Update app.py

app.py

@@ -5,6 +5,7 @@ import joblib
 import pandas as pd
 import numpy as np
 
+# Check if scikit-learn is installed
 try:
     import sklearn
     st.write("scikit-learn is installed.")
@@ -14,9 +15,6 @@ except ImportError:
 # Load your emotion prediction model
 emotion_model = load_model('lstm_model.h5')
 
-# Load the KNN recommender model
-#recommender_model = joblib.load('knn_model.pkl')
-
 # Load the KNN recommender model
 try:
     recommender_model = joblib.load('knn_model.pkl')
@@ -24,16 +22,13 @@ except Exception as e:
     st.error(f"Error loading model: {e}")
 
 # Load the tokenizer (ensure it's the one used during training)
- tokenizer = joblib.load('tokenizer.pkl')  # Update this to the correct path    
+tokenizer = joblib.load('tokenizer.pkl')  # Correct the path
 
-# Load the dataset
+# Load the dataset and preprocess
 df = pd.read_csv('df1.csv')  
-#remove null values
+# Optionally, remove null values and drop unwanted columns
 df = df.dropna()
-
-# Drop unwanted columns
-#dropping irrelevant features
-df = df.drop(['Unnamed: 0', 'lyrics_filename',  'analysis_url', 'track_href',"type","id","uri"], axis = 1)
+df = df.drop(['Unnamed: 0', 'lyrics_filename',  'analysis_url', 'track_href', "type", "id", "uri"], axis=1)
 
 # Set up the title of the app
 st.title('Emotion and Audio Feature-based Song Recommendation System')
@@ -45,29 +40,25 @@ 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:  # Ensure this matches your DataFrame's structure
+for feature_name in df.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=128)
         emotion = emotion_model.predict(padded_sequence).flatten()
 
-        # Combine emotion and audio features for recommendation
-        combined_features = np.concatenate([emotion, audio_features])  # Ensure the concatenation logic matches your model's expectation
+        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.write("Emotion Detected:", emotion[0])
         st.header('Recommended Songs')
         for _, song in recommended_songs.iterrows():
-            st.write(song)  # Customize this to display relevant song info
+            st.write(song)
     else:
         st.error("Please fill in all the fields.")