async version of the image classifier

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

app.py

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+import aiohttp
+import io
+import random
+import panel as pn
+
+from PIL import Image
+
+from transformers import CLIPProcessor, CLIPModel
+from typing import List, Tuple
+
+pn.extension(design='bootstrap', sizing_mode="stretch_width")
+
+async def random_url(_):
+    api_url = random.choice([
+        "https://api.thecatapi.com/v1/images/search",
+        "https://api.thedogapi.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.
+    """
+    if not image_url:
+        yield '## Provide an image URL' 
+        return
+    yield '## Fetching image and running model ⚙'
+    pil_img = await open_image_url(image_url)
+    img = pn.pane.Image(pil_img, height=400, align='center')
+
+    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
+
+# 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.bind(
+    process_inputs, image_url=image_url, class_names=class_names
+)
+
+# create dashboard
+main = pn.WidgetBox(
+    input_widgets,
+    interactive_result,
+)
+
+pn.template.BootstrapTemplate(
+    title="Panel Image Classification Demo",
+    main=main,
+    main_max_width="min(50%, 698px)",
+    header_background="#F08080",
+).servable(title="Panel Image Classification Demo");

requirements.txt

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+panel
+jupyter
+transformers
+numpy
+torch