Update app.py

app.py

@@ -406,46 +406,73 @@ with gr.Blocks(css=css_tech_theme) as demo:
         with gr.TabItem("📤 Submission"):
             with gr.Markdown("""
             <div class="submission-section">
-            <h2>Submit Your Predictions</h2>
-            <p>Upload your prediction file and provide your model name to evaluate and submit to the leaderboard.</p> </div>"""):
+                <h2>Submit Your Predictions</h2>
+                <p>Upload your prediction file and provide your model name to evaluate and submit to the leaderboard.</p>
+            </div>
+            """):
                 with gr.Row(elem_id="submission-fields"):
                     file_input = gr.File(label="Upload Prediction CSV", file_types=[".csv"], interactive=True)
                     model_name_input = gr.Textbox(label="Model Name", placeholder="Enter your model name")
+        
                 with gr.Row(elem_id="submission-results"):
                     overall_accuracy_display = gr.Number(label="Overall Accuracy", interactive=False)
+        
                 with gr.Row(elem_id="submission-buttons"):
                     eval_button = gr.Button("Evaluate")
                     submit_button = gr.Button("Prove and Submit to Leaderboard", visible=False)
                     eval_status = gr.Textbox(label="Evaluation Status", interactive=False)
-            def handle_evaluation(file, model_name):
-                # Check if required inputs are provided
-                if not file:
-                    return "Error: Please upload a prediction file.", 0, gr.update(visible=False)
-                if not model_name or model_name.strip() == "":
-                    return "Error: Please enter a model name.", 0, gr.update(visible=False)
-                    # Perform evaluation
+        
+        # Define the functions outside the `with` block
+        def handle_evaluation(file, model_name):
+            # Check if required inputs are provided
+            if not file:
+                return "Error: Please upload a prediction file.", 0, gr.update(visible=False)
+            if not model_name or model_name.strip() == "":
+                return "Error: Please enter a model name.", 0, gr.update(visible=False)
+        
+            try:
+                # Load predictions file
+                predictions_df = pd.read_csv(file.name)
+        
+                # Validate required columns in the prediction file
+                required_columns = ['question_id', 'predicted_answer']
+                missing_columns = [col for col in required_columns if col not in predictions_df.columns]
+                if missing_columns:
+                    return (f"Error: Missing required columns in prediction file: {', '.join(missing_columns)}.",
+                            0, gr.update(visible=False))
+        
+                # Perform evaluation
                 status, leaderboard = evaluate_predictions(file, model_name, add_to_leaderboard=False)
                 if leaderboard.empty:
                     overall_accuracy = 0
                 else:
                     overall_accuracy = leaderboard.iloc[-1]["Overall Accuracy"]
-                    # Show the submit button after evaluation
+        
+                # Show the submit button after successful evaluation
                 return status, overall_accuracy, gr.update(visible=True)
-            def handle_submission(file, model_name):
-                # Handle leaderboard submission
-                status, _ = evaluate_predictions(file, model_name, add_to_leaderboard=True)
-                return f"Submission to leaderboard completed: {status}"
-            eval_button.click(
-                handle_evaluation,
-                inputs=[file_input, model_name_input],
-                outputs=[eval_status, overall_accuracy_display, submit_button],
-            )
-            submit_button.click(
-                handle_submission,
-                inputs=[file_input, model_name_input],
-                outputs=[eval_status],
-            )
-    
+        
+            except Exception as e:
+                # Handle unexpected errors
+                return f"Error during evaluation: {str(e)}", 0, gr.update(visible=False)
+        
+        def handle_submission(file, model_name):
+            # Handle leaderboard submission
+            status, _ = evaluate_predictions(file, model_name, add_to_leaderboard=True)
+            return f"Submission to leaderboard completed: {status}"
+        
+        # Connect button clicks to the functions
+        eval_button.click(
+            handle_evaluation,
+            inputs=[file_input, model_name_input],
+            outputs=[eval_status, overall_accuracy_display, submit_button],
+        )
+        
+        submit_button.click(
+            handle_submission,
+            inputs=[file_input, model_name_input],
+            outputs=[eval_status],
+        )
+
 
 
         with gr.TabItem("🏅 Leaderboard"):