| 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") | |
| import panel as pn | |
| import pandas as pd | |
| import os | |
| import datetime | |
| import io | |
| from google_sheet import fetch_leaderboard | |
| from google_drive import upload_to_drive | |
| pn.extension() | |
| # File upload widget | |
| file_input = pn.widgets.FileInput(accept='.zip', multiple=False) | |
| # Status message | |
| status = pn.pane.Markdown("") | |
| # Leaderboard display | |
| leaderboard = pn.pane.DataFrame(pd.DataFrame(), width=600) | |
| def submit_file(event): | |
| if file_input.value is None: | |
| status.object = "⚠️ Please upload a .zip file before submitting." | |
| return | |
| # Save uploaded file | |
| timestamp = datetime.datetime.now().isoformat().replace(":", "_") | |
| filename = f"{timestamp}_{file_input.filename}" | |
| submission_path = os.path.join("submissions", filename) | |
| os.makedirs("submissions", exist_ok=True) | |
| with open(submission_path, "wb") as f: | |
| f.write(file_input.value) | |
| try: | |
| drive_file_id = upload_to_drive(submission_path, filename) | |
| status.object = f"✅ Uploaded to Google Drive [File ID: {drive_file_id}]" | |
| except Exception as e: | |
| status.object = f"❌ Failed to upload to Google Drive: {e}" | |
| # Update leaderboard | |
| try: | |
| df = fetch_leaderboard() | |
| if not df.empty: | |
| df_sorted = df.sort_values(by="score", ascending=False) | |
| leaderboard.object = df_sorted | |
| else: | |
| leaderboard.object = pd.DataFrame() | |
| except Exception as e: | |
| status.object += f"\n⚠️ Could not load leaderboard: {e}" | |
| submit_button = pn.widgets.Button(name="Submit", button_type="primary") | |
| submit_button.on_click(submit_file) | |
| # Layout | |
| app = pn.Column( | |
| "## 🏆 Hackathon Leaderboard", | |
| file_input, | |
| submit_button, | |
| status, | |
| "### Leaderboard", | |
| leaderboard | |
| ) | |
| app.servable() | |
| # 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) | |