Update app.py

app.py

@@ -13,11 +13,11 @@ rf_model = joblib.load('best_rf_model.pkl')
 vectorizer = joblib.load('vectorizer.pkl')
 
 # Define the function to predict sentiment
-def predict_sentiment(review_text, model_name):
+def predict_sentiment(review_text, model_name, positive_threshold=0.6):
     # Preprocess the review text using the same vectorizer used during training
     transformed_text = vectorizer.transform([review_text])  # This is sparse matrix
 
-    # Convert sparse matrix to dense (necessary for SVM)
+    # Convert sparse matrix to dense (necessary for SVM and other models)
     transformed_text_dense = transformed_text.toarray()  # Convert sparse matrix to dense array
 
     # Choose model based on the dropdown selection
@@ -33,8 +33,16 @@ def predict_sentiment(review_text, model_name):
     # Make predictions and get probabilities
     if hasattr(model, 'predict_proba'):
         prob = model.predict_proba(transformed_text_dense)  # Use dense matrix here
+        sentiment_prob = prob[0][1]  # Probability of positive sentiment
         sentiment = model.predict(transformed_text_dense)[0]
-        probability = f"Positive Comment: {prob[0][1]*100:.2f}%, Negative Comment: {prob[0][0]*100:.2f}%"
+
+        # Apply threshold to determine sentiment class
+        if sentiment_prob >= positive_threshold:
+            sentiment_class = "Positive"
+            probability = f"Positive Comment: {sentiment_prob*100:.2f}%, Negative Comment: {(1-sentiment_prob)*100:.2f}%"
+        else:
+            sentiment_class = "Negative"
+            probability = f"Positive Comment: {sentiment_prob*100:.2f}%, Negative Comment: {(1-sentiment_prob)*100:.2f}%"
     else:
         sentiment = model.predict(transformed_text_dense)[0]
         probability = "Model does not support probability estimation."