Update app.py

app.py

@@ -313,7 +313,7 @@ with gr.Blocks() as demo:
     gr.Markdown("""
         # Competition Title
         ### Welcome to the Competition Overview
-        ![Competition Logo](https://via.placeholder.com/150)  
+        ![Competition Logo](Mobile-MMLU2_.pdf)  
         Here you can submit your predictions, view the leaderboard, and track your performance!
     """)
 
@@ -321,27 +321,79 @@ with gr.Blocks() as demo:
         with gr.TabItem("📖 Overview"):
             gr.Markdown("""
                 ## Overview
-                This competition evaluates models on a benchmark dataset. Submit your predictions, see results instantly, and compare with others.
+                # Welcome to the Mobile-MMLU Benchmark Competition
+
+Evaluate the performance of mobile-compatible Large Language Models (LLMs) on 16,186 scenario-based and factual questions across 80 fields. Compete to showcase your model’s accuracy for real-world mobile scenarios.
+
+## What is Mobile-MMLU?
+
+Mobile-MMLU is a benchmark designed to test the capabilities of LLMs optimized for mobile use. By participating in this competition, you contribute to advancing mobile intelligence benchmarks and shaping the future of mobile-compatible AI systems.
+
+---
+
+## How It Works
+
+1. **Download the Dataset**  
+   Access the dataset and detailed generation instructions on our [GitHub page](https://github.com/your-github-repo).
+
+2. **Generate Predictions**  
+   Use your LLM to answer the questions and format your predictions as a CSV file with the following structure as written on our gothub page :
+	 
+3. **Submit Predictions**  
+Upload your predictions via the submission portal.
+
+4. **Evaluation**  
+Your submission will be scored on accuracy 
+
+5. **Leaderboard**  
+Compare your results against other participants on the live leaderboard.
+
+---
+
+## Competition Tasks
+
+Participants are tasked with generating predictions for the dataset and optimizing their models for:
+- **Accuracy**: Correctly answering questions across diverse fields.
+---
+
+
+## Get Started
+
+1. **Prepare Your Model**  
+Refer to our [GitHub page](https://github.com/your-github-repo) for dataset access and response generation instructions.
+
+2. **Submit Predictions**  
+Format your submission as specified in the rules.
+
+3. **Track Progress**  
+Check the leaderboard for real-time rankings.
+
+---
+
+## Contact Us
+
+For questions or support, contact us at: [Insert Email Address]
                 """)
 
-        with gr.TabItem("📤 Submission"):
-            file_input = gr.File(label="Upload Prediction CSV")
-            model_name_input = gr.Textbox(label="Model Name", placeholder="Enter your model name")
-            add_to_leaderboard_checkbox = gr.Checkbox(label="Add to Leaderboard?", value=True)
+       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.Textbox(label="Overall Accuracy", interactive=False)
+                add_to_leaderboard_checkbox = gr.Checkbox(label="Add to Leaderboard?", value=True)
+
+            eval_button = gr.Button("Evaluate")
             eval_status = gr.Textbox(label="Evaluation Status", interactive=False)
-            leaderboard_table_preview = gr.Dataframe(
-                value=load_leaderboard(),
-                label="Leaderboard (Preview)",
-                interactive=False,
-                wrap=True,
-            )
-            eval_button = gr.Button("Evaluate and Update Leaderboard")
+
             eval_button.click(
                 evaluate_predictions,
                 inputs=[file_input, model_name_input, add_to_leaderboard_checkbox],
-                outputs=[eval_status, leaderboard_table_preview],
+                outputs=[eval_status, overall_accuracy_display],
             )
 
+
         with gr.TabItem("🏅 Leaderboard"):
             leaderboard_table = gr.Dataframe(
                 value=load_leaderboard(),