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| # # demo.launch() | |
| # import gradio as gr | |
| # import pandas as pd | |
| # import os | |
| # import re | |
| # from datetime import datetime | |
| # LEADERBOARD_FILE = "leaderboard.csv" # File to store all submissions persistently | |
| # LAST_UPDATED = datetime.now().strftime("%B %d, %Y") | |
| # def initialize_leaderboard_file(): | |
| # """ | |
| # Ensure the leaderboard file exists and has the correct headers. | |
| # """ | |
| # if not os.path.exists(LEADERBOARD_FILE): | |
| # # Create the file with headers | |
| # pd.DataFrame(columns=[ | |
| # "Model Name", "Overall Accuracy", "Valid Accuracy", | |
| # "Correct Predictions", "Total Questions", "Timestamp" | |
| # ]).to_csv(LEADERBOARD_FILE, index=False) | |
| # else: | |
| # # Check if the file is empty and write headers if needed | |
| # if 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): | |
| # """ | |
| # Clean and normalize the predicted answers. | |
| # """ | |
| # if pd.isna(answer): | |
| # return None | |
| # answer = str(answer) | |
| # clean = re.sub(r'[^A-Da-d]', '', answer) | |
| # if clean: | |
| # return clean[0].upper() | |
| # return None | |
| # def update_leaderboard(results): | |
| # """ | |
| # Append new submission results to the leaderboard file. | |
| # """ | |
| # 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"), | |
| # } | |
| # new_entry_df = pd.DataFrame([new_entry]) | |
| # new_entry_df.to_csv(LEADERBOARD_FILE, mode='a', index=False, header=False) | |
| # def load_leaderboard(): | |
| # """ | |
| # Load all submissions from the leaderboard file. | |
| # """ | |
| # 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): | |
| # """ | |
| # Evaluate predictions and optionally add results to the leaderboard. | |
| # """ | |
| # ground_truth_file = "ground_truth.csv" | |
| # if not os.path.exists(ground_truth_file): | |
| # return "Ground truth file not found.", load_leaderboard() | |
| # if not prediction_file: | |
| # return "Prediction file not uploaded.", load_leaderboard() | |
| # try: | |
| # # Load predictions and ground truth | |
| # predictions_df = pd.read_csv(prediction_file.name) | |
| # ground_truth_df = pd.read_csv(ground_truth_file) | |
| # # Merge predictions with ground truth | |
| # 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) | |
| # # Evaluate predictions | |
| # 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) | |
| # # Calculate accuracy | |
| # 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, | |
| # 'valid_accuracy': valid_accuracy, | |
| # 'correct_predictions': correct_predictions, | |
| # 'total_questions': total_predictions, | |
| # } | |
| # # Update leaderboard only if opted in | |
| # 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 | |
| # initialize_leaderboard_file() | |
| # # Gradio Interface | |
| # with gr.Blocks() as demo: | |
| # gr.Markdown("# Prediction Evaluation Tool with Leaderboard") | |
| # with gr.Tabs(): | |
| # # Submission Tab | |
| # with gr.TabItem("🏅 Submission"): | |
| # file_input = gr.File(label="Upload Prediction CSV") | |
| # model_name_input = gr.Textbox(label="Model Name", placeholder="Enter your model name") | |
| # add_to_leaderboard_checkbox = gr.Checkbox(label="Add to Leaderboard?", value=True) | |
| # eval_status = gr.Textbox(label="Evaluation Status", interactive=False) | |
| # leaderboard_table_preview = gr.Dataframe( | |
| # value=load_leaderboard(), | |
| # label="Leaderboard (Preview)", | |
| # interactive=False, | |
| # wrap=True, | |
| # ) | |
| # eval_button = gr.Button("Evaluate and Update Leaderboard") | |
| # eval_button.click( | |
| # evaluate_predictions, | |
| # inputs=[file_input, model_name_input, add_to_leaderboard_checkbox], | |
| # outputs=[eval_status, leaderboard_table_preview], | |
| # ) | |
| # # Leaderboard Tab | |
| # with gr.TabItem("🏅 Leaderboard"): | |
| # leaderboard_table = gr.Dataframe( | |
| # value=load_leaderboard(), | |
| # label="Leaderboard", | |
| # interactive=False, | |
| # wrap=True, | |
| # ) | |
| # refresh_button = gr.Button("Refresh Leaderboard") | |
| # refresh_button.click( | |
| # lambda: load_leaderboard(), | |
| # inputs=[], | |
| # outputs=[leaderboard_table], | |
| # ) | |
| # gr.Markdown(f"Last updated on **{LAST_UPDATED}**") | |
| # demo.launch() | |
| import gradio as gr | |
| import pandas as pd | |
| import os | |
| import re | |
| from datetime import datetime | |
| from huggingface_hub import hf_hub_download | |
| from huggingface_hub import HfApi, HfFolder | |
| LEADERBOARD_FILE = "leaderboard.csv" | |
| GROUND_TRUTH_FILE = "ground_truth.csv" | |
| LAST_UPDATED = datetime.now().strftime("%B %d, %Y") | |
| # Ensure authentication and suppress warnings | |
| os.environ["HF_HUB_DISABLE_SYMLINKS_WARNING"] = "1" | |
| 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: | |
| predictions_df = pd.read_csv(prediction_file.name) | |
| 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, | |
| 'valid_accuracy': valid_accuracy, | |
| 'correct_predictions': correct_predictions, | |
| 'total_questions': total_predictions, | |
| } | |
| 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() | |
| with gr.Blocks() as demo: | |
| gr.Markdown(""" | |
| # Competition Title | |
| ### Welcome to the Competition Overview | |
|  | |
| Here you can submit your predictions, view the leaderboard, and track your performance! | |
| """) | |
| with gr.Tabs(): | |
| with gr.TabItem("📖 Overview"): | |
| gr.Markdown(""" | |
| ## Overview | |
| This competition evaluates models on a benchmark dataset. Submit your predictions, see results instantly, and compare with others. | |
| """) | |
| with gr.TabItem("📤 Submission"): | |
| file_input = gr.File(label="Upload Prediction CSV") | |
| model_name_input = gr.Textbox(label="Model Name", placeholder="Enter your model name") | |
| add_to_leaderboard_checkbox = gr.Checkbox(label="Add to Leaderboard?", value=True) | |
| eval_status = gr.Textbox(label="Evaluation Status", interactive=False) | |
| leaderboard_table_preview = gr.Dataframe( | |
| value=load_leaderboard(), | |
| label="Leaderboard (Preview)", | |
| interactive=False, | |
| wrap=True, | |
| ) | |
| eval_button = gr.Button("Evaluate and Update Leaderboard") | |
| eval_button.click( | |
| evaluate_predictions, | |
| inputs=[file_input, model_name_input, add_to_leaderboard_checkbox], | |
| outputs=[eval_status, leaderboard_table_preview], | |
| ) | |
| with gr.TabItem("🏅 Leaderboard"): | |
| leaderboard_table = gr.Dataframe( | |
| value=load_leaderboard(), | |
| label="Leaderboard", | |
| interactive=False, | |
| wrap=True, | |
| ) | |
| refresh_button = gr.Button("Refresh Leaderboard") | |
| refresh_button.click( | |
| lambda: load_leaderboard(), | |
| inputs=[], | |
| outputs=[leaderboard_table], | |
| ) | |
| gr.Markdown(f"Last updated on **{LAST_UPDATED}**") | |
| demo.launch() | |