Update app.py

app.py

@@ -5,19 +5,17 @@ import joblib
 import pandas as pd
 import numpy as np
 
-# Load your models
+# Load your emotion prediction model
 emotion_model = load_model('lstm_model.h5')
 
-# Save the scikit-learn model
-joblib.dump(knn_model, 'knn_model.pkl')
-# Load the model
+# Load the KNN recommender model
 recommender_model = joblib.load('knn_model.pkl')
 
-# Load the tokenizer (if used during training)
-# tokenizer = joblib.load('tokenizer.pkl')  # Update with actual file name
+# Load the tokenizer (ensure it's the one used during training)
+# tokenizer = joblib.load('tokenizer.pkl')  # Update this to the correct path
 
 # Load the dataset
-df = pd.read_csv('df1.csv')  # Make sure this is the correct DataFrame
+df = pd.read_csv('df1.csv')  # Ensure this is the correct DataFrame
 
 # Set up the title of the app
 st.title('Emotion and Audio Feature-based Song Recommendation System')
@@ -29,19 +27,20 @@ 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
+for feature_name in df.columns:  # Ensure this matches your DataFrame's structure
     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=128)
-        emotion = emotion_model.predict(padded_sequence).flatten()  # Flatten if needed
+        emotion = emotion_model.predict(padded_sequence).flatten()
 
         # Combine emotion and audio features for recommendation
-        combined_features = np.concatenate([[emotion], audio_features])
+        combined_features = np.concatenate([emotion, audio_features])  # Ensure the concatenation logic matches your model's expectation
 
         # Generate recommendations using the KNN model
         distances, indices = recommender_model.kneighbors([combined_features], n_neighbors=5)
@@ -51,6 +50,6 @@ if st.button('Predict Emotion and Recommend Songs'):
         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
+            st.write(song)  # Customize this to display relevant song info
     else:
-        st.error("Please fill in all the fields.")
+        st.error("Please fill in all the fields.")