import gradio as gr
import numpy as np
import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity

import os
import requests

url = 'https://huggingface.co/datasets/sheacon/song_lyrics/resolve/main/v2ga_w_embeddings.parquet'
response = requests.get(url, stream=True)

filename = os.path.join(os.getcwd(), url.split('/')[-1])
with open(filename, 'wb') as file:
    for chunk in response.iter_content(chunk_size=8192):
        if chunk:
            file.write(chunk)

print(f"File '{filename}' has been downloaded to the present working directory.")


pwd = os.getcwd()
print("Present Working Directory:", pwd)

contents = os.listdir(pwd)
print("Contents of the Directory:")
for item in contents:
    print(item)

df = pd.read_csv('v2ga_w_embeddings.parquet')

def get_most_similar_songs(artist, title, df):
    def find_most_similar(embedding_column):
        chosen_song = df[(df['artist'] == artist) & (df['title'] == title)][embedding_column].values
        if len(chosen_song) == 0:
            return None

        chosen_song = chosen_song.reshape(1, -1)
        similarity_matrix = cosine_similarity(df[embedding_column].values.tolist(), chosen_song)
        most_similar_indices = np.argsort(similarity_matrix.flatten())[-5:-1][::-1] # Top 4 excluding the selected song
        return df.iloc[most_similar_indices][['title', 'artist', 'lyrics']].to_dict(orient='records')

    results = {}
    for embedding in ['embedding_glove', 'embedding_minilm', 'embedding_roberta', 'embedding_gpt']:
        most_similar = find_most_similar(embedding)
        if most_similar is None:
            return "Song not found. Please ensure the artist and title are correct."

        results[embedding] = most_similar

    return results

def update_titles_dropdown(artist):
    titles = sorted(df[df['artist'] == artist]['title'].unique())
    return titles

artists = sorted(df['artist'].unique())

artist_dropdown = gr.inputs.Dropdown(artists, label="Artist")
title_dropdown = gr.inputs.Dropdown([], label="Title", updatable=True)

output_interface = gr.outputs.JSON(label="Similar Songs")

iface = gr.Interface(
    fn=get_most_similar_songs,
    inputs=[artist_dropdown, title_dropdown],
    outputs=output_interface,
    examples=[("The Beatles", "Let It Be"), ("Eminem", "Lose Yourself")],
    title="Semantic Song Search: Most Similar Song",
    description="Find the 4 most similar songs to the selected song based on different embeddings (GloVe, MiniLM, RoBERTa, GPT).",
    update=update_titles_dropdown
)

iface.launch()