app.py

import streamlit as st
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.sequence import pad_sequences
import joblib
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler

def load_models():
    # Load the tokenizer (ensure it's the one used during training)
    tokenizer = joblib.load('tokenizer.pkl')

    # Load the emotion prediction model
    emotion_model = load_model('lstm_model.h5')

    # Load the dataset
    df = pd.read_csv('df1.csv')
    df = df.drop(['Unnamed: 0', 'lyrics_filename', 'analysis_url', 'track_href', "type", "id", "uri"], axis=1)

    # Preprocess for content-based
    audio_feature_columns = ['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness',
           'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo',
           'duration_ms', 'time_signature']

    audio_features = df[audio_feature_columns]
    mood_cats = df[['mood_cats']]
    mood_cats_df = pd.DataFrame(mood_cats)

    # Normalize audio features for content-based
    scaler_cb = StandardScaler()
    audio_features_scaled_cb = scaler_cb.fit_transform(audio_features)
    audio_features_df_cb = pd.DataFrame(audio_features_scaled_cb, columns=audio_feature_columns)
    combined_features = pd.concat([mood_cats, audio_features_df_cb], axis=1)

    # Load the similarity matrix for content-based
    similarity_matrix = np.load('similarity_matrix.npy')

    # Load the content-based recommendation function
    recommend_cont = joblib.load('recommendation_cont_function.joblib')

    # Preprocessing for KNN
    scaler_knn = StandardScaler()
    audio_features_scaled_knn = scaler_knn.fit_transform(audio_features)
    audio_features_df_knn = pd.DataFrame(audio_features_scaled_knn, columns=audio_feature_columns)
    combined_features = pd.concat([mood_cats_df, audio_features_df_knn], axis=1)

    # Load the KNN model
    knn = joblib.load('knn_model.joblib')

    # Load the KNN recommendation function
    recommend_knn = joblib.load('recommendation_knn_function.joblib')

    # Load the hybrid recommendation function
    hybrid_recommendation = joblib.load('hybrid_recommendation_function.joblib')

    return tokenizer, emotion_model, df, audio_feature_columns, combined_features_cb, similarity_matrix, recommend_cont, mood_cats_df, audio_features_df_knn, combined_features_knn, knn, recommend_knn, hybrid_recommendation

def main():
    # Set up the title of the app
    st.title('Emotion and Audio Feature-based Song Recommendation System')

    # Load models and data
    tokenizer, emotion_model, df, audio_feature_columns, combined_features_cb, similarity_matrix, recommend_cont, mood_cats_df, audio_features_df_knn, combined_features_knn, knn, recommend_knn, hybrid_recommendation = load_models()

    # Get data from index 0
    query_data = df.iloc[0]

    # Process the lyrics
    sequence = tokenizer.texts_to_sequences([query_data['lyrics']])
    padded_sequence = pad_sequences(sequence, maxlen=50)
    emotion = emotion_model.predict(padded_sequence).flatten()

    # Mapping emotion predictions to encoded categories
    emotion_mapping = {0: 'happy', 1: 'sad', 2: 'calm', 3: 'anger'}
    encoded_emotion = np.argmax(emotion)  
    emotion_category = emotion_mapping[encoded_emotion]

    # Combine emotion and audio features for recommendation
    combined_features_hybrid = np.concatenate([encoded_emotion, query_data[audio_feature_columns].values])

    # Generate recommendations using the hybrid model
    hybrid_recs = hybrid_recommendation(song_index=0)

    st.write("Emotion Detected:", emotion[0])
    st.header('Recommended Songs (Hybrid)')
    st.write(hybrid_recs)

if __name__ == '__main__':
    main()