Update app.py

app.py

@@ -30,12 +30,12 @@ def analyze_wod(file_obj, wod_type):
     if wod_type == "-- WOD type --" or wod_type is None:
         # Show warning dialog and return empty DataFrame
         gr.Warning("Please select a WOD type first!")
-        return pd.DataFrame()
+        return "", pd.DataFrame()
     
     # Check if file is uploaded
     if file_obj is None:
         gr.Warning("Please upload a PDF file first!")
-        return pd.DataFrame()
+        return "", pd.DataFrame()
     
     print(f"Analyzing '{file_obj.name}' (Type: {wod_type})...")
     
@@ -53,6 +53,7 @@ def analyze_wod(file_obj, wod_type):
         
         # Process the document using the API
         api_response = process_wod_document(temp_file_path, wod_type)
+
         # api_response = json.loads(output_test) 
         
         # Clean up temporary file if we created one
@@ -63,7 +64,7 @@ def analyze_wod(file_obj, wod_type):
         if api_response.get("status") != "success":
             error_msg = api_response.get("message", "Unknown error occurred")
             gr.Error(f"API Error: {error_msg}")
-            return pd.DataFrame()
+            return "", pd.DataFrame()
         
         # Parse the API response
         results = api_response.get("results", {})
@@ -92,17 +93,20 @@ def analyze_wod(file_obj, wod_type):
             "Status": statuses
         })
         
-        # Show success message with prediction
+        # Get prediction for display
         prediction = results.get("prediction", "Unknown")
         gr.Info(f"Analysis completed! Overall prediction: {prediction}")
         
-        return df
+        # Format prediction as centered H1 for display
+        prediction_display = f"<h1 style='text-align: center; color: #1f77b4;'>{prediction}</h1>" if prediction != "Unknown" else ""
+        
+        return prediction_display, df
         
     except Exception as e:
         error_msg = f"Error processing document: {str(e)}"
         print(error_msg)
         gr.Error(error_msg)
-        return pd.DataFrame()
+        return "", pd.DataFrame()
 
 # --- Gradio User Interface Definition ---
 # Using gr.Blocks() for a custom layout that matches the elegant design.
@@ -140,6 +144,9 @@ with gr.Blocks(
     gr.Markdown("---")
     gr.Markdown("## Results")
     
+    # Prediction display (centered H1)
+    prediction_output = gr.Markdown(value="", visible=True)
+    
     # DataFrame to display the output, with styling for the 'Status' column
     results_output = gr.DataFrame(
         headers=["Requirement", "Reason", "Status"],
@@ -154,7 +161,7 @@ with gr.Blocks(
     analyze_btn.click(
         fn=analyze_wod,
         inputs=[file_input, type_input],
-        outputs=[results_output]
+        outputs=[prediction_output, results_output]
     )
 
 # --- Launch the Application with Authentication ---