Update app.py

app.py

@@ -57,12 +57,12 @@ for col in required_columns:
 # ✅ Convert "Case Problem" column to lowercase and remove spaces for consistency
 df["Case Problem"] = df["Case Problem"].astype(str).str.strip().str.lower()
 
-# ✅ Train and save LabelEncoder to handle unseen labels correctly
+# ✅ Train and save LabelEncoder
 encoder = LabelEncoder()
 df["Case Problem Encoded"] = encoder.fit_transform(df["Case Problem"])
 df["Feedback Encoded"] = encoder.fit_transform(df["Feedback"])
 
-# ✅ Save encoder to use it later
+# ✅ Save encoder
 joblib.dump(encoder, "case_problem_encoder.pkl")
 
 # ✅ Train Model
@@ -72,7 +72,7 @@ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_
 model = RandomForestClassifier(n_estimators=100, random_state=42)
 model.fit(X_train, y_train)
 
-# ✅ Save trained model
+# ✅ Save model
 joblib.dump(model, "feedback_model.pkl")
 print("✅ Model trained successfully!")
 
@@ -108,7 +108,7 @@ async def predict_feedback(data: PredictionInput):
     except Exception as e:
         return {"error": str(e)}
 
-# ✅ Gradio UI with better handling
+# ✅ Gradio UI with Submit button
 def gradio_interface(case_problem):
     if model is None:
         return "Model not trained yet."
@@ -128,14 +128,15 @@ def gradio_interface(case_problem):
     except Exception as e:
         return f"Error: {str(e)}"
 
-# ✅ Start both API & Gradio
+# ✅ Start API & Gradio with Submit button
 def start_app():
     """ Start API and Gradio Interface """
     gr_interface = gr.Interface(
         fn=gradio_interface, 
-        inputs="text", 
-        outputs="text", 
-        live=True  
+        inputs=gr.Textbox(label="Enter Case Problem"),
+        outputs=gr.Textbox(label="Predicted Feedback"),
+        live=False,  # ✅ Submit button enabled
+        allow_flagging="never",
     )
     gr_interface.launch(share=True, debug=True)  # ✅ Debugging enabled
     uvicorn.run(app, host="0.0.0.0", port=8000)