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

    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)