from evaluate_code.llm_evaluator import LLMEvaluator
from datetime import datetime
from config import TASKS, MODEL_NAMES
import os
import json
import csv

def save_results(date_str,results, task_name):
    """
    Save results to both JSON and CSV files
    
    Args:
        results: Dictionary containing evaluation results
        task_name: Name of the task
    """
    # Create results directory if it doesn't exist
    if not os.path.exists("results"):
        os.makedirs("results")
    
    # Create date folder
    date_path = os.path.join("results", date_str)
    if not os.path.exists(date_path):
        os.makedirs(date_path)
    
    # Create task folder
    task_path = os.path.join(date_path, task_name)
    if not os.path.exists(task_path):
        os.makedirs(task_path)
    
    # Save JSON
    json_path = os.path.join(task_path, "results.json")
    with open(json_path, 'w', encoding='utf-8') as f:
        json.dump(results, f, ensure_ascii=False, indent=2)
    if task_name in ["S_0D", "S_1D", "S_Modification", "M_Merge", "M_Birth", "M_Filtration", "R_Selection", "R_Generation"]:
        # Save CSV
        csv_path = os.path.join(task_path, "results.csv")
        with open(csv_path, 'w', newline='', encoding='utf-8') as f:
            writer = csv.writer(f)
            # Write header
            writer.writerow(['file_name', 'accuracy'])
            # Write data
            for file_name, (accuracy, _) in results.items():
                # Remove .parquet extension
                file_name = file_name.replace('.parquet', '')
                writer.writerow([file_name, accuracy])
    elif task_name in ["H_Selection", "H_Generation"]:
        # Save CSV
        csv_path = os.path.join(task_path, "results.csv")
        with open(csv_path, 'w', newline='', encoding='utf-8') as f:
            writer = csv.writer(f)
            # Write header
            writer.writerow(['file_name', 'mean_rank', 'std_rank'])
            # Write data
            for file_name, (_, details) in results.items():
                mean_rank = details['statistics']['mean_rank']
                std_rank = details['statistics']['std_rank']
                file_name = file_name.replace('.parquet', '')
                writer.writerow([file_name, mean_rank, std_rank])

def main():
    date_str = datetime.now().strftime("%Y%m%d_%H%M")
    # Process each task in the task list
    for task_name in TASKS:
        for model_name in MODEL_NAMES:
            print(f"Processing task: {task_name} with model: {model_name}")
            evaluator = LLMEvaluator(
                task_name=task_name,
                model_name=model_name
            )
        # Process all graphs in all files
        results = evaluator.process_dataset()
        # Save results
        save_results(date_str, results, task_name)


if __name__ == "__main__":
    main()