LinuxWhiz/Coder2

1import io2import random3from typing import List, Tuple4 5import aiohttp6import panel as pn7from PIL import Image8from transformers import CLIPModel, CLIPProcessor9 10pn.extension(design="bootstrap", sizing_mode="stretch_width")11 12ICON_URLS = {13    "brand-github": "https://github.com/holoviz/panel",14    "brand-twitter": "https://twitter.com/Panel_Org",15    "brand-linkedin": "https://www.linkedin.com/company/panel-org",16    "message-circle": "https://discourse.holoviz.org/",17    "brand-discord": "https://discord.gg/AXRHnJU6sP",18}19 20 21async def random_url(_):22    pet = random.choice(["cat", "dog"])23    api_url = f"https://api.the{pet}api.com/v1/images/search"24    async with aiohttp.ClientSession() as session:25        async with session.get(api_url) as resp:26            return (await resp.json())[0]["url"]27 28 29@pn.cache30def load_processor_model(31    processor_name: str, model_name: str32) -> Tuple[CLIPProcessor, CLIPModel]:33    processor = CLIPProcessor.from_pretrained(processor_name)34    model = CLIPModel.from_pretrained(model_name)35    return processor, model36 37 38async def open_image_url(image_url: str) -> Image:39    async with aiohttp.ClientSession() as session:40        async with session.get(image_url) as resp:41            return Image.open(io.BytesIO(await resp.read()))42 43 44def get_similarity_scores(class_items: List[str], image: Image) -> List[float]:45    processor, model = load_processor_model(46        "openai/clip-vit-base-patch32", "openai/clip-vit-base-patch32"47    )48    inputs = processor(49        text=class_items,50        images=[image],51        return_tensors="pt",  # pytorch tensors52    )53    outputs = model(**inputs)54    logits_per_image = outputs.logits_per_image55    class_likelihoods = logits_per_image.softmax(dim=1).detach().numpy()56    return class_likelihoods[0]57 58 59async def process_inputs(class_names: List[str], image_url: str):60    """61    High level function that takes in the user inputs and returns the62    classification results as panel objects.63    """64    try:65        main.disabled = True66        if not image_url:67            yield "##### ⚠️ Provide an image URL"68            return69    70        yield "##### ⚙ Fetching image and running model..."71        try:72            pil_img = await open_image_url(image_url)73            img = pn.pane.Image(pil_img, height=400, align="center")74        except Exception as e:75            yield f"##### 😔 Something went wrong, please try a different URL!"76            return77    78        class_items = class_names.split(",")79        class_likelihoods = get_similarity_scores(class_items, pil_img)80    81        # build the results column82        results = pn.Column("##### 🎉 Here are the results!", img)83    84        for class_item, class_likelihood in zip(class_items, class_likelihoods):85            row_label = pn.widgets.StaticText(86                name=class_item.strip(), value=f"{class_likelihood:.2%}", align="center"87            )88            row_bar = pn.indicators.Progress(89                value=int(class_likelihood * 100),90                sizing_mode="stretch_width",91                bar_color="secondary",92                margin=(0, 10),93                design=pn.theme.Material,94            )95            results.append(pn.Column(row_label, row_bar))96        yield results97    finally:98        main.disabled = False99 100 101# create widgets102randomize_url = pn.widgets.Button(name="Randomize URL", align="end")103 104image_url = pn.widgets.TextInput(105    name="Image URL to classify",106    value=pn.bind(random_url, randomize_url),107)108class_names = pn.widgets.TextInput(109    name="Comma separated class names",110    placeholder="Enter possible class names, e.g. cat, dog",111    value="cat, dog, parrot",112)113 114input_widgets = pn.Column(115    "##### 😊 Click randomize or paste a URL to start classifying!",116    pn.Row(image_url, randomize_url),117    class_names,118)119 120# add interactivity121interactive_result = pn.panel(122    pn.bind(process_inputs, image_url=image_url, class_names=class_names),123    height=600,124)125 126# add footer127footer_row = pn.Row(pn.Spacer(), align="center")128for icon, url in ICON_URLS.items():129    href_button = pn.widgets.Button(icon=icon, width=35, height=35)130    href_button.js_on_click(code=f"window.open('{url}')")131    footer_row.append(href_button)132footer_row.append(pn.Spacer())133 134# create dashboard135main = pn.WidgetBox(136    input_widgets,137    interactive_result,138    footer_row,139)140 141title = "Panel Demo - Image Classification"142pn.template.BootstrapTemplate(143    title=title,144    main=main,145    main_max_width="min(50%, 698px)",146    header_background="#F08080",147).servable(title=title)