Update app.py

app.py

@@ -1,24 +1,24 @@
 import gradio as gr
 import joblib
 
-def predict_sentiment(review, model_type):
-    if model_type == "Logistic Regression":
-        model = joblib.load("logistic_pipeline.pkl")
-    else:  # KNN
-        model = joblib.load("knn_pipeline.pkl")
-    
-    prediction = model.predict([review])[0]
-    probabilities = model.predict_proba([review])[0]
-    proba_positive = probabilities[1]
-    result = f"Sentiment: {'Positive' if prediction else 'Negative'} (Confidence: {proba_positive:.2f} for Positive)"
-    return result
+logistic_pipeline = joblib.load('logistic_pipeline.pkl')
+knn_pipeline = joblib.load('knn_pipeline.pkl')
 
-iface = gr.Interface(fn=predict_sentiment,
-                     inputs=[gr.Textbox(lines=2, label="Enter Review"),
-                             gr.Dropdown(choices=["Logistic Regression", "KNN"], label="Model Type")],
-                     outputs=gr.Textbox(label="Prediction"),
-                     title="Sentiment Analysis",
-                     description="Select a model to predict the sentiment of your review.")
+def predict_class_probabilities(text, model_choice):
+    if model_choice == "Logistic Regression":
+        probabilities = logistic_pipeline.predict_proba([text])[0]
+    else:
+        probabilities = knn_pipeline.predict_proba([text])[0]
+    return {'Positive': probabilities[1], 'Negative': probabilities[0]}
 
-if __name__ == "__main__":
-    iface.launch()
+#Gradio interface
+input_text = gr.Textbox(lines=5, label="Enter your review comment")
+model_choice = gr.Dropdown(choices=["Logistic Regression", "KNN"], label="Choose a model")
+output_probs = gr.Label()
+
+iface = gr.Interface(fn=predict_class_probabilities,
+                     inputs=[input_text, model_choice],
+                     outputs=output_probs,
+                     title="Sentiment Emotion Predictor",
+                     description="Predict sentiment of review (positive or negative).")
+iface.launch()