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import io
import random
from typing import List, Tuple
import pandas as pd

import aiohttp
import panel as pn
from PIL import Image
from transformers import CLIPModel, CLIPProcessor

pn.extension(design="bootstrap", sizing_mode="stretch_width")

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






def get_image_filenames():
    image_filenames = []
    for i in range(1, 31):
        filename = f'image_{i}.jpg'  # Assuming the files are named like 'image_1.jpg', 'image_2.jpg', etc.
        image_filenames.append(filename)
    return image_filenames

image_list = get_image_filenames()
print(image_list)


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,
)

df = pd.read_csv('nba_teams.csv') # load csv of team file names
nba_team_list = df[0].tolist()


title = "NBA Team Identifying Expert"
pn.template.BootstrapTemplate(
    title=title,
    main=main,
    main_max_width="min(50%, 698px)",
    header_background="#F08080",
).servable(title=title)