import io
import random
from typing import List, Tuple

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

import folium
import pandas as pd
import panel as pn
from folium.plugins import MarkerCluster, MiniMap
from branca.element import Template, MacroElement
from pathlib import Path


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

www_dir = Path(__file__).parent.resolve() / "www"

df = pd.read_parquet(Path(__file__).parent / "All_hh.parquet")


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)