Update app.py

app.py

@@ -177,7 +177,7 @@ def load_leaderboard():
     df = pd.read_csv(LEADERBOARD_FILE)
     
     # Fill empty Team Name
-    df['Team Name'] = df['Team Name'].fillna('Unknown Team')
+    df['Team Name'] = df['Team Name'].fillna(df['Model Name'])
     
     # Fill empty Timestamp
     df['Timestamp'] = df['Timestamp'].fillna(datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
@@ -213,6 +213,19 @@ def load_leaderboard():
     
     return df
 
+# def load_leaderboard_pro():
+#     if not os.path.exists(LEADERBOARD_FILE_pro) or os.stat(LEADERBOARD_FILE_pro).st_size == 0:
+#         return pd.DataFrame({
+#             "Model Name": [],
+#             "Overall Accuracy": [],
+#             "Correct Predictions": [],
+#             "Total Questions": [],
+#             "Timestamp": [],
+#             "Team Name": [],
+            
+#         })
+#     return pd.read_csv(LEADERBOARD_FILE_pro)
+
 def load_leaderboard_pro():
     if not os.path.exists(LEADERBOARD_FILE_pro) or os.stat(LEADERBOARD_FILE_pro).st_size == 0:
         return pd.DataFrame({
@@ -222,9 +235,48 @@ def load_leaderboard_pro():
             "Total Questions": [],
             "Timestamp": [],
             "Team Name": [],
-            
         })
-    return pd.read_csv(LEADERBOARD_FILE_pro)
+    
+    # Read the CSV file
+    df = pd.read_csv(LEADERBOARD_FILE_pro)
+    
+    # Fill empty Team Name
+    df['Team Name'] = df['Team Name'].fillna(df['Model Name'])
+    
+    # Fill empty Timestamp
+    df['Timestamp'] = df['Timestamp'].fillna(datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
+    
+    # If Timestamp is an empty string, replace with current timestamp
+    df.loc[df['Timestamp'] == '', 'Timestamp'] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    
+    # Fill Correct Predictions based on Accuracy and Total Questions if possible
+    # Assuming Total Questions is typically 16186 and Accuracy is a percentage
+    df['Correct Predictions'] = df.apply(
+        lambda row: round(9497 * (row['Overall Accuracy'] / 100)) 
+        if pd.isna(row['Correct Predictions']) or row['Correct Predictions'] == '' 
+        else row['Correct Predictions'], 
+        axis=1
+    )
+    
+    # Remove duplicate entries, keeping the last occurrence
+    df = df.drop_duplicates(subset='Model Name', keep='last')
+    
+    # Ensure all expected columns are present
+    expected_columns = [
+        "Model Name", 
+        "Overall Accuracy", 
+        "Correct Predictions", 
+        "Total Questions", 
+        "Timestamp", 
+        "Team Name"
+    ]
+    
+    # Reorder columns and save
+    df = df[expected_columns]
+    df.to_csv(LEADERBOARD_FILE_pro, index=False)
+    
+    return df
+