#!/usr/bin/env python3
"""
Calculate success rates for each model in MarkdownValidator-MCP task
"""

import json
import os
from pathlib import Path
from collections import defaultdict

# Define base path and model list
BASE_DIR = Path("/Users/wzr/TOSEM-2025/RESULTS")
MODELS = [
    "DeepSeek-R1",
    "DeepSeek-V3-1",
    "GPT-4o-mini",
    "GPT-5",
    "Gemini-2.5-flash",
    "Gemini-2.5-flash-nothinking",
    "Qwen3-235b",
]
PROJECT_NAME = "MarkdownValidator-MCP"


def analyze_model_results():
    """Analyze execution results for each model"""
    results = {}

    for model in MODELS:
        test_results_dir = BASE_DIR / model / PROJECT_NAME / "test_results"

        if not test_results_dir.exists():
            print(
                f"Warning: test_results directory not found for model {model}: {test_results_dir}"
            )
            continue

        model_stats = {
            "success_count": 0,
            "failure_count": 0,
            "total_count": 0,
            "sessions": [],
        }

        # Iterate through all session subdirectories
        for session_dir in sorted(test_results_dir.iterdir()):
            if not session_dir.is_dir():
                continue

            execution_info = session_dir / "execution_info.json"
            if not execution_info.exists():
                continue

            try:
                with open(execution_info, "r", encoding="utf-8") as f:
                    exec_data = json.load(f)

                success = exec_data.get("success", False)
                input_file = exec_data.get("input_file", "unknown")

                model_stats["total_count"] += 1
                if success:
                    model_stats["success_count"] += 1
                else:
                    model_stats["failure_count"] += 1

                model_stats["sessions"].append(
                    {
                        "session": session_dir.name,
                        "success": success,
                        "input_file": input_file,
                        "error": exec_data.get("error"),
                    }
                )

            except Exception as e:
                print(f"Error reading {execution_info}: {e}")

        results[model] = model_stats

    return results


def print_summary(results):
    """Print statistics summary"""
    print("\n" + "=" * 80)
    print(f"Model Execution Results - {PROJECT_NAME}")
    print("=" * 80 + "\n")

    for model, stats in results.items():
        total = stats["total_count"]
        success = stats["success_count"]
        failure = stats["failure_count"]

        if total > 0:
            success_rate = (success / total) * 100
            print(f"{model}")
            print(f"   Total: {total} runs")
            print(f"   Success: {success} runs ({success_rate:.1f}%)")
            print(f"   Failure: {failure} runs ({100-success_rate:.1f}%)")
            print()
        else:
            print(f"{model}")
            print(f"   No data")
            print()

    print("=" * 80)


def save_detailed_results(results, output_file="success-finish_detailed_results.json"):
    """Save detailed results to JSON file"""
    output_path = Path(__file__).parent / output_file
    with open(output_path, "w", encoding="utf-8") as f:
        json.dump(results, f, indent=2, ensure_ascii=False)
    print(f"\nDetailed results saved to: {output_path}")


def save_csv_summary(results, output_file="success-finish_rate.csv"):
    """Save summary to CSV file"""
    import csv

    output_path = Path(__file__).parent / output_file
    with open(output_path, "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(["Model", "Total", "Success", "Failure", "Success_Rate(%)"])

        for model, stats in results.items():
            total = stats["total_count"]
            success = stats["success_count"]
            failure = stats["failure_count"]
            success_rate = (success / total * 100) if total > 0 else 0

            writer.writerow([model, total, success, failure, f"{success_rate:.2f}"])

    print(f"CSV summary saved to: {output_path}")


if __name__ == "__main__":
    results = analyze_model_results()
    print_summary(results)
    save_detailed_results(results)
    save_csv_summary(results)