Update app.py

app.py

@@ -194,20 +194,6 @@ def clean_answer(answer):
         return clean[0].upper()
     return None
 
-def update_leaderboard(results):
-    new_entry = {
-        "Model Name": results['model_name'],
-        "Overall Accuracy": round(results['overall_accuracy'] * 100, 2),
-        "Valid Accuracy": round(results['valid_accuracy'] * 100, 2),
-        "Correct Predictions": results['correct_predictions'],
-        "Total Questions": results['total_questions'],
-        "Timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
-    }
-    leaderboard_df = pd.DataFrame([new_entry])
-    if os.path.exists(LEADERBOARD_FILE):
-        existing_df = pd.read_csv(LEADERBOARD_FILE)
-        leaderboard_df = pd.concat([existing_df, leaderboard_df], ignore_index=True)
-    leaderboard_df.to_csv(LEADERBOARD_FILE, index=False)
 
 def evaluate_predictions(prediction_file):
     ground_truth_file = "ground_truth.csv"
@@ -245,18 +231,50 @@ def evaluate_predictions(prediction_file):
     except Exception as e:
         return f"Error during evaluation: {str(e)}"
 
-def load_leaderboard():
-    if not os.path.exists(LEADERBOARD_FILE):
-        return pd.DataFrame({"Message": ["Leaderboard is empty."]})
-    return pd.read_csv(LEADERBOARD_FILE)
 
 # Build Gradio App
 
+def update_leaderboard(results):
+    """
+    Update the leaderboard file with new results.
+    """
+    new_entry = {
+        "Model Name": results['model_name'],
+        "Overall Accuracy": round(results['overall_accuracy'] * 100, 2),
+        "Valid Accuracy": round(results['valid_accuracy'] * 100, 2),
+        "Correct Predictions": results['correct_predictions'],
+        "Total Questions": results['total_questions'],
+        "Timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+    }
+
+    # Convert new entry to DataFrame
+    new_entry_df = pd.DataFrame([new_entry])
+
+    # Append to leaderboard file
+    if not os.path.exists(LEADERBOARD_FILE):
+        # If file does not exist, create it with headers
+        new_entry_df.to_csv(LEADERBOARD_FILE, index=False)
+    else:
+        # Append without headers
+        new_entry_df.to_csv(LEADERBOARD_FILE, mode='a', index=False, header=False)
+
+
 def load_leaderboard():
+    """
+    Load the leaderboard from the leaderboard file.
+    """
     if not os.path.exists(LEADERBOARD_FILE):
-        return pd.DataFrame({"Message": ["Leaderboard is empty."]})
-    print("Loading leaderboard data...")
+        return pd.DataFrame({
+            "Model Name": [],
+            "Overall Accuracy": [],
+            "Valid Accuracy": [],
+            "Correct Predictions": [],
+            "Total Questions": [],
+            "Timestamp": [],
+        })
     return pd.read_csv(LEADERBOARD_FILE)
+
+
 def evaluate_predictions_and_update_leaderboard(prediction_file):
     """
     Evaluate predictions and update the leaderboard.