Update app.py

app.py

@@ -27,7 +27,10 @@ status = pn.pane.Markdown("")
 
 # Leaderboard display
 leaderboard = pn.pane.DataFrame(pd.DataFrame(), width=600)
+import tempfile
 
+temp_dir = tempfile.gettempdir()
+submission_path = os.path.join(temp_dir, filename)
 def submit_file(event):
     if file_input.value is None:
         status.object = "⚠️ Please upload a .zip file before submitting."
@@ -64,141 +67,3 @@ app = pn.Column(
 )
 
 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)