import json
from urllib import request
from fastapi import FastAPI
from starlette.middleware.sessions import SessionMiddleware
from starlette.responses import HTMLResponse, RedirectResponse
from starlette.requests import Request
import gradio as gr
import uvicorn
from fastapi.responses import HTMLResponse
from fastapi.responses import RedirectResponse
import pandas as pd

import spotipy
from spotipy import oauth2

import heatmap

PORT_NUMBER = 8080
SPOTIPY_CLIENT_ID = 'c087fa97cebb4f67b6f08ba841ed8378'
SPOTIPY_CLIENT_SECRET = 'ae27d6916d114ac4bb948bb6c58a72d9'
SPOTIPY_REDIRECT_URI = 'https://hf-hackathon-2023-01-spotify.hf.space'
SCOPE = 'user-library-read'

sp_oauth = oauth2.SpotifyOAuth(SPOTIPY_CLIENT_ID, SPOTIPY_CLIENT_SECRET, SPOTIPY_REDIRECT_URI, scope=SCOPE)

app = FastAPI()
app.add_middleware(SessionMiddleware, secret_key="w.o.w")

@app.get('/', response_class=HTMLResponse)
async def homepage(request: Request):
    token = request.session.get('token')
    if token:
        return RedirectResponse("/gradio")

    url = str(request.url)
    code = sp_oauth.parse_response_code(url)
    if code != url:
        token_info = sp_oauth.get_access_token(code)
        request.session['token'] = token_info['access_token']
        return RedirectResponse("/gradio")

    auth_url = sp_oauth.get_authorize_url()
    return "<a href='" + auth_url + "'>Login to Spotify</a>"



from vega_datasets import data

iris = data.iris()


def scatter_plot_fn(request: gr.Request):
    token = request.request.session.get('token')
    if token:
        sp = spotipy.Spotify(token)
        results = sp.current_user()
        print(results)
    return gr.ScatterPlot(
        value=iris,
    )

def heatmap_plot_fn(request: gr.Request):
    token = request.request.session.get('token')
    if token:
        sp = spotipy.Spotify(token)
        data = heatmap.build_heatmap(heatmap.fetch_recent_songs(sp))
        fig, _ = heatmap.plot(data)
        return fig


def get_features(spotify):
    features = []
    for index in range(0, 10):
        results = spotify.current_user_saved_tracks(offset=index*50, limit=50)
        track_ids = [item['track']['id'] for item in results['items']]
        features.extend(spotify.audio_features(track_ids))

    df = pd.DataFrame(data=features)
    names = [
        'danceability', 
        'energy',
        'loudness',
        'speechiness',
        'acousticness',
        'instrumentalness',
        'liveness',
        'valence',
        'tempo',
    ]
    features_means = df[names].mean()
    # print (features_means.to_json())
    return features_means


##########
def get_started():
    # redirects to spotify and comes back
    # then generates plots
    return

with gr.Blocks() as demo:
    gr.Markdown(" ## Spotify Analyzer 🥳🎉")
    gr.Markdown("This app analyzes how cool your music taste is. We dare you to take this challenge!")
    with gr.Row():
        get_started_btn = gr.Button("Get Started")
    with gr.Row():
        with gr.Column():
            with gr.Row():
                with gr.Column():
                    hm_plot = gr.Plot().style(container=True)
                with gr.Column():
                    plot = gr.ScatterPlot(show_label=False).style(container=True)
            with gr.Row():
                with gr.Column():
                    plot = gr.ScatterPlot(show_label=False).style(container=True)
                with gr.Column():
                    plot = gr.ScatterPlot(show_label=False).style(container=True)
    with gr.Row():
        gr.Markdown(" ### We have recommendations for you!")
    with gr.Row():
        gr.Dataframe(
            headers=["Song", "Album", "Artist"],
            datatype=["str", "str", "str"],
            label="Reccomended Songs",
            value=[["something", "something", "something"], ["something", "something", "something"]]  # TODO: replace with actual reccomendations once get_started() is implemeted.
        )
    demo.load(fn=scatter_plot_fn, outputs=plot)
    demo.load(fn=heatmap_plot_fn, output=hm_plot)


gradio_app = gr.mount_gradio_app(app, demo, "/gradio")
uvicorn.run(app, host="0.0.0.0", port=7860)