Update app.py

app.py

@@ -431,41 +431,86 @@ with gr.Blocks(css=css_tech_theme) as demo:
         #         outputs=[eval_status, overall_accuracy_display],
         #     )
 
-        with gr.TabItem("📤 Submission"):
-            with gr.Row():
-                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():
-                overall_accuracy_display = gr.Number(label="Overall Accuracy", interactive=False)
-                eval_button = gr.Button("Evaluate")
-                eval_status = gr.Textbox(label="Evaluation Status", interactive=False)
-                submit_button = gr.Button("Prove and Submit to Leaderboard", visible=False)  # Initially hidden
-            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
-                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
-                    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],)
+        # with gr.TabItem("📤 Submission"):
+        #     with gr.Row():
+        #         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():
+        #         overall_accuracy_display = gr.Number(label="Overall Accuracy", interactive=False)
+        #         eval_button = gr.Button("Evaluate")
+        #         eval_status = gr.Textbox(label="Evaluation Status", interactive=False)
+        #         submit_button = gr.Button("Prove and Submit to Leaderboard", visible=False)  # Initially hidden
+        #     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
+        #         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
+        #             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],)
+         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>
+    """):
+        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
+        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
+        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],
+    )
+    
 
 
         with gr.TabItem("🏅 Leaderboard"):