Update app.py

app.py

@@ -1,10 +1,12 @@
 import streamlit as st
-#import torch
-# Import your model classes here
+from tensorflow.keras.models import load_model
+import joblib
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
 
 # Load your models
-emotion_model = 'lstm_model.h5'
-recommender_model = 'knn_model.npy'
+emotion_model = load_model('lstm_model.h5')
+recommender_model = joblib.load('knn_model.npy')
 
 st.title("Emotion-based Song Recommender")
 
@@ -12,11 +14,18 @@ st.title("Emotion-based Song Recommender")
 lyrics = st.text_area("Enter lyrics here:")
 
 if st.button("Recommend Songs"):
+    
     if lyrics:
         # Predict emotion
-        emotion = emotion_model.predict(lyrics)
+        # Here, ensure that the input shape and preprocessing of lyrics
+        # match the requirements of your LSTM model
+        sequence = tokenizer.texts_to_sequences([lyrics])
+        padded_sequence = pad_sequences(sequence, maxlen=128)
+        emotion = emotion_model.predict(padded_sequence)  # Adjust this as per your model's requirement
 
         # Get song recommendations
+        # The recommend method should be defined as part of your KNN model
+        # or as a separate function that uses the KNN model
         recommendations = recommender_model.recommend(emotion, ...)
 
         st.write("Emotion Detected:", emotion)