import os

import streamlit as st
import torch
import pandas as pd
import numpy as np
import requests

from bokeh.plotting import figure, show
from bokeh.models import HoverTool, ColumnDataSource, CustomJSHover
from bokeh.embed import file_html
from bokeh.resources import CDN  # Import CDN here
from datasets import load_dataset, Dataset, load_from_disk
from huggingface_hub import login
from sklearn.manifold import TSNE
from tqdm import tqdm


@st.cache_data
def load_hf_dataset():
    # login to huggingface
    login(token=os.environ.get("HF_TOKEN"))

    # load from huggingface
    roster = pd.DataFrame(load_dataset('MAPS-research/GEMRec-Roster', split='train'))
    promptBook = pd.DataFrame(load_dataset('MAPS-research/GEMRec-Metadata', split='train'))

    # process dataset
    roster = roster[['model_id', 'model_name', 'modelVersion_id', 'modelVersion_name',
                                                       'model_download_count']].drop_duplicates().reset_index(drop=True)

    # add 'custom_score_weights' column to promptBook if not exist
    if 'weighted_score_sum' not in promptBook.columns:
        promptBook.loc[:, 'weighted_score_sum'] = 0

    # merge roster and promptbook
    promptBook = promptBook.merge(roster[['model_id', 'model_name', 'modelVersion_id', 'modelVersion_name', 'model_download_count']],
                                                                    on=['model_id', 'modelVersion_id'], how='left')

    # add column to record current row index
    promptBook.loc[:, 'row_idx'] = promptBook.index

    return roster, promptBook

def show_with_bokeh(data, streamlit=False):
    # Extract x, y coordinates and image URLs
    x_coords, y_coords, image_urls = zip(*data)

    # Create a ColumnDataSource
    source = ColumnDataSource(data=dict(x=x_coords, y=y_coords, image=image_urls))

    # Create a figure
    p = figure(width=800, height=600)

    # Add scatter plot
    scatter = p.scatter(x='x', y='y', size=20, source=source)

    # Define hover tool
    hover = HoverTool()
    # hover.tooltips = """
    #         <div>
    #             <iframe src="@image" width="512" height="512"></iframe>
    #         </div>
    #     """
    # hover.formatters = {'@image': CustomJSHover(code="""
    #         const index = cb_data.index;
    #         const url = cb_data.source.data['image'][index];
    #         return '<iframe src="' + url + '" width="512" height="512"></iframe>';
    #     """)}

    hover.tooltips = """
            <div>
                <img src="@image" style='object-fit: contain'; height=100%">
            </div>
        """
    hover.formatters = {'@image': CustomJSHover(code="""
            const index = cb_data.index;
            const url = cb_data.source.data['image'][index];
            return '<img src="' + url + '">';
        """)}

    p.add_tools(hover)

    # Generate HTML with the plot
    html = file_html(p, CDN, "Interactive Scatter Plot with Hover Images")

    # Save the HTML file or show it
    # with open("scatter_plot_with_hover_images.html", "w") as f:
    #     f.write(html)

    if streamlit:
        st.bokeh_chart(p, use_container_width=True)
    else:
        show(p)


def show_with_bokeh_2(data, image_size=[40, 40], streamlit=False):
    # Extract x, y coordinates and image URLs
    x_coords, y_coords, image_urls = zip(*data)

    # Create a ColumnDataSource
    source = ColumnDataSource(data=dict(x=x_coords, y=y_coords, image=image_urls))

    # Create a figure
    p = figure(width=800, height=600, aspect_ratio=1.0)

    # Add image glyphs
    # image_size = 40  # Adjust this size as needed
    scale = 0.1
    image_size = [int(image_size[0])*scale, int(image_size[1])*scale]
    print(image_size)
    p.image_url(url='image', x='x', y='y', source=source, w=image_size[0], h=image_size[1], anchor="center")

    # Define hover tool
    hover = HoverTool()
    hover.tooltips = """
            <div>
                <img src="@image" style='object-fit: contain'; height=100%'">
            </div>
        """
    p.add_tools(hover)

    # Generate HTML with the plot
    html = file_html(p, CDN, "Scatter Plot with Images")

    # Save the HTML file or show it
    # with open("scatter_plot_with_images.html", "w") as f:
    #     f.write(html)

    if streamlit:
        st.bokeh_chart(p, use_container_width=True)
    else:
        show(p)


if __name__ == '__main__':
    # load dataset
    roster, promptBook = load_hf_dataset()

    print('==> loading feats')
    feats = {}
    for pt in os.listdir('../data/feats'):
        if pt.split('.')[-1] == 'pt' and pt.split('.')[0].isdigit():
            feats[pt.split('.')[0]] = torch.load(os.path.join('../data/feats', pt))

    print('==> applying t-SNE')
    # apply t-SNE to entries in each feat in feats to get 2D coordinates
    tsne = TSNE(n_components=2, random_state=0)
    # for k, v in tqdm(feats.items()):
    #     feats[k]['tsne'] = tsne.fit_transform(v['all'].numpy())
    prompt_id = '49'
    feats[prompt_id]['tsne'] = tsne.fit_transform(feats[prompt_id]['all'].numpy())

    print(feats[prompt_id]['tsne'])

    keys = []
    for k in feats[prompt_id].keys():
        if k != 'all' and k != 'tsne':
            keys.append(int(k.item()))

    print(keys)

    data = []
    for idx in range(len(keys)):
        modelVersion_id = keys[idx]
        image_id = promptBook[(promptBook['modelVersion_id'] == modelVersion_id) & (promptBook['prompt_id'] == int(prompt_id))].reset_index(drop=True).loc[0, 'image_id']
        image_url = f"https://modelcofferbucket.s3-accelerate.amazonaws.com/{image_id}.png"
        scale = 50
        data.append((feats[prompt_id]['tsne'][idx][0]*scale, feats[prompt_id]['tsne'][idx][1]*scale, image_url))

        image_size = promptBook[(promptBook['image_id'] == image_id)].reset_index(drop=True).loc[0, 'size'].split('x')

    # # Sample data: (x, y) coordinates and corresponding image URLs
    # data = [
    #     (2, 5, "https://www.crunchyroll.com/imgsrv/display/thumbnail/480x720/catalog/crunchyroll/669dae5dbea3d93bb5f1012078501976.jpeg"),
    #     (4, 8, "https://i.pinimg.com/originals/40/6d/38/406d38957bc4fd12f34c5dfa3d73b86d.jpg"),
    #     (7, 3, "https://i.pinimg.com/550x/76/27/d2/7627d227adc6fb5fb6662ebfb9d82d7e.jpg"),
    #     # Add more data points and image URLs
    # ]

    # show_with_bokeh(data, streamlit=True)
    show_with_bokeh_2(data, image_size=image_size, streamlit=True)