import os
import spotipy
import gradio as gr
import pandas as pd

from get_scaler import get_scaler
from dotenv import load_dotenv
from recommendations import recommend_songs
from spotipy.oauth2 import SpotifyClientCredentials

# Load environment variables from .env file
load_dotenv()

# Access the Spotify API credentials
client_id = os.getenv('SPOTIFY_CLIENT_ID')
client_secret = os.getenv('SPOTIFY_CLIENT_SECRET')

# Authenticate with the Spotify API
sp = spotipy.Spotify(auth_manager=SpotifyClientCredentials(client_id=client_id, client_secret=client_secret))

# data = pd.read_csv("kmeans_clustered_spotify_dataset.csv")
data = get_scaler()[0]

def fetch_song_cover(song_name):
    # Search for the song
    results = sp.search(q=song_name, limit=1, type='track')
    if results['tracks']['items']:
        song = results['tracks']['items'][0]
        cover_url = song['album']['images'][0]['url']
        user_song_name = song['name']

        user_song = song['name']

        return cover_url, song['name'], song['artists'][0]['name']
    else:
        return None, "Song not found", "Artist not found"

def get_recommendations(song_name):
  suggestions = recommend_songs(song_list=[{'name': song_name}], spotify_data=data)
  song_covers = []

  for suggestion in suggestions:
    print(suggestion)
    cover = fetch_song_cover(suggestion["name"])
    song_covers.append(cover[0])

  return song_covers

# Gradio Interface
def gradio_interface(song_name):
    cover_url, song_name, artist_name = fetch_song_cover(song_name)

    if cover_url:
        return cover_url, f"Song: {song_name}", f"Artist: {artist_name}", gr.update(visible=True), gr.update(visible=True)
    else:
        return None, "Song not found", "Artist not found"

# Creating Gradio Interface
with gr.Blocks() as demo:
    gr.Markdown("# Music Recommendation System using Spotify Dataset")

    with gr.Row():
        with gr.Column():
            song_input = gr.Textbox(label="Enter Song Name")
            search_button = gr.Button("Find Song")

        with gr.Column():
            cover_output = gr.Image(label="Cover Image")
            song_name_output = gr.Textbox(label="Song Name")
            artist_name_output = gr.Textbox(label="Artist Name")

    recommendations_labels = gr.Row(visible=False)
    
    recommendations_songs = gr.Column(visible=False)

    with recommendations_labels:
      gr.Markdown("# You may also like")

    with recommendations_songs: 
      song_covers = gr.Gallery(label="Image Gallery")

    search_button.click(fn=gradio_interface, 
                        inputs=song_input, 
                        outputs=[cover_output, song_name_output, artist_name_output, recommendations_labels, recommendations_songs]).then(
                                  fn=get_recommendations,
                                  inputs=song_input,
                                  outputs=song_covers
                              )

# Launching the Gradio app
demo.launch(debug=True)