Update app.py

app.py

@@ -62,6 +62,9 @@ encoder = LabelEncoder()
 df["Case Problem Encoded"] = encoder.fit_transform(df["Case Problem"])
 df["Feedback Encoded"] = encoder.fit_transform(df["Feedback"])
 
+# ✅ Save encoder for later use
+joblib.dump(encoder, "case_problem_encoder.pkl")
+
 # ✅ Train Model
 X = df[["Case Problem Encoded"]]
 y = df["Feedback Encoded"]
@@ -73,9 +76,6 @@ model.fit(X_train, y_train)
 joblib.dump(model, "feedback_model.pkl")
 print("✅ Model trained successfully!")
 
-# ✅ Save encoder classes for future use
-joblib.dump(encoder, "case_problem_encoder.pkl")
-
 # ✅ API Input Model
 class PredictionInput(BaseModel):
     case_problem: str
@@ -86,11 +86,19 @@ async def predict_feedback(data: PredictionInput):
     if model is None:
         return {"error": "Model is not trained yet."}
 
+    # ✅ Load encoder
+    encoder = joblib.load("case_problem_encoder.pkl")
+
     # ✅ Convert input to lowercase to match training data
     case_problem_lower = data.case_problem.lower()
 
+    # ✅ Check if input exists in training data
     if case_problem_lower not in df["Case Problem"].values:
-        return {"error": "Invalid case problem. Please enter a valid category from the dataset."}
+        valid_problems = list(df["Case Problem"].unique())  # Get valid options
+        return {
+            "error": f"Invalid case problem. Please enter a valid category.",
+            "Valid Categories": valid_problems
+        }
 
     try:
         case_problem_encoded = encoder.transform([case_problem_lower])
@@ -100,16 +108,17 @@ async def predict_feedback(data: PredictionInput):
     except Exception as e:
         return {"error": str(e)}
 
-# ✅ Gradio UI with async execution
+# ✅ Gradio UI with suggestions for valid categories
 def gradio_interface(case_problem):
     if model is None:
         return "Model not trained yet."
 
-    # ✅ Convert input to lowercase for consistency
+    encoder = joblib.load("case_problem_encoder.pkl")
     case_problem_lower = case_problem.lower()
 
     if case_problem_lower not in df["Case Problem"].values:
-        return "Invalid case problem. Please enter a valid category from the dataset."
+        valid_problems = ", ".join(df["Case Problem"].unique())
+        return f"Invalid case problem. Please enter a valid category. Options: {valid_problems}"
 
     try:
         case_problem_encoded = encoder.transform([case_problem_lower])
@@ -126,9 +135,9 @@ def start_app():
         fn=gradio_interface, 
         inputs="text", 
         outputs="text", 
-        live=True  # ✅ Ensures Gradio UI updates properly
+        live=True  
     )
-    gr_interface.launch(share=True, debug=True)  # ✅ Debugging enabled to see errors
+    gr_interface.launch(share=True, debug=True)  # ✅ Debugging enabled
     uvicorn.run(app, host="0.0.0.0", port=8000)
 
 if __name__ == "__main__":