add leaderboard

README.md

@@ -8,6 +8,7 @@ sdk_version: 6.2.0
 app_file: app.py
 pinned: false
 short_description: Two exercises (La Disparition and Toulouse cancelling)
+hf_oauth: true
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,45 +1,117 @@
 import gradio as gr
 from gradio_client import Client, handle_file
+from huggingface_hub import hf_hub_download, upload_file
 import os
+import time
+import pandas as pd
+from datetime import datetime
 
-# 1. Configuration
+# --- CONFIGURATION ---
 PRIVATE_SPACE_ID = "LLM-course/lipogram_private"
+DATASET_REPO_ID = "LLM-course/leaderboard-lipogram"
 HF_TOKEN = os.environ.get("HF_TOKEN")
+LOCAL_CSV = "leaderboard.csv"
 
-# 2. Connect to the Private Evaluator
-client = Client(PRIVATE_SPACE_ID, token=HF_TOKEN)
+# --- DATABASE HELPERS ---
+def sync_leaderboard():
+    """Download the latest leaderboard from the Private Dataset"""
+    try:
+        path = hf_hub_download(
+            repo_id=DATASET_REPO_ID,
+            filename="leaderboard.csv",
+            repo_type="dataset",
+            token=HF_TOKEN
+        )
+        return pd.read_csv(path)
+    except Exception:
+        # If file doesn't exist yet, return empty structure
+        return pd.DataFrame(columns=["Timestamp", "User", "Score", "Ex 1", "Ex 2"])
+
+def save_score(user, score, ex1, ex2):
+    """Append new score and push back to Dataset"""
+    df = sync_leaderboard()
+    new_entry = {
+        "Timestamp": datetime.now().strftime("%Y-%m-%d %H:%M"),
+        "User": user,
+        "Score": score,
+        "Ex 1": ex1,
+        "Ex 2": ex2
+    }
+    df = pd.concat([df, pd.DataFrame([new_entry])], ignore_index=True)
+    df.to_csv(LOCAL_CSV, index=False)
+    
+    # Push the updated file back to the private dataset
+    upload_file(
+        path_or_fileobj=LOCAL_CSV,
+        path_in_repo="leaderboard.csv",
+        repo_id=DATASET_REPO_ID,
+        repo_type="dataset",
+        token=HF_TOKEN
+    )
+    return df
+
+# --- MAIN LOGIC ---
+user_last_submission = {}
+
+# ... (Keep all your previous imports and helper functions) ...
+
+def submit_challenge(file, request: gr.Request):
+    # Check if the user is logged in via OAuth
+    if request.username is None:
+        raise gr.Error("Please 'Sign in with Hugging Face' at the top of the page to submit.")
+    
+    user_name = request.username
+    user_key = f"{user_name}" # Use username as the unique key
+    
+    # Rate Limiting (10 mins)
+    if user_key in user_last_submission and (time.time() - user_last_submission[user_key]) < 600:
+        raise gr.Error("One submission every 10 minutes allowed.")
 
-def submit_challenge(file):
-    if file is None:
-        return "Please upload your 'challenge.py' file first."
+    if file is None: raise gr.Error("Please upload a file.")
+    
+    gr.Info(f"Hello {user_name}, sending your code to the evaluator...")
     
     try:
-        result = client.predict(
-            file_obj=handle_file(file.name), 
-            api_name="/predict"
-        )
-        return result
+        client = Client(PRIVATE_SPACE_ID, token=HF_TOKEN)
+        result_text = client.predict(file_obj=handle_file(file.name), api_name="/predict")
+        
+        # Parse the result from your Private Space output
+        try:
+            # Assumes format "Final Grade: XX.X%"
+            total_score = float(result_text.split('%')[0].split(':')[-1].strip())
+        except:
+            total_score = 0.0
+
+        # Save to your Private Dataset "DB"
+        updated_df = save_score(user_name, total_score, "Passed Check", "Passed Check")
+        user_last_submission[user_key] = time.time()
+        
+        return result_text, updated_df.sort_values(by="Score", ascending=False)
+    
     except Exception as e:
-        return f"Error contacting evaluation server: {str(e)}"
+        return f"Error: {str(e)}", sync_leaderboard()
 
-# 3. Simple User Interface
+# --- UPDATED UI ---
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# LLM Lipogram Submission")
-    gr.Markdown("""
-    Upload your `challenge.py` script below. It will be sent to a private 
-    evaluation server and tested against 20 hidden prompts.
-    """)
+    gr.Markdown("# LLM Lipogram Challenge Portal")
     
-    with gr.Row():
-        file_input = gr.File(label="Upload challenge.py", file_types=[".py"])
-        output_text = gr.Markdown(label="Evaluation Report")
-    
-    submit_btn = gr.Button("Submit for Grading", variant="primary")
+    # This displays a login button if the user is not authenticated
+    gr.LoginButton() 
     
+    with gr.Tabs():
+        with gr.TabItem("Submit"):
+            gr.Markdown("### 1. Sign in above\n### 2. Upload challenge.py below")
+            file_input = gr.File(label="challenge.py")
+            submit_btn = gr.Button("Evaluate My Code", variant="primary")
+            output_text = gr.Markdown()
+            
+        with gr.TabItem("Leaderboard"):
+            leaderboard_df = gr.DataFrame(value=sync_leaderboard, interactive=False)
+
     submit_btn.click(
         fn=submit_challenge, 
         inputs=file_input, 
-        outputs=output_text
+        outputs=[output_text, leaderboard_df]
     )
 
 demo.launch()

requirements.txt

@@ -0,0 +1,4 @@
+gradio
+gradio-client
+huggingface-hub
+pandas