Duplicate from Panel-Org/panel-template

Co-authored-by: Sophia Yang <[email protected]>

.gitattributes

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+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+RUN python3 -m pip install --no-cache-dir --upgrade pip
+RUN python3 -m pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["panel", "serve", "/code/app.py", "--address", "0.0.0.0", "--port", "7860",  "--allow-websocket-origin", "*"]
+
+RUN mkdir /.cache
+RUN chmod 777 /.cache
+RUN mkdir .chroma
+RUN chmod 777 .chroma

README.md

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+---
+title: Panel Template
+emoji: 📈
+colorFrom: gray
+colorTo: green
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import io
+import random
+from typing import List, Tuple
+
+import aiohttp
+import panel as pn
+from PIL import Image
+from transformers import CLIPModel, CLIPProcessor
+
+pn.extension(design="bootstrap", sizing_mode="stretch_width")
+
+ICON_URLS = {
+    "brand-github": "https://github.com/holoviz/panel",
+    "brand-twitter": "https://twitter.com/Panel_Org",
+    "brand-linkedin": "https://www.linkedin.com/company/panel-org",
+    "message-circle": "https://discourse.holoviz.org/",
+    "brand-discord": "https://discord.gg/AXRHnJU6sP",
+}
+
+
+async def random_url(_):
+    pet = random.choice(["cat", "dog"])
+    api_url = f"https://api.the{pet}api.com/v1/images/search"
+    async with aiohttp.ClientSession() as session:
+        async with session.get(api_url) as resp:
+            return (await resp.json())[0]["url"]
+
+
+@pn.cache
+def load_processor_model(
+    processor_name: str, model_name: str
+) -> Tuple[CLIPProcessor, CLIPModel]:
+    processor = CLIPProcessor.from_pretrained(processor_name)
+    model = CLIPModel.from_pretrained(model_name)
+    return processor, model
+
+
+async def open_image_url(image_url: str) -> Image:
+    async with aiohttp.ClientSession() as session:
+        async with session.get(image_url) as resp:
+            return Image.open(io.BytesIO(await resp.read()))
+
+
+def get_similarity_scores(class_items: List[str], image: Image) -> List[float]:
+    processor, model = load_processor_model(
+        "openai/clip-vit-base-patch32", "openai/clip-vit-base-patch32"
+    )
+    inputs = processor(
+        text=class_items,
+        images=[image],
+        return_tensors="pt",  # pytorch tensors
+    )
+    outputs = model(**inputs)
+    logits_per_image = outputs.logits_per_image
+    class_likelihoods = logits_per_image.softmax(dim=1).detach().numpy()
+    return class_likelihoods[0]
+
+
+async def process_inputs(class_names: List[str], image_url: str):
+    """
+    High level function that takes in the user inputs and returns the
+    classification results as panel objects.
+    """
+    try:
+        main.disabled = True
+        if not image_url:
+            yield "##### ⚠️ Provide an image URL"
+            return
+    
+        yield "##### ⚙ Fetching image and running model..."
+        try:
+            pil_img = await open_image_url(image_url)
+            img = pn.pane.Image(pil_img, height=400, align="center")
+        except Exception as e:
+            yield f"##### 😔 Something went wrong, please try a different URL!"
+            return
+    
+        class_items = class_names.split(",")
+        class_likelihoods = get_similarity_scores(class_items, pil_img)
+    
+        # build the results column
+        results = pn.Column("##### 🎉 Here are the results!", img)
+    
+        for class_item, class_likelihood in zip(class_items, class_likelihoods):
+            row_label = pn.widgets.StaticText(
+                name=class_item.strip(), value=f"{class_likelihood:.2%}", align="center"
+            )
+            row_bar = pn.indicators.Progress(
+                value=int(class_likelihood * 100),
+                sizing_mode="stretch_width",
+                bar_color="secondary",
+                margin=(0, 10),
+                design=pn.theme.Material,
+            )
+            results.append(pn.Column(row_label, row_bar))
+        yield results
+    finally:
+        main.disabled = False
+
+
+# create widgets
+randomize_url = pn.widgets.Button(name="Randomize URL", align="end")
+
+image_url = pn.widgets.TextInput(
+    name="Image URL to classify",
+    value=pn.bind(random_url, randomize_url),
+)
+class_names = pn.widgets.TextInput(
+    name="Comma separated class names",
+    placeholder="Enter possible class names, e.g. cat, dog",
+    value="cat, dog, parrot",
+)
+
+input_widgets = pn.Column(
+    "##### 😊 Click randomize or paste a URL to start classifying!",
+    pn.Row(image_url, randomize_url),
+    class_names,
+)
+
+# add interactivity
+interactive_result = pn.panel(
+    pn.bind(process_inputs, image_url=image_url, class_names=class_names),
+    height=600,
+)
+
+# add footer
+footer_row = pn.Row(pn.Spacer(), align="center")
+for icon, url in ICON_URLS.items():
+    href_button = pn.widgets.Button(icon=icon, width=35, height=35)
+    href_button.js_on_click(code=f"window.open('{url}')")
+    footer_row.append(href_button)
+footer_row.append(pn.Spacer())
+
+# create dashboard
+main = pn.WidgetBox(
+    input_widgets,
+    interactive_result,
+    footer_row,
+)
+
+title = "Panel Demo - Image Classification"
+pn.template.BootstrapTemplate(
+    title=title,
+    main=main,
+    main_max_width="min(50%, 698px)",
+    header_background="#F08080",
+).servable(title=title)

requirements.txt

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+panel
+jupyter
+transformers
+numpy
+torch
+aiohttp