Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import pandas as pd
 import os
 from evaluation import evaluate_model  # Import your evaluation function
+import zipfile
 
 # Define the path where you want to save the leaderboard data
 leaderboard_file = "leaderboard.csv"
@@ -12,53 +13,89 @@ if os.path.exists(leaderboard_file):
 else:
     leaderboard = pd.DataFrame(columns=["Model Name", "Score"])
 
+
+def extract_model(model_file, extract_dir="models"):
+    """
+    Extracts the uploaded model file if it's a zip archive.
+    """
+    os.makedirs(extract_dir, exist_ok=True)  # Ensure the directory exists
+    model_path = os.path.join(extract_dir, model_file.name)
+
+    if model_file.name.endswith(".zip"):
+        with zipfile.ZipFile(model_file, 'r') as zip_ref:
+            zip_ref.extractall(extract_dir)
+            print(f"Extracted model to: {extract_dir}")
+        return extract_dir
+    else:
+        # Save the file directly if it's not a zip
+        model_file.save(model_path)
+        return model_path
+
+
 # Submit the evaluation and update the leaderboard
 def submit_evaluation(model_name, model_file):
     """
     Handles the model submission, evaluates it, and updates the leaderboard.
     """
-    # Save the uploaded model to a folder
-    model_path = os.path.join("models", model_file.name)
-    model_file.save(model_path)
+    try:
+        # Extract or save the uploaded model
+        model_path = extract_model(model_file)
+
+        print(f"Model saved or extracted to: {model_path}")
+        print("Starting evaluation...")
 
-    # Example test data (replace with your actual test dataset)
-    test_data = [
-        ("Example text 1", 0),  # (text, label)
-        ("Example text 2", 1),
-        # Add more test data here
-    ]
+        # Example test data (replace with your actual test dataset)
+        test_data = [
+            ("Example text 1", 0),  # (text, label)
+            ("Example text 2", 1),
+        ]
 
-    # Evaluate the model using your custom evaluation code
-    score = evaluate_model(model_path, test_data)
+        # Evaluate the model using your custom evaluation code
+        score = evaluate_model(model_path, test_data)
+        print(f"Model evaluated successfully. Score: {score}")
 
-    # Update the leaderboard
-    new_entry = {"Model Name": model_name, "Score": score}
-    global leaderboard
-    leaderboard = leaderboard.append(new_entry, ignore_index=True)
-    leaderboard_sorted = leaderboard.sort_values(by="Score", ascending=False)
+        # Update the leaderboard
+        new_entry = {"Model Name": model_name, "Score": score}
+        global leaderboard
+        leaderboard = leaderboard.append(new_entry, ignore_index=True)
+        leaderboard_sorted = leaderboard.sort_values(by="Score", ascending=False)
 
-    # Save the updated leaderboard
-    leaderboard_sorted.to_csv(leaderboard_file, index=False)
+        # Save the updated leaderboard
+        leaderboard_sorted.to_csv(leaderboard_file, index=False)
+        print("Leaderboard updated.")
+
+        # Return the sorted leaderboard
+        return leaderboard_sorted, "Model submitted successfully!"
+
+    except Exception as e:
+        print(f"Error during evaluation: {str(e)}")
+        return leaderboard, f"Error: {str(e)}"
 
-    # Return the sorted leaderboard as output
-    return leaderboard_sorted
 
 # Create the Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("# Model Evaluation Leaderboard")
-    
-    # Model submission interface
+
+    # User inputs for model name and file upload
     with gr.Row():
         model_name_input = gr.Textbox(label="Model Name", placeholder="Enter the model name")
-        model_file_input = gr.File(label="Upload Model (Hugging Face Model Format)", file_types=[".pt", ".bin", ".h5", ".zip"])
+        model_file_input = gr.File(
+            label="Upload Model (Supported Formats: .pt, .bin, .h5, .zip)",
+            file_types=[".pt", ".bin", ".h5", ".zip"]
+        )
 
     submit_button = gr.Button("Submit Evaluation")
-    
-    # Leaderboard display area
-    leaderboard_display = gr.Dataframe(leaderboard)
-    try:
-        submit_button.click(submit_evaluation, inputs=[model_name_input, model_file_input], outputs=[leaderboard_display])
-    except:
-        print('No')
-# Launch the interface
+
+    # Leaderboard display and status message
+    leaderboard_display = gr.Dataframe(leaderboard, label="Leaderboard")
+    status_message = gr.Textbox(label="Status", interactive=False)
+
+    # Link the submit button to the evaluation function
+    submit_button.click(
+        submit_evaluation,
+        inputs=[model_name_input, model_file_input],
+        outputs=[leaderboard_display, status_message]
+    )
+
+# Launch the Gradio app
 demo.launch(share=True)