import os
import sys
import argparse
import json
from dotenv import load_dotenv

# Ensure project root is on sys.path
_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
if _ROOT not in sys.path:
    sys.path.insert(0, _ROOT)

# Import evaluation logic
from train.evaluate import evaluate_model, score_model, OpenEnvClient, start_server
from backend.api.llm_evaluator import get_llm_analysis

def main():
    parser = argparse.ArgumentParser(description="BESS-RL LLM Evaluation CLI")
    parser.add_argument("--task", type=str, default="hard", choices=["easy", "medium", "hard"], help="Task complexity")
    parser.add_argument("--seeds", type=int, default=5, help="Number of evaluation seeds")
    parser.add_argument("--model", type=str, default=None, help="Specific model name (defaults to best_model_<task>)")
    args = parser.parse_args()

    # Load API Key
    load_dotenv(os.path.join(_ROOT, ".env"))
    
    model_name = args.model if args.model else f"best_model_{args.task}"
    model_path = os.path.join(_ROOT, "train", "models", model_name)
    
    # Initialize Client and Server
    server_process = start_server()
    client = OpenEnvClient(base_url="http://127.0.0.1:8000")
    
    try:
        eval_seeds = list(range(300, 300 + args.seeds))
        results = evaluate_model(client, model_path, args.task, eval_seeds)
        scores, overall = score_model(results, args.task)
        
        # Prepare data for LLM
        llm_input = {
            **results,
            "task": args.task,
            "model_name": model_name,
            "num_seeds": args.seeds,
            "scores": {**scores, "overall": overall}
        }

        # Call LLM Evaluator
        analysis = get_llm_analysis(llm_input)

        # OUTPUT: Only Score and Verdict as requested
        final_score = analysis.get('score', overall)
        verdict = analysis.get('verdict', 'N/A')
        
        # print(f"Overall Score (AI Evaluated): {final_score:.3f}")
        provider = analysis.get('provider', 'AI')
        print(f"Overall Score ({provider} Evaluated): {final_score:.3f}")
        print(f"AI Verdict: {verdict}")
    finally:
        if server_process:
            server_process.terminate()

if __name__ == "__main__":
    main()