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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 | |
# Load your models | |
emotion_model = load_model('lstm_model.h5') | |
recommender_model = np.load('knn_model.npy', allow_pickle=True) | |
# Load the tokenizer (if used during training) | |
# tokenizer = joblib.load('tokenizer.pkl') # Update with actual file name | |
# Load the dataset | |
df = pd.read_csv('df1.csv') # Make sure this is the correct DataFrame | |
# 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 = [] | |
for feature_name in df.columns: # Make sure this matches your DataFrame | |
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): | |
sequence = tokenizer.texts_to_sequences([lyrics]) | |
padded_sequence = pad_sequences(sequence, maxlen=128) | |
emotion = emotion_model.predict(padded_sequence).flatten() # Flatten if needed | |
# Combine emotion and audio features for recommendation | |
combined_features = np.concatenate([[emotion], audio_features]) | |
# Generate recommendations using the KNN model | |
distances, indices = recommender_model.kneighbors([combined_features], n_neighbors=5) | |
recommended_songs = df.iloc[indices.flatten()] | |
# Display emotion and recommendations | |
st.write("Emotion Detected:", emotion[0]) # Adjust as per your model's output | |
st.header('Recommended Songs') | |
for _, song in recommended_songs.iterrows(): | |
st.write(song) # Adjust based on your dataset | |
else: | |
st.error("Please fill in all the fields.") | |