Create test.py

test.py

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+import numpy as np
+import gradio as gr
+import ast
+import requests
+
+from theme_dropdown import create_theme_dropdown  # noqa: F401
+
+dropdown, js = create_theme_dropdown()
+
+models = [
+    {"name": "Stable Diffusion 2", "url": "stabilityai/stable-diffusion-2-1"},
+    {"name": "stability AI", "url": "stabilityai/stable-diffusion-2-1-base"},
+    {"name": "Compressed-S-D", "url": "nota-ai/bk-sdm-small"},
+    {"name": "Future Diffusion", "url": "nitrosocke/Future-Diffusion"},
+    {"name": "JWST Deep Space Diffusion", "url": "dallinmackay/JWST-Deep-Space-diffusion"},
+    {"name": "Robo Diffusion 3 Base", "url": "nousr/robo-diffusion-2-base"},
+    {"name": "Robo Diffusion", "url": "nousr/robo-diffusion"},
+    {"name": "Tron Legacy Diffusion", "url": "dallinmackay/Tron-Legacy-diffusion"},
+]   
+
+text_gen = gr.Interface.load("spaces/daspartho/prompt-extend")
+
+current_model = models[0]
+
+models2 = []
+for model in models:
+    model_url = f"models/{model['url']}"
+    loaded_model = gr.Interface.load(model_url, live=True, preprocess=True)
+    models2.append(loaded_model)
+
+def text_it(inputs, text_gen=text_gen):
+    return text_gen(inputs)
+
+def flip_text(x):
+    return x[::-1]
+
+def send_it(inputs, model_choice):
+    proc = models2[model_choice]
+    return proc(inputs)
+
+
+def flip_image(x):
+    return np.fliplr(x)
+
+
+def set_model(current_model_index):
+    global current_model
+    current_model = models[current_model_index]
+    return gr.update(value=f"{current_model['name']}")
+
+
+with gr.Blocks(theme='pikto/theme@>=0.0.1,<0.0.3') as pan:
+    gr.Markdown("AI CONTENT TOOLS.")
+                    
+    with gr.Tab("T-to-I"):
+        
+    ##model = ("stabilityai/stable-diffusion-2-1")
+         model_name1 = gr.Dropdown(
+                label="Choose Model",
+                choices=[m["name"] for m in models],
+                type="index",
+                value=current_model["name"],
+                interactive=True,
+         )
+         input_text = gr.Textbox(label="Prompt idea",)
+
+        ##  run = gr.Button("Generate Images")
+         with gr.Row():
+             see_prompts = gr.Button("Generate Prompts")
+             run = gr.Button("Generate Images", variant="primary")
+        
+         with gr.Row():
+             magic1 = gr.Textbox(label="Generated Prompt", lines=2)
+             output1 = gr.Image(label="")
+          
+             
+         with gr.Row():    
+             magic2 = gr.Textbox(label="Generated Prompt", lines=2)
+             output2 = gr.Image(label="")
+
+            
+         run.click(send_it, inputs=[magic1, model_name1], outputs=[output1])
+         run.click(send_it, inputs=[magic2, model_name1], outputs=[output2])
+         see_prompts.click(text_it, inputs=[input_text], outputs=[magic1])
+         see_prompts.click(text_it, inputs=[input_text], outputs=[magic2])
+        
+    model_name1.change(set_model, inputs=model_name1, outputs=[output1, output2,])
+        
+    with gr.Tab("Flip Image"):
+         #Using Gradio Demos as API - This is Hot!
+         API_URL_INITIAL = "https://ysharma-playground-ai-exploration.hf.space/run/initial_dataframe"
+         API_URL_NEXT10 = "https://ysharma-playground-ai-exploration.hf.space/run/next_10_rows"
+
+#define inference function
+#First: Get initial images for the grid display 
+def get_initial_images():
+  response = requests.post(API_URL_INITIAL, json={
+            "data": []
+            }).json()
+  #data = response["data"][0]['data'][0][0][:-1]
+  response_dict = response['data'][0]
+  return response_dict  #, [resp[0][:-1] for resp in response["data"][0]["data"]]
+
+#Second: Process response dictionary to get imges as hyperlinked image tags
+def process_response(response_dict):
+  return [resp[0][:-1] for resp in response_dict["data"]]
+
+response_dict = get_initial_images()
+initial = process_response(response_dict)
+initial_imgs  = '<div style="display: grid; grid-template-columns: repeat(3, 1fr); grid-template-rows: repeat(3, 1fr); grid-gap: 0; background-color: #fff; padding: 20px; box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);">\n' + "\n".join(initial[:-1])
+
+#Third: Load more images for the grid
+def get_next10_images(response_dict, row_count):
+    row_count = int(row_count)
+    #print("(1)",type(response_dict))
+    #Convert the string to a dictionary
+    if isinstance(response_dict, dict) == False :
+        response_dict = ast.literal_eval(response_dict)
+    response = requests.post(API_URL_NEXT10, json={
+              "data": [response_dict, row_count ] #len(initial)-1
+               }).json()
+    row_count+=10
+    response_dict = response['data'][0]
+    #print("(2)",type(response))
+    #print("(3)",type(response['data'][0]))
+    next_set  = [resp[0][:-1] for resp in response_dict["data"]]
+    next_set_images = '<div style="display: grid; grid-template-columns: repeat(3, 1fr); grid-template-rows: repeat(3, 1fr); grid-gap: 0; background-color: #fff; padding: 20px; box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); ">\n' + "\n".join(next_set[:-1])
+    return response_dict, row_count, next_set_images  #response['data'][0]
+
+#get_next10_images(response_dict=response_dict, row_count=9)
+#position: fixed; top: 0; left: 0; width: 100%; background-color: #fff; padding: 20px; box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+
+#Defining the Blocks layout
+with gr.Blocks(css = """#img_search img {width: 100%; height: 100%; object-fit: cover;}""") as demo:
+  gr.HTML(value="top of page", elem_id="top",visible=False)
+  gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
+        <div
+        style="
+            display: inline-flex;
+            align-items: center;
+            gap: 0.8rem;
+            font-size: 1.75rem;
+        "
+        >
+        <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
+            Using Gradio Demos as API - 2 </h1><br></div>
+        <div><h4 style="font-weight: 500; margin-bottom: 7px; margin-top: 5px;">
+            Stream <a href="https://github.com/playgroundai/liked_images" target="_blank">PlaygroundAI Images</a> ina beautiful grid</h4><br>
+        </div>""")
+  with gr.Accordion(label="Details about the working:", open=False, elem_id='accordion'):
+    gr.HTML("""
+        <p style="margin-bottom: 10px; font-size: 90%"><br>
+        ▶️Do you see the "view api" link located in the footer of this application? 
+        By clicking on this link, a page will open which provides documentation on the REST API that developers can use to query the Interface function / Block events.<br>
+        ▶️In this demo, I am making such an API request to the <a href="https://huggingface.co/spaces/ysharma/Playground_AI_Exploration" target="_blank">Playground_AI_Exploration</a> Space.<br>
+        ▶️I am exposing an API endpoint of this Gradio app as well. This can easily be done by one line of code, just set the api_name parameter of the event listener.
+        </p></div>""")
+
+  with gr.Column(): #(elem_id = "col-container"):
+    b1 = gr.Button("Load More Images").style(full_width=False)
+    df = gr.Textbox(visible=False,elem_id='dataframe', value=response_dict)
+    row_count = gr.Number(visible=False, value=19 )
+    img_search = gr.HTML(label = 'Images from PlaygroundAI dataset', elem_id="img_search", 
+                         value=initial_imgs ) #initial[:-1] )
+      
+  gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/Stream_PlaygroundAI_Images?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a></center> 
+        </p></div>''')
+  b1.click(get_next10_images, [df, row_count], [df, row_count, img_search], api_name = "load_playgroundai_images" ) 
+            
+
+    with gr.Tab("Diffuser"):
+        with gr.Row():
+            text_input = gr.Textbox()                      ##   Diffuser
+            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)
+pan.queue(concurrency_count=200)
+pan.launch(inline=True, show_api=True, max_threads=400)