Update app.py

app.py

@@ -16,13 +16,148 @@ HF_TOKEN = os.getenv("HF_TOKEN")
 if not HF_TOKEN:
     raise ValueError("HF_TOKEN environment variable is not set or invalid.")
 
+# def initialize_leaderboard_file():
+#     """
+#     Ensure the leaderboard file exists and has the correct headers.
+#     """
+#     if not os.path.exists(LEADERBOARD_FILE):
+#         pd.DataFrame(columns=[
+#            "Model Name", "Overall Accuracy", "Valid Accuracy",
+#             "Correct Predictions", "Total Questions", "Timestamp"
+#         ]).to_csv(LEADERBOARD_FILE, index=False)
+#     elif os.stat(LEADERBOARD_FILE).st_size == 0:
+#         pd.DataFrame(columns=[
+#             "Model Name", "Overall Accuracy", "Valid Accuracy",
+#             "Correct Predictions", "Total Questions", "Timestamp"
+#         ]).to_csv(LEADERBOARD_FILE, index=False)
+
+# def clean_answer(answer):
+#     if pd.isna(answer):
+#         return None
+#     answer = str(answer)
+#     clean = re.sub(r'[^A-Da-d]', '', answer)
+#     return clean[0].upper() if clean else None
+
+
+# def update_leaderboard(results):
+#     """
+#     Append new submission results to the leaderboard file and push updates to the Hugging Face repository.
+#     """
+#     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"),
+#     }
+
+#     try:
+#         # Update the local leaderboard file
+#         new_entry_df = pd.DataFrame([new_entry])
+#         file_exists = os.path.exists(LEADERBOARD_FILE)
+        
+#         new_entry_df.to_csv(
+#             LEADERBOARD_FILE,
+#             mode='a',  # Append mode
+#             index=False,
+#             header=not file_exists  # Write header only if the file is new
+#         )
+#         print(f"Leaderboard updated successfully at {LEADERBOARD_FILE}")
+
+#         # Push the updated file to the Hugging Face repository using HTTP API
+#         api = HfApi()
+#         token = HfFolder.get_token()
+        
+#         api.upload_file(
+#             path_or_fileobj=LEADERBOARD_FILE,
+#             path_in_repo="leaderboard.csv",
+#             repo_id="SondosMB/ss",  # Your Space repository
+#             repo_type="space",
+#             token=token
+#         )
+#         print("Leaderboard changes pushed to Hugging Face repository.")
+        
+#     except Exception as e:
+#         print(f"Error updating leaderboard file: {e}")
+
+
+
+# def load_leaderboard():
+#     if not os.path.exists(LEADERBOARD_FILE) or os.stat(LEADERBOARD_FILE).st_size == 0:
+#         return pd.DataFrame({
+#             "Model Name": [],
+#             "Overall Accuracy": [],
+#             "Valid Accuracy": [],
+#             "Correct Predictions": [],
+#             "Total Questions": [],
+#             "Timestamp": [],
+#         })
+#     return pd.read_csv(LEADERBOARD_FILE)
+
+# def evaluate_predictions(prediction_file, model_name, add_to_leaderboard):
+#     try:
+#         ground_truth_path = hf_hub_download(
+#             repo_id="SondosMB/ground-truth-dataset",
+#             filename="ground_truth.csv",
+#             repo_type="dataset",
+#             use_auth_token=True
+#         )
+#         ground_truth_df = pd.read_csv(ground_truth_path)
+#     except FileNotFoundError:
+#         return "Ground truth file not found in the dataset repository.", load_leaderboard()
+#     except Exception as e:
+#         return f"Error loading ground truth: {e}", load_leaderboard()
+
+#     if not prediction_file:
+#         return "Prediction file not uploaded.", load_leaderboard()
+
+#     try:
+#         #load predition file
+#         predictions_df = pd.read_csv(prediction_file.name)
+#          # Validate required columns in prediction file
+#         required_columns = ['question_id', 'predicted_answer']
+#         missing_columns = [col for col in required_columns if col not in predictions_df.columns]
+#         if missing_columns:
+#             return (f"Error: Missing required columns in prediction file: {', '.join(missing_columns)}.",
+#                     load_leaderboard())
+
+#         # Validate 'Answer' column in ground truth file
+#         if 'Answer' not in ground_truth_df.columns:
+#             return "Error: 'Answer' column is missing in the ground truth dataset.", load_leaderboard()
+#         merged_df = pd.merge(predictions_df, ground_truth_df, on='question_id', how='inner')
+#         merged_df['pred_answer'] = merged_df['predicted_answer'].apply(clean_answer)
+
+#         valid_predictions = merged_df.dropna(subset=['pred_answer'])
+#         correct_predictions = (valid_predictions['pred_answer'] == valid_predictions['Answer']).sum()
+#         total_predictions = len(merged_df)
+#         total_valid_predictions = len(valid_predictions)
+
+#         overall_accuracy = correct_predictions / total_predictions if total_predictions > 0 else 0
+#         valid_accuracy = correct_predictions / total_valid_predictions if total_valid_predictions > 0 else 0
+
+#         results = {
+#             'model_name': model_name if model_name else "Unknown Model",
+#             'overall_accuracy': overall_accuracy,
+#         }
+
+#         if add_to_leaderboard:
+#             update_leaderboard(results)
+#             return "Evaluation completed and added to leaderboard.", load_leaderboard()
+#         else:
+#             return "Evaluation completed but not added to leaderboard.", load_leaderboard()
+  
+#     except Exception as e:
+#         return f"Error during evaluation: {str(e)}", load_leaderboard()
+
+# initialize_leaderboard_file()
 def initialize_leaderboard_file():
     """
     Ensure the leaderboard file exists and has the correct headers.
     """
     if not os.path.exists(LEADERBOARD_FILE):
         pd.DataFrame(columns=[
-           "Model Name", "Overall Accuracy", "Valid Accuracy",
+            "Model Name", "Overall Accuracy", "Valid Accuracy",
             "Correct Predictions", "Total Questions", "Timestamp"
         ]).to_csv(LEADERBOARD_FILE, index=False)
     elif os.stat(LEADERBOARD_FILE).st_size == 0:
@@ -44,7 +179,7 @@ def update_leaderboard(results):
     Append new submission results to the leaderboard file and push updates to the Hugging Face repository.
     """
     new_entry = {
-       "Model Name": results['model_name'],
+        "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'],
@@ -139,6 +274,9 @@ def evaluate_predictions(prediction_file, model_name, add_to_leaderboard):
         results = {
             'model_name': model_name if model_name else "Unknown Model",
             'overall_accuracy': overall_accuracy,
+            'valid_accuracy': valid_accuracy,
+            'correct_predictions': correct_predictions,
+            'total_questions': total_predictions,
         }
 
         if add_to_leaderboard:
@@ -152,6 +290,7 @@ def evaluate_predictions(prediction_file, model_name, add_to_leaderboard):
 
 initialize_leaderboard_file()
 
+
 # Function to set default mode
 # Function to set default mode
 import gradio as gr