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| 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.metrics.pairwise import cosine_similarity | |
| # Check if scikit-learn is installed | |
| try: | |
| import sklearn | |
| st.write("scikit-learn is installed.") | |
| except ImportError: | |
| st.error("scikit-learn is not installed.") | |
| # Load your emotion prediction model | |
| emotion_model = load_model('lstm_model.h5') | |
| # Load the KNN recommender model | |
| try: | |
| recommender_model = joblib.load('knn_model.pkl') | |
| except Exception as e: | |
| st.error(f"Error loading KNN model: {e}") | |
| # Load the tokenizer (ensure it's the one used during training) | |
| tokenizer = joblib.load('tokenizer.pkl') # Correct the path | |
| # Load the dataset and preprocess | |
| df = pd.read_csv('df1.csv') | |
| df = df.drop(['Unnamed: 0', 'lyrics_filename', 'analysis_url', 'track_href', "type", "id", "uri", 'mood'], axis=1) | |
| # Set up the title of the app | |
| st.title('Emotion and Audio Feature-based Song Recommendation System') | |
| # Input field for lyrics | |
| st.header('Enter Song Lyrics') | |
| lyrics = st.text_area("Input the lyrics of the song here:") | |
| # Input fields for audio features | |
| st.header('Enter Audio Features') | |
| audio_features = [] | |
| # Display only relevant columns for audio features | |
| audio_feature_columns = ['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness', 'acousticness', | |
| 'instrumentalness', 'liveness', 'valence', 'tempo'] | |
| for feature_name in audio_feature_columns: | |
| feature = st.number_input(f"Enter value for {feature_name}:", step=0.01) | |
| audio_features.append(feature) | |
| # Predict and Recommend button | |
| if st.button('Predict Emotion and Recommend Songs'): | |
| if lyrics and all(audio_features): | |
| # 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]) | |
| # Generate recommendations using the KNN model | |
| knn_distances, knn_indices = recommender_model.kneighbors([combined_features], n_neighbors=5) | |
| knn_recommended_songs = df.iloc[knn_indices.flatten()] | |
| st.write("Emotion Detected:", emotion[0]) | |
| st.header('Recommended Songs (KNN)') | |
| for _, song in knn_recommended_songs.iterrows(): | |
| st.write(song) | |
| else: | |
| st.error("Please fill in all the fields.") |