import time
from datetime import datetime
from typing import Any, Dict

from celery import Task
from sqlalchemy.orm import Session

from .database import SessionLocal
from .models import Job
from .queue import celery_app


class DatabaseTask(Task):
    """Base task class with database session management."""

    _db: Session = None

    @property
    def db(self) -> Session:
        """Get database session."""
        if self._db is None:
            self._db = SessionLocal()
        return self._db

    def after_return(self, *args, **kwargs):
        """Clean up database session after task completion."""
        if self._db is not None:
            self._db.close()
            self._db = None


@celery_app.task(base=DatabaseTask, bind=True)
def process_train(
    self,
    job_id: int,
    parameters: Dict[str, Any],
    user_id: int,
) -> Dict[str, Any]:
    """Process a training job."""
    job = self.db.query(Job).filter(Job.id == job_id).first()
    if not job:
        return {"error": f"Job {job_id} not found"}

    try:
        # Update job status
        job.status = "running"
        job.started_at = datetime.utcnow()
        self.db.commit()

        # TODO: Implement actual training logic
        # This is a placeholder that simulates training
        time.sleep(5)  # Simulate training time
        result = {"accuracy": 0.95, "loss": 0.05}

        # Update job with results
        job.status = "completed"
        job.progress = 100.0
        job.result = result
        job.completed_at = datetime.utcnow()
        self.db.commit()

        return result

    except Exception as e:
        # Update job with error
        job.status = "failed"
        job.error = str(e)
        job.completed_at = datetime.utcnow()
        self.db.commit()
        raise


@celery_app.task(base=DatabaseTask, bind=True)
def process_predict(
    self,
    job_id: int,
    parameters: Dict[str, Any],
    user_id: int,
) -> Dict[str, Any]:
    """Process a prediction job."""
    job = self.db.query(Job).filter(Job.id == job_id).first()
    if not job:
        return {"error": f"Job {job_id} not found"}

    try:
        # Update job status
        job.status = "running"
        job.started_at = datetime.utcnow()
        self.db.commit()

        # TODO: Implement actual prediction logic
        # This is a placeholder that simulates prediction
        time.sleep(2)  # Simulate prediction time
        result = {"predictions": [0, 1, 0, 1, 0]}

        # Update job with results
        job.status = "completed"
        job.progress = 100.0
        job.result = result
        job.completed_at = datetime.utcnow()
        self.db.commit()

        return result

    except Exception as e:
        # Update job with error
        job.status = "failed"
        job.error = str(e)
        job.completed_at = datetime.utcnow()
        self.db.commit()
        raise


@celery_app.task(base=DatabaseTask, bind=True)
def process_evaluate(
    self,
    job_id: int,
    parameters: Dict[str, Any],
    user_id: int,
) -> Dict[str, Any]:
    """Process an evaluation job."""
    job = self.db.query(Job).filter(Job.id == job_id).first()
    if not job:
        return {"error": f"Job {job_id} not found"}

    try:
        # Update job status
        job.status = "running"
        job.started_at = datetime.utcnow()
        self.db.commit()

        # TODO: Implement actual evaluation logic
        # This is a placeholder that simulates evaluation
        time.sleep(3)  # Simulate evaluation time
        result = {"accuracy": 0.92, "precision": 0.91, "recall": 0.93}

        # Update job with results
        job.status = "completed"
        job.progress = 100.0
        job.result = result
        job.completed_at = datetime.utcnow()
        self.db.commit()

        return result

    except Exception as e:
        # Update job with error
        job.status = "failed"
        job.error = str(e)
        job.completed_at = datetime.utcnow()
        self.db.commit()
        raise
