# This modules handles the task queue, results, and leaderboard storage.

import json
import uuid
from datetime import datetime
from pathlib import Path
from typing import Optional

import asyncio
import pandas as pd

from inference import evaluate_model

# Get absolute path
CURRENT_DIR = Path(__file__).parent.absolute()

# Constants
QUEUE_DIR = CURRENT_DIR / "queue"
PATHS = {
    "tasks": QUEUE_DIR / "tasks.json",
    "results": QUEUE_DIR / "results.json",
    "leaderboard": QUEUE_DIR / "leaderboard.json",
}


# Handle storing and loading data from JSON files
class StorageManager:
    """Handles all JSON storage operations"""

    def __init__(self, paths: dict[str, Path]):
        self.paths = paths
        self._ensure_directories()

    def _ensure_directories(self):
        """Ensure all necessary directories and files exist"""
        for path in self.paths.values():
            path.parent.mkdir(parents=True, exist_ok=True)
            if not path.exists():
                path.write_text("[]")

    def load(self, key: str) -> list:
        """Load JSON file"""
        return json.loads(self.paths[key].read_text())

    def save(self, key: str, data: list):
        """Save data to JSON file"""
        self.paths[key].write_text(
            json.dumps(data, indent=4, default=str, ensure_ascii=False)
        )

    def update_task(self, task_id: str, updates: dict):
        """Update specific task with new data"""
        tasks = self.load("tasks")
        for task in tasks:
            if task["id"] == task_id:
                task.update(updates)
                break
        self.save("tasks", tasks)


# Initialize storage manager
storage_manager = StorageManager(PATHS)


# Export external functions
def get_leaderboard_data():
    """Return leaderboard data as DataFrame"""
    try:
        return pd.DataFrame(storage_manager.load("leaderboard"))
    except Exception as e:
        print(f"Error loading leaderboard: {e}")
        return pd.DataFrame()


def get_results():
    """Return list of evaluation results"""
    return storage_manager.load("results")


def get_tasks():
    """Return list of tasks"""
    return storage_manager.load("tasks")


def get_status(query: str) -> dict:
    """Check status of a model evaluation task_id or model_name"""
    if not query:
        return {"error": "Please enter a model name or task ID"}

    try:
        results = get_results()
        tasks = get_tasks()

        # First try to find by task ID
        result = next((r for r in results if r["task_id"] == query), None)
        task = next((t for t in tasks if t["id"] == query), None)

        # If not found, try to find by model name
        if not result:
            result = next((r for r in results if r["model"] == query), None)
        if not task:
            task = next((t for t in tasks if t["model"] == query), None)

        if result:
            # If we found results, return them
            return {
                "status": "completed",
                "model": result["model"],
                "subset": result["subset"],
                "num_files": result["num_files"],
                "average_per": result["average_per"],
                "average_pwed": result["average_pwed"],
                "detailed_results": result["detailed_results"],
                "timestamp": result["timestamp"],
            }
        elif task:
            # If we only found task status, return that
            return task
        else:
            return {"error": f"No results found for '{query}'"}

    except Exception as e:
        print(f"Error checking status: {e}")
        return {"error": f"Error checking status: {str(e)}"}


def start_eval_task(
    model_name: str, submission_name: str, github_url: Optional[str] = None
) -> str:
    """Start evaluation task in background. Returns task ID that can be used to check status."""

    # Generate a task ID
    task_id = str(uuid.uuid4())

    # Create task entry
    task = {
        "id": task_id,
        "model": model_name,
        "subset": "test",
        "submission_name": submission_name,
        "github_url": github_url,
        "status": "queued",
        "submitted_at": datetime.now().isoformat(),
    }

    # Save task
    tasks = storage_manager.load("tasks")
    tasks.append(task)
    storage_manager.save("tasks", tasks)

    # Start evaluation in background
    asyncio.run(_eval_task(task_id, model_name, submission_name, "test", github_url))

    return task_id


async def _eval_task(
    task_id: str,
    model_name: str,
    submission_name: str,
    subset: str = "test",
    github_url: Optional[str] = None,
    max_samples: Optional[int] = None,
):
    """Background task to evaluate model and save updated results"""
    try:
        # Indicate task is processing
        storage_manager.update_task(task_id, {"status": "processing"})

        # Evaluate model
        result = evaluate_model(model_name, subset, max_samples)
        avg_per = result["average_per"]
        avg_pwed = result["average_pwed"]

        # Save results
        print("Saving results...")
        current_results = storage_manager.load("results")
        current_results.append(result)
        storage_manager.save("results", current_results)

        # Update leaderboard
        print("Updating leaderboard...")
        leaderboard = storage_manager.load("leaderboard")
        entry = next(
            (e for e in leaderboard if e["submission_name"] == submission_name),
            None,
        )

        if entry:
            # Simply update with new scores
            entry.update(
                {
                    "task_id": task_id,
                    "average_per": avg_per,
                    "average_pwed": avg_pwed,
                    "model": model_name,
                    "subset": subset,
                    "github_url": github_url,
                    "submission_date": datetime.now().isoformat(),
                }
            )
        else:
            leaderboard.append(
                {
                    "task_id": task_id,
                    "submission_id": str(uuid.uuid4()),
                    "submission_name": submission_name,
                    "model": model_name,
                    "average_per": avg_per,
                    "average_pwed": avg_pwed,
                    "subset": subset,
                    "github_url": github_url,
                    "submission_date": datetime.now().isoformat(),
                }
            )

        storage_manager.save("leaderboard", leaderboard)
        storage_manager.update_task(task_id, {"status": "completed"})
        print("Evaluation completed successfully")

    except Exception as e:
        error_msg = f"Evaluation failed: {str(e)}"
        print(error_msg)
        storage_manager.update_task(task_id, {"status": "failed", "error": error_msg})