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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') | |
# Load the scaler for KNN | |
scaler_knn = StandardScaler() | |
# Function for hybrid recommendation | |
def hybrid_recommendation(song_index): | |
# Get data for the query song | |
query_data = df.iloc[song_index] | |
# Process the lyrics for emotion prediction using LSTM | |
sequence = tokenizer.texts_to_sequences([query_data['lyrics']]) | |
padded_sequence = pad_sequences(sequence, maxlen=50) | |
predicted_emotion = emotion_model.predict(padded_sequence).flatten() | |
# Preprocess for KNN | |
audio_features_knn = query_data[['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness', | |
'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', | |
'duration_ms', 'time_signature']].values.reshape(1, -1) | |
mood_cats = query_data[['mood_cats']] | |
mood_cats_df = pd.DataFrame(mood_cats) | |
audio_features_scaled_knn = scaler_knn.fit_transform(audio_features_knn) | |
combined_features = pd.concat([mood_cats_df, pd.DataFrame(audio_features_scaled_knn, columns=audio_features_knn.columns)], axis=1) | |
# Predict using the KNN model | |
knn_recommendations = knn_model.kneighbors(combined_features, n_neighbors=5, return_distance=False)[0] | |
# Mapping emotion predictions to encoded categories | |
emotion_mapping = {0: 'happy', 1: 'sad', 2: 'calm', 3: 'anger'} | |
encoded_emotion = np.argmax(predicted_emotion) | |
emotion_category = emotion_mapping[encoded_emotion] | |
# Compute cosine similarity for content-based recommendation | |
features_for_similarity = df[['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness', | |
'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', | |
'duration_ms', 'time_signature']].values | |
cosine_similarities = cosine_similarity([emotion_category], features_for_similarity).flatten() | |
# Combine recommendations from both models | |
combined_indices = np.argsort(-np.concatenate([knn_recommendations, cosine_similarities])) | |
hybrid_recs_sorted = combined_indices[:5] # Select top 5 recommendations | |
return hybrid_recs_sorted | |
# 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 hybrid recommendations | |
hybrid_recs = hybrid_recommendation(song_index_to_recommend) | |
# Display the recommendations | |
st.write("Hybrid Recommendations:") | |
for index in hybrid_recs: | |
st.write(f"Song Index: {index}, Title: {df.iloc[index]['title']}, Artist: {df.iloc[index]['artist']}") | |