from flask import Flask, render_template, request, redirect, url_for, send_from_directory, session import json import random import os import string import logging from datetime import datetime from huggingface_hub import login, HfApi, hf_hub_download # Set up logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', handlers=[ logging.FileHandler("app.log"), logging.StreamHandler() ]) logger = logging.getLogger(__name__) # Use the Hugging Face token from environment variables hf_token = os.environ.get("HF_TOKEN") if hf_token: login(token=hf_token) else: logger.error("HF_TOKEN not found in environment variables") app = Flask(__name__) app.config['SECRET_KEY'] = 'supersecretkey' # Change this to a random secret key # Directories for visualizations VISUALIZATION_DIRS = { "No-XAI": "htmls_NO_XAI_mod", "Dater": "htmls_DATER_mod2", "Chain-of-Table": "htmls_COT_mod", "Plan-of-SQLs": "htmls_POS_mod2" } def get_method_dir(method): if method == 'No-XAI': return 'NO_XAI' elif method == 'Dater': return 'DATER' elif method == 'Chain-of-Table': return 'COT' elif method == 'Plan-of-SQLs': return 'POS' else: return None METHODS = ["No-XAI", "Dater", "Chain-of-Table", "Plan-of-SQLs"] def save_session_data(username, data, is_update=False): try: seed = data.get('seed', 'unknown') start_time = data.get('start_time', datetime.now().isoformat()) file_name = f'{username}_seed{seed}_{start_time}_session.json' file_name = "".join(c for c in file_name if c.isalnum() or c in ['_', '-', '.']) json_data = json.dumps(data, indent=4) temp_file_path = f"/tmp/{file_name}" with open(temp_file_path, 'w') as f: f.write(json_data) api = HfApi() repo_path = "session_data_foward_simulation" if is_update: # If updating, we need to delete the old file first existing_files = api.list_repo_files(repo_id="luulinh90s/Tabular-LLM-Study-Data", repo_type="space") existing_file = next( (f for f in existing_files if f.startswith(f'{repo_path}/{username}_seed{seed}_{start_time}')), None) if existing_file: api.delete_file(repo_id="luulinh90s/Tabular-LLM-Study-Data", repo_type="space", path_in_repo=existing_file) api.upload_file( path_or_fileobj=temp_file_path, path_in_repo=f"{repo_path}/{file_name}", repo_id="luulinh90s/Tabular-LLM-Study-Data", repo_type="space", ) os.remove(temp_file_path) logger.info( f"Session data {'updated' if is_update else 'saved'} for user {username} in Hugging Face Data Space") except Exception as e: logger.exception(f"Error {'updating' if is_update else 'saving'} session data for user {username}: {e}") def load_session_data(username): try: api = HfApi() files = api.list_repo_files(repo_id="luulinh90s/Tabular-LLM-Study-Data", repo_type="space") user_files = [f for f in files if f.startswith(f'session_data_foward_simulation/{username}_') and f.endswith('_session.json')] if not user_files: logger.warning(f"No session data found for user {username}") return None latest_file = sorted(user_files, reverse=True)[0] file_path = hf_hub_download(repo_id="luulinh90s/Tabular-LLM-Study-Data", repo_type="space", filename=latest_file) with open(file_path, 'r') as f: data = json.load(f) logger.info(f"Session data loaded for user {username} from Hugging Face Data Space") return data except Exception as e: logger.exception(f"Error loading session data for user {username}: {e}") return None def load_samples(): common_samples = [] categories = ["TP", "TN", "FP", "FN"] for category in categories: files = set(os.listdir(f'htmls_NO_XAI_mod/{category}')) for method in ["Dater", "Chain-of-Table", "Plan-of-SQLs"]: method_dir = VISUALIZATION_DIRS[method] files &= set(os.listdir(f'{method_dir}/{category}')) for file in files: common_samples.append({'category': category, 'file': file}) logger.info(f"Found {len(common_samples)} common samples across all methods") return common_samples def select_balanced_samples(samples): try: if len(samples) < 10: logger.warning(f"Not enough common samples. Only {len(samples)} available.") return samples selected_samples = random.sample(samples, 10) logger.info(f"Selected 10 unique samples") return selected_samples except Exception as e: logger.exception("Error selecting balanced samples") return [] @app.route('/', methods=['GET', 'POST']) def index(): if request.method == 'POST': username = request.form.get('username') seed = request.form.get('seed') method = request.form.get('method') if not username or not seed or not method: return "Please fill in all fields and select a method.", 400 try: seed = int(seed) random.seed(seed) all_samples = load_samples() selected_samples = select_balanced_samples(all_samples) if len(selected_samples) == 0: return "No common samples were found", 500 start_time = datetime.now().isoformat() session_data = { 'username': username, 'seed': str(seed), # Store as string 'method': method, 'selected_samples': selected_samples, 'current_index': 0, 'responses': [], 'start_time': start_time } save_session_data(username, session_data) return redirect(url_for('experiment', username=username)) except Exception as e: logger.exception(f"Error in index route: {e}") return "An error occurred", 500 return render_template('index.html') @app.route('/experiment/', methods=['GET', 'POST']) def experiment(username): try: session_data = load_session_data(username) if not session_data: return redirect(url_for('index')) selected_samples = session_data['selected_samples'] method = session_data['method'] current_index = session_data['current_index'] if current_index >= len(selected_samples): return redirect(url_for('completed', username=username)) sample = selected_samples[current_index] visualization_dir = VISUALIZATION_DIRS[method] visualization_path = f"{visualization_dir}/{sample['category']}/{sample['file']}" statement = """ Please note that in select row function, starting index is 0 for Chain-of-Table 1 for Dater and Index * represents the selection of the whole Table. Based on the explanation provided, what do you think the AI model will predict? Will it predict the statement as TRUE or FALSE? """ return render_template('experiment.html', sample_id=current_index, statement=statement, visualization=url_for('send_visualization', filename=visualization_path), username=username, method=method) except Exception as e: logger.exception(f"An error occurred in the experiment route: {e}") return "An error occurred", 500 @app.route('/feedback', methods=['POST']) def feedback(): try: username = request.form['username'] prediction = request.form['prediction'] session_data = load_session_data(username) if not session_data: logger.error(f"No session data found for user: {username}") return redirect(url_for('index')) session_data['responses'].append({ 'sample_id': session_data['current_index'], 'user_prediction': prediction }) session_data['current_index'] += 1 save_session_data(username, session_data) logger.info(f"Prediction saved for user {username}, sample {session_data['current_index'] - 1}") if session_data['current_index'] >= len(session_data['selected_samples']): return redirect(url_for('completed', username=username)) return redirect(url_for('experiment', username=username)) except Exception as e: logger.exception(f"Error in feedback route: {e}") return "An error occurred", 500 @app.route('/completed/') def completed(username): try: session_data = load_session_data(username) if not session_data: logger.error(f"No session data found for user: {username}") return redirect(url_for('index')) session_data['end_time'] = datetime.now().isoformat() responses = session_data['responses'] method = session_data['method'] if method == "Chain-of-Table": json_file = 'Tabular_LLMs_human_study_vis_6_COT.json' elif method == "Plan-of-SQLs": json_file = 'Tabular_LLMs_human_study_vis_6_POS.json' elif method == "Dater": json_file = 'Tabular_LLMs_human_study_vis_6_DATER.json' elif method == "No-XAI": json_file = 'Tabular_LLMs_human_study_vis_6_NO_XAI.json' else: return "Invalid method", 400 with open(json_file, 'r') as f: ground_truth = json.load(f) correct_predictions = 0 true_predictions = 0 false_predictions = 0 for response in responses: sample_id = response['sample_id'] user_prediction = response['user_prediction'] visualization_file = session_data['selected_samples'][sample_id]['file'] index = visualization_file.split('-')[1].split('.')[0] ground_truth_key = f"{get_method_dir(method)}_test-{index}.html" logger.info(f"ground_truth_key: {ground_truth_key}") if ground_truth_key in ground_truth: model_prediction = ground_truth[ground_truth_key]['answer'].upper() if user_prediction.upper() == model_prediction: correct_predictions += 1 if user_prediction.upper() == "TRUE": true_predictions += 1 elif user_prediction.upper() == "FALSE": false_predictions += 1 else: logger.warning(f"Missing key in ground truth: {ground_truth_key}") accuracy = (correct_predictions / len(responses)) * 100 if responses else 0 accuracy = round(accuracy, 2) true_percentage = (true_predictions / len(responses)) * 100 if len(responses) else 0 false_percentage = (false_predictions / len(responses)) * 100 if len(responses) else 0 true_percentage = round(true_percentage, 2) false_percentage = round(false_percentage, 2) session_data['accuracy'] = accuracy session_data['true_percentage'] = true_percentage session_data['false_percentage'] = false_percentage save_session_data(username, session_data, is_update=True) # Update the existing file return render_template('completed.html', accuracy=accuracy, true_percentage=true_percentage, false_percentage=false_percentage) except Exception as e: logger.exception(f"An error occurred in the completed route: {e}") return "An error occurred", 500 @app.route('/visualizations/') def send_visualization(filename): logger.info(f"Attempting to serve file: {filename}") base_dir = os.getcwd() file_path = os.path.normpath(os.path.join(base_dir, filename)) if not file_path.startswith(base_dir): return "Access denied", 403 if not os.path.exists(file_path): return "File not found", 404 directory = os.path.dirname(file_path) file_name = os.path.basename(file_path) logger.info(f"Serving file from directory: {directory}, filename: {file_name}") return send_from_directory(directory, file_name) if __name__ == "__main__": app.run(host="0.0.0.0", port=7860, debug=True)