Update app.py

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 import joblib
 
-# Load models (Make sure to change the paths to where your models are stored)
+# Load models (Ensure the correct paths to where your models are stored)
 logreg_model = joblib.load('best_lr_model.pkl')
 knn_model = joblib.load('best_knn_model.pkl')
 svc_model = joblib.load('best_svc_model.pkl')
@@ -9,6 +9,10 @@ rf_model = joblib.load('best_rf_model.pkl')
 
 # Prediction function
 def predict_sentiment(review_text, model_name):
+    # Reshape input to match expected 2D array format (single sample, single feature)
+    review_text_reshaped = [review_text]  # This ensures the input is in a 2D array (1 sample, N features)
+    
+    # Select the appropriate model
     if model_name == "Logistic Regression":
         model = logreg_model
     elif model_name == "K-Nearest Neighbors":
@@ -17,16 +21,22 @@ def predict_sentiment(review_text, model_name):
         model = svc_model
     elif model_name == "Random Forest":
         model = rf_model
+    else:
+        return "Model not found", {}
 
     # Assuming the models support `predict_proba` for probabilities
-    prob = model.predict_proba([review_text])  # Adjust according to your preprocessing steps
-    positive_prob = prob[0][1]
-    negative_prob = prob[0][0]
-    
-    # Predict the sentiment class (based on the highest probability)
-    predicted_class = "Positive Feedback" if positive_prob > negative_prob else "Negative Feedback"
-    
-    return predicted_class, {"Positive Comment": f"{positive_prob * 100:.2f}%", "Negative Comment": f"{negative_prob * 100:.2f}%"}
+    try:
+        prob = model.predict_proba(review_text_reshaped)  # Adjust according to your preprocessing steps
+        positive_prob = prob[0][1]
+        negative_prob = prob[0][0]
+        
+        # Predict the sentiment class (based on the highest probability)
+        predicted_class = "Positive Feedback" if positive_prob > negative_prob else "Negative Feedback"
+        
+        return predicted_class, {"Positive Comment": f"{positive_prob * 100:.2f}%", "Negative Comment": f"{negative_prob * 100:.2f}%"}
+
+    except Exception as e:
+        return f"Error: {e}", {}
 
 # Gradio interface setup
 def create_interface():
@@ -55,4 +65,4 @@ def create_interface():
     return interface
 
 # Launch the interface
-create_interface().launch()
+create_interface().launch(share=True)  # Add share=True to create a public link