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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 | |
# Load the 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') | |
# 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) | |
# Load the similarity matrix | |
similarity_matrix = np.load('similarity_matrix.npy') | |
# Load the content-based recommendation function | |
recommend_cont = joblib.load('recommendation_function.joblib') | |
# Load the hybrid recommendation function | |
hybrid_recommendation = joblib.load('hybrid_recommendation_function.joblib') | |
# Load the content-based recommendation function | |
recommend_cont = joblib.load('recommendation_cont_function.joblib') | |
# Load the KNN model | |
knn = joblib.load('knn_model.joblib') | |
# Load the KNN recommendation function | |
recommend_knn = joblib.load('recommendation_knn_function.joblib') | |
# Set up the title of the app | |
st.title('Emotion and Audio Feature-based Song Recommendation System') | |
# 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) # 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, 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) | |