Update app.py

app.py

@@ -8,7 +8,7 @@ import zipfile
 # Cache the model loading
 @st.cache_resource
 def load_model():
-    # The shareable link to your Google Drive file
+    # Saved model link
     url = "https://drive.google.com/uc?id=1m9YVs0cBRT3-j98rn7d_0DT7jwB_EXPu"
     output = 'model.zip'
     gdown.download(url, output, quiet=False)
@@ -19,7 +19,7 @@ def load_model():
     model = tf.keras.models.load_model('best_model')
     return model
 
-# Cache the JSON data loading
+# Cache the JSON
 @st.cache_data
 def load_json(filename):
     with open(filename, 'r') as f:
@@ -58,23 +58,20 @@ def predict_rating(reviewerID, itemID, model):
     prediction = model.predict(prediction_inputs)
     return prediction.item()
 
-# Load the model
 model = load_model()
 
-# Streamlit app interface
+# App interface
 st.title('Music Rating Prediction - Amazon Review')
 
 # Example input values
 example_reviewerID = "61658"  # Example reviewerID
 example_itemID = "5000"  # Example itemID
 
-# User inputs with examples
-# reviewerID = st.text_input('Reviewer ID', value=example_reviewerID)
-# itemID = st.text_input('Item ID', value=example_itemID)
-reviewerID = st.text_input('Reviewer ID')
-itemID = st.text_input('Item ID')
+# Inputs
+reviewerID = st.text_input('Reviewer ID', value=example_reviewerID)
+itemID = st.text_input('Item ID', value=example_itemID)
 
-# Predict button
+# Button
 if st.button('Predict Rating'):
     prediction = predict_rating(reviewerID, itemID, model)
     st.write(f'Predicted Rating: {prediction:.2f} ⭐')