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| import gradio as gr | |
| import pandas as pd | |
| import os | |
| import re | |
| from datetime import datetime | |
| # Leaderboard Data (example CSV file for leaderboard) | |
| LEADERBOARD_FILE = "leaderboard.csv" | |
| def clean_answer(answer): | |
| if pd.isna(answer): | |
| return None | |
| answer = str(answer) | |
| clean = re.sub(r'[^A-Da-d]', '', answer) | |
| if clean: | |
| first_letter = clean[0].upper() | |
| if first_letter in ['A', 'B', 'C', 'D']: | |
| return first_letter | |
| return None | |
| def update_leaderboard(results): | |
| # Append results to leaderboard file | |
| new_entry = { | |
| "Model Name": results['model_name'], | |
| "Overall Accuracy": f"{results['overall_accuracy']:.2%}", | |
| "Valid Accuracy": f"{results['valid_accuracy']:.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" # Specify the path to the ground truth file | |
| if not prediction_file: | |
| return "Prediction file not uploaded", None | |
| if not os.path.exists(ground_truth_file): | |
| return "Ground truth file not found", None | |
| try: | |
| predictions_df = pd.read_csv(prediction_file.name) | |
| ground_truth_df = pd.read_csv(ground_truth_file) | |
| filename = os.path.basename(prediction_file.name) | |
| model_name = filename.split('_')[1].split('.')[0] if "_" in filename else "unknown_model" | |
| 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) | |
| correct_predictions = (merged_df['pred_answer'] == merged_df['Answer']).sum() | |
| total_predictions = len(merged_df) | |
| overall_accuracy = correct_predictions / total_predictions | |
| results = { | |
| 'model_name': model_name, | |
| 'overall_accuracy': overall_accuracy, | |
| 'correct_predictions': correct_predictions, | |
| 'total_questions': total_predictions, | |
| } | |
| update_leaderboard(results) | |
| return "Evaluation completed successfully! Leaderboard updated.", LEADERBOARD_FILE | |
| except Exception as e: | |
| return f"Error: {str(e)}", None | |
| # Gradio Interface with Leaderboard | |
| def display_leaderboard(): | |
| if not os.path.exists(LEADERBOARD_FILE): | |
| return "Leaderboard is empty." | |
| leaderboard_df = pd.read_csv(LEADERBOARD_FILE) | |
| return leaderboard_df.to_markdown(index=False) | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown("# Prediction Evaluation Tool with Leaderboard") | |
| with gr.Tab("Evaluate"): | |
| file_input = gr.File(label="Upload Prediction CSV") | |
| eval_status = gr.Textbox(label="Evaluation Status") | |
| eval_results_file = gr.File(label="Download Evaluation Results") | |
| eval_button = gr.Button("Evaluate") | |
| eval_button.click( | |
| evaluate_predictions, inputs=file_input, outputs=[eval_status, eval_results_file] | |
| ) | |
| with gr.Tab("Leaderboard"): | |
| leaderboard_text = gr.Textbox(label="Leaderboard", interactive=False) | |
| refresh_button = gr.Button("Refresh Leaderboard") | |
| refresh_button.click(display_leaderboard, outputs=leaderboard_text) | |
| if __name__ == "__main__": | |
| demo.launch() | |