import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
import sounddevice as sd
import soundfile as sf
import numpy as np
import requests
import webbrowser
import os

# Load pre-trained Wav2Vec2 model and tokenizer
model_name = "facebook/wav2vec2-large-xlsr-53"  # Model name for audio processing
tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name)
model = Wav2Vec2ForCTC.from_pretrained(model_name)

# Function to record audio
def record_audio(duration=5, fs=16000):
    print("Recording...")
    audio = sd.rec(int(duration * fs), samplerate=fs, channels=1, dtype='float32')
    sd.wait()  # Wait until recording is finished
    print("Recording finished.")
    return audio.flatten()

# Function for emotion recognition
def recognize_emotion(audio):
    # Normalize audio if necessary (check your audio data properties if required)
    input_values = tokenizer(audio, return_tensors='pt', padding='longest', sampling_rate=16000).input_values

    # Get the logits (raw predictions) and apply softmax to get probabilities
    with torch.no_grad():
        logits = model(input_values).logits
        predicted_ids = torch.argmax(logits, dim=-1)

    # Decode the predicted IDs to text
    transcription = tokenizer.decode(predicted_ids[0])

    return transcription  # Return the detected text

# Function to get Spotify playlist based on mood
def get_playlist(mood):
    url = "https://unsa-unofficial-spotify-api.p.rapidapi.com/search"
    querystring = {"query": mood, "count": "10", "type": "playlists"}
    headers = {
        'x-rapidapi-key': "your-api-key",  # Replace with your actual API key
        'x-rapidapi-host': "unsa-unofficial-spotify-api.p.rapidapi.com"
    }

    try:
        response = requests.get(url, headers=headers, params=querystring)
        response.raise_for_status()  # Raises error for bad responses
        playlist_id = response.json()["Results"][0]["id"]  # Get the first playlist
        return playlist_id
    except requests.exceptions.RequestException as e:
        print(f"Error fetching playlist data: {e}")
        return None

# Function to open the Spotify playlist in a web browser
def open_playlist(playlist_id):
    webbrowser.open(f'https://open.spotify.com/playlist/{playlist_id}')

# Main function to record audio and recognize mood
def main():
    # Record audio
    audio = record_audio()

    # Recognize the mood/emotion from audio
    emotion_text = recognize_emotion(audio)
    print(f"Detected Emotion: {emotion_text}")

    # Get Spotify playlist based on the detected emotion
    playlist_id = get_playlist(emotion_text)
    if playlist_id:
        open_playlist(playlist_id)

if __name__ == "__main__":
    main()