Update app.py

app.py

@@ -1,15 +1,15 @@
 import gradio as gr
 import joblib
 import numpy as np
+from sklearn.feature_extraction.text import CountVectorizer
 
 # Load models (change paths to where your .pkl files 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')
+svc_model = joblib.load('best_svc_model.pkl')  # Ensure this model has 'probability=True' if needed
 rf_model = joblib.load('best_rf_model.pkl')
 
 # Preprocessing: Load the same vectorizer you used during training
-from sklearn.feature_extraction.text import CountVectorizer
 vectorizer = joblib.load('vectorizer.pkl')
 
 # Define the function to predict sentiment
@@ -17,6 +17,7 @@ def predict_sentiment(review_text, model_name):
     # Preprocess the review text using the same vectorizer used during training
     transformed_text = vectorizer.transform([review_text])  # Transform the input review text
 
+    # Choose model based on the dropdown selection
     if model_name == "Logistic Regression":
         model = logreg_model
     elif model_name == "K-Nearest Neighbors":