app.py

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

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

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

# Load the tokenizer
tokenizer = joblib.load('tokenizer.pkl')

# Load the dataset
df = pd.read_csv('df1.csv')

# 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_cb = pd.concat([mood_cats, audio_features_df_cb], axis=1)

# 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_knn = pd.concat([mood_cats_df, audio_features_df_knn], axis=1)

# Function for content-based recommendation
def recommend_cont(song_index, num_recommendations=5):
    song_similarity = similarity_matrix[song_index]
    # Get indices and similarity scores of top similar songs
    similar_songs = sorted(list(enumerate(song_similarity)), key=lambda x: x[1], reverse=True)[1:num_recommendations+1]
    recommended_song_indices = [idx for idx, similarity in similar_songs]
    recommended_songs = df.iloc[recommended_song_indices].copy()
    recommended_songs['score'] = [similarity for idx, similarity in similar_songs]
    return recommended_songs

# Function for KNN-based recommendation
def recommend_knn(query_index, n_recommendations=5):
    distances, indices = knn_model.kneighbors(combined_features_knn.iloc[query_index].values.reshape(1, -1), n_neighbors=n_recommendations)
    recommended_songs = df.iloc[indices.flatten()].copy()
    # Convert distances to scores
    recommended_songs['score'] = 1 / (1 + distances.flatten())  # Inverse of distance
    return recommended_songs

# Function for hybrid recommendation
def hybrid_recommendation(song_index, top_n=10):
    # Get recommendations from both models
    content_based_recs = recommend_cont(song_index, top_n)
    knn_based_recs = recommend_knn(song_index, top_n)

    # Combine recommendations
    combined_recs = pd.concat([content_based_recs, knn_based_recs])

    # Group by song index (or identifier) and average scores
    hybrid_recs = combined_recs.groupby(combined_recs.index).mean().sort_values(by='score', ascending=False).head(top_n)

    return hybrid_recs

# Set up the title of the app
st.title('Hybrid Recommender App')

# Get song index from user input
song_index_to_recommend = st.number_input('Enter song index:', min_value=0, max_value=len(df)-1, value=0)

# Get lyrics for emotion prediction
lyrics = df.iloc[song_index_to_recommend]['lyrics']

# Process the lyrics
sequence = tokenizer.texts_to_sequences([lyrics])
padded_sequence = pad_sequences(sequence, maxlen=50)  # Adjust the maxlen to match the expected input size
emotion = emotion_model.predict(padded_sequence).flatten()

# Combine emotion and audio features for recommendation
combined_features = np.concatenate([emotion, audio_features_scaled_knn[song_index_to_recommend]])

# Get hybrid recommendations
hybrid_recs = hybrid_recommendation(song_index_to_recommend)

# Display the predicted emotion and recommendations
st.write(f"Predicted Emotion: {emotion}")
st.write("Hybrid Recommendations:")
for index in hybrid_recs.index:
    st.write(f"Song Index: {index}, Title: {df.iloc[index]['title']}, Artist: {df.iloc[index]['artist']}, Score: {hybrid_recs.loc[index, 'score']}")