode 100644 index 0000000000000000000000000000000000000000..1acc2128c9d480adb4a8618578453041c6afaf6b Binary files /dev/null and b/demos/unispeech-speaker-verification/samples/cate_blanch_2.mp3 differ diff --git a/demos/unispeech-speaker-verification/samples/cate_blanch_3.mp3 b/demos/unispeech-speaker-verification/samples/cate_blanch_3.mp3 new file mode 100644 index 0000000000000000000000000000000000000000..fa0a9f7663aa26173f43a7b416df38a8cedcb79c Binary files /dev/null and b/demos/unispeech-speaker-verification/samples/cate_blanch_3.mp3 differ diff --git a/demos/unispeech-speaker-verification/samples/heath_ledger.mp3 b/demos/unispeech-speaker-verification/samples/heath_ledger.mp3 new file mode 100644 index 0000000000000000000000000000000000000000..eb63071e1b3da9fcd072ec2801eb6165c49b0cb3 Binary files /dev/null and b/demos/unispeech-speaker-verification/samples/heath_ledger.mp3 differ diff --git a/demos/unispeech-speaker-verification/samples/heath_ledger_2.mp3 b/demos/unispeech-speaker-verification/samples/heath_ledger_2.mp3 new file mode 100644 index 0000000000000000000000000000000000000000..d74f1ec3f08cc168b25184e75eeb65dc0550c1ac Binary files /dev/null and b/demos/unispeech-speaker-verification/samples/heath_ledger_2.mp3 differ diff --git a/demos/unispeech-speaker-verification/samples/kirsten_dunst.wav b/demos/unispeech-speaker-verification/samples/kirsten_dunst.wav new file mode 100644 index 0000000000000000000000000000000000000000..62db17ffcacd2d90a18833ca17f2947cc140b300 --- /dev/null +++ b/demos/unispeech-speaker-verification/samples/kirsten_dunst.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7ab77b27959f0f43126c94c4de3baa23b3f92c3c2e26ba322af2d6cd688f3233 +size 1287798 diff --git a/demos/video_identity/run.py b/demos/video_identity/run.py new file mode 100644 index 0000000000000000000000000000000000000000..152dab9b0e8389c69531bb109124160ab03156e1 --- /dev/null +++ b/demos/video_identity/run.py @@ -0,0 +1,18 @@ +import gradio as gr +import os + + +def video_identity(video): + return video + + +demo = gr.Interface(video_identity, + gr.Video(), + "playable_video", + examples=[ + os.path.join(os.path.dirname(__file__), + "video/video_sample.mp4")], + cache_examples=True) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/video_identity/screenshot.png b/demos/video_identity/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..9999f8c57f5ab7fb81ff0b892a6fdf3ec6b12371 --- /dev/null +++ b/demos/video_identity/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a431f9aacbd4427e007e8c08329743cee71f6cbb793734074c70d7389cab0480 +size 215602 diff --git a/demos/video_identity/video/video_sample.mp4 b/demos/video_identity/video/video_sample.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..b962d7f9787361bfd14cc58ff5c95f3a3db2eefe --- /dev/null +++ b/demos/video_identity/video/video_sample.mp4 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fc7e05ed802c94d74e9005e3bc53d5abfd36ab4b63be1602f6cfe697b789c418 +size 261179 diff --git a/demos/webcam/run.py b/demos/webcam/run.py new file mode 100644 index 0000000000000000000000000000000000000000..4f2a9e06226d9b780c77cb94e32dd38ecc9f6d3e --- /dev/null +++ b/demos/webcam/run.py @@ -0,0 +1,17 @@ +import numpy as np + +import gradio as gr + + +def snap(image, video): + return [image, video] + + +demo = gr.Interface( + snap, + [gr.Image(source="webcam", tool=None), gr.Video(source="webcam")], + ["image", "video"], +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/webcam/screenshot.png b/demos/webcam/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..5858bfa75d97125516b2856688e3b1f4ca8cca3d --- /dev/null +++ b/demos/webcam/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:17332adea6939c98a363a13c1445499051cfdcab903af7a32d06b414f7765507 +size 589678 diff --git a/demos/zip_to_json/run.py b/demos/zip_to_json/run.py new file mode 100644 index 0000000000000000000000000000000000000000..2500b409d12175e25cc4062696624e85f5f78124 --- /dev/null +++ b/demos/zip_to_json/run.py @@ -0,0 +1,23 @@ +from zipfile import ZipFile + +import gradio as gr + + +def zip_to_json(file_obj): + files = [] + with ZipFile(file_obj.name) as zfile: + for zinfo in zfile.infolist(): + files.append( + { + "name": zinfo.filename, + "file_size": zinfo.file_size, + "compressed_size": zinfo.compress_size, + } + ) + return files + + +demo = gr.Interface(zip_to_json, "file", "json") + +if __name__ == "__main__": + demo.launch() diff --git a/demos/zip_to_json/screenshot.png b/demos/zip_to_json/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..85a15137fa5efefc7fc49a6d44637365bf7da690 --- /dev/null +++ b/demos/zip_to_json/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:017624b0c837e70b24501ee9c0c1befc5f07fbafc5f01556fc4405d134a9a0c3 +size 37168 diff --git a/demos/zip_two_files/files/titanic.csv b/demos/zip_two_files/files/titanic.csv new file mode 100644 index 0000000000000000000000000000000000000000..63b68ab0ba98c667f515c52f08c0bbd5573d5330 --- /dev/null +++ b/demos/zip_two_files/files/titanic.csv @@ -0,0 +1,892 @@ +PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked +1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S +2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C +3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S +4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S +5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S +6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q +7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S +8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S +9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S +10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C +11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S +12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S +13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S +14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S +15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S +16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S +17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q +18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S +19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S +20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C +21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S +22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S +23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q +24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S +25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S +26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S +27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C +28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S +29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q +30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S +31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C +32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C +33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q +34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S +35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C +36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S +37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C +38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S +39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S +40,1,3,"Nicola-Yarred, Miss. 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Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S +890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C +891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q diff --git a/demos/zip_two_files/run.py b/demos/zip_two_files/run.py new file mode 100644 index 0000000000000000000000000000000000000000..29c1b015ed9f627e1964ccb65b7c9dae6f1e0865 --- /dev/null +++ b/demos/zip_two_files/run.py @@ -0,0 +1,25 @@ +import os +from zipfile import ZipFile + +import gradio as gr + + +def zip_two_files(file1, file2): + with ZipFile("tmp.zip", "w") as zipObj: + zipObj.write(file1.name, "file1") + zipObj.write(file2.name, "file2") + return "tmp.zip" + + +demo = gr.Interface( + zip_two_files, + ["file", "file"], + "file", + examples=[ + [os.path.join(os.path.dirname(__file__),"files/titanic.csv"), + os.path.join(os.path.dirname(__file__),"files/titanic.csv")], + ], +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/zip_two_files/screenshot.png b/demos/zip_two_files/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..49c51b78a2a3ab894a3dd4c81f3ea197eac68909 --- /dev/null +++ b/demos/zip_two_files/screenshot.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b1494912b7f1af4d8ac3b3ce6600adc6200ea8021ed0273fd1878fbeab0bc7f7 +size 46335 diff --git a/gradio-3.1.4-py3-none-any.whl b/gradio-3.1.4-py3-none-any.whl new file mode 100644 index 0000000000000000000000000000000000000000..8d12ac8ca73259294cc2bcbccc18ff3ae117c4a7 --- /dev/null +++ b/gradio-3.1.4-py3-none-any.whl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:acdeb378e415ce4f9b85f8cbd91bbe73c75e3e83adf4476902259f01140af5f1 +size 5649118 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/run.py b/run.py new file mode 100644 index 0000000000000000000000000000000000000000..a5831990e722cc79b2efbb8b3dde3f86d31f6ceb --- /dev/null +++ b/run.py @@ -0,0 +1,38 @@ +import importlib +import gradio as gr +import os +import sys +import copy +import pathlib + +demo_dir = pathlib.Path(__file__).parent / "demos" + + +all_demos = [] +for p in os.listdir("./demos"): + full_dir = os.path.join(demo_dir, p) + is_dir = os.path.isdir(full_dir) + no_requirements = not os.path.exists(os.path.join(full_dir, "requirements.txt")) + has_app = os.path.exists(os.path.join(full_dir, "run.py")) + if is_dir and no_requirements and has_app: + all_demos.append(p) + + to_exclude = ["blocks_demos", "rows_and_columns", "image_classifier_interface_load", "generate_english_german", + "all_demos"] + all_demos = [d for d in all_demos if d not in to_exclude] + +demo_module = None +with gr.Blocks() as mega_demo: + with gr.Tabs(): + for demo_name in all_demos: + with gr.TabItem(demo_name): + old_path = copy.deepcopy(sys.path) + sys.path = [os.path.join(demo_dir, demo_name)] + sys.path + if demo_module is None: + demo_module = importlib.import_module(f"run") + else: + demo_module = importlib.reload(demo_module) + demo_module.demo + sys.path = old_path + +mega_demo.launch()