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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)
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