Update app.py

app.py

@@ -74,139 +74,139 @@ 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
+# 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
+#         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)
+#         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)
+#         # 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)
+#         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)