Update app.py

app.py

@@ -516,18 +516,7 @@ with gr.Blocks(css=css_tech_theme) as demo:
             <h3 style="color: #6a1b9a;">How It Works</h3>
             <ol>
                 <li>1️⃣ <strong>Download the Dataset:</strong> Access the dataset and detailed instructions on the <a href="https://github.com/your-github-repo" target="_blank">GitHub page</a>. Follow the steps to ensure your environment is set up correctly.</li>
-                <li>2️⃣ <strong>Generate Predictions:</strong> Use the provided script in the GitHub repository to generate answers. Ensure the output file matches the following format:</li>
-            </ol>
-            <pre style="background-color: #f9f7fd; border: 1px solid #d3bce8; border-radius: 8px; padding: 10px;">
-    question_id,predicted_answer
-    q1,A
-    q2,B
-    q3,C
-    ...
-            </pre>
-            <p>The <code>question_id</code> is the unique identifier for each question, and <code>predicted_answer</code> is your model's prediction (A, B, C, or D).</p>
-            
-            <ol start="3">
+                <li>2️⃣ <strong>Generate Predictions:</strong> Use the provided script in the GitHub repository to generate answers. Ensure the output file matches the format in the github </li>
                 <li>3️⃣ <strong>Submit Predictions:</strong> Upload your CSV file to the <strong>Submission Page</strong> on this platform.</li>
                 <li>4️⃣ <strong>Evaluation:</strong> Your submission will be scored based on accuracy. The results will include overall and valid accuracy metrics.</li>
                 <li>5️⃣ <strong>Leaderboard:</strong> Optionally, add your results to the real-time leaderboard to compare your model's performance with others.</li>
@@ -550,16 +539,16 @@ with gr.Blocks(css=css_tech_theme) as demo:
             </div>
             """)
             with gr.Row(elem_id="submission-fields"):
-                file_input = gr.File(label="Upload Prediction CSV", file_types=[".csv"], interactive=True,scale=1, min_width=800)
-                model_name_input = gr.Textbox(label="Model Name", placeholder="Enter your model name",scale=1, min_width=800)
+                file_input = gr.File(label="Upload Prediction CSV", file_types=[".csv"], interactive=True,scale=1, max_width=100%)
+                model_name_input = gr.Textbox(label="Model Name", placeholder="Enter your model name",scale=1, max_width=100%)
         
             with gr.Row(elem_id="submission-results"):
-                overall_accuracy_display = gr.Number(label="Overall Accuracy", interactive=False,scale=1, min_width=800)
+                overall_accuracy_display = gr.Number(label="Overall Accuracy", interactive=False,scale=1, max_width=100%)
         
             with gr.Row(elem_id="submission-buttons"):
-                eval_button = gr.Button("Evaluate")
-                submit_button = gr.Button("Prove and Submit to Leaderboard", elem_id="evaluation-status", visible=False)
-                eval_status = gr.Textbox(label="Evaluation Status", interactive=False)
+                eval_button = gr.Button("Evaluate",scale=1, max_width=100%)
+                submit_button = gr.Button("Prove and Submit to Leaderboard", elem_id="evaluation-status", visible=False,scale=1, max_width=100%)
+                eval_status = gr.Textbox(label="Evaluation Status", interactive=False,scale=1, max_width=100%)
         
         # Define the functions outside the `with` block
         def handle_evaluation(file, model_name):