import json
import os
import random
import shutil
import zipfile
from datetime import datetime
from pathlib import Path

from app.workers.celery_app import celery_app
from app.database import SessionLocal
from app.models import Export, Image, Species
from app.config import get_settings

settings = get_settings()


@celery_app.task(bind=True)
def generate_export(self, export_id: int):
    """Generate a zip export for CoreML training."""
    db = SessionLocal()
    try:
        export = db.query(Export).filter(Export.id == export_id).first()
        if not export:
            return {"error": "Export not found"}

        # Update status
        export.status = "generating"
        export.celery_task_id = self.request.id
        db.commit()

        # Parse filter criteria
        criteria = json.loads(export.filter_criteria) if export.filter_criteria else {}
        min_images = criteria.get("min_images_per_species", 100)
        licenses = criteria.get("licenses")
        min_quality = criteria.get("min_quality")
        species_ids = criteria.get("species_ids")

        # Build query for images
        query = db.query(Image).filter(Image.status == "downloaded")

        if licenses:
            query = query.filter(Image.license.in_(licenses))

        if min_quality:
            query = query.filter(Image.quality_score >= min_quality)

        if species_ids:
            query = query.filter(Image.species_id.in_(species_ids))

        # Group by species and filter by min count
        from sqlalchemy import func
        species_counts = db.query(
            Image.species_id,
            func.count(Image.id).label("count")
        ).filter(Image.status == "downloaded").group_by(Image.species_id).all()

        valid_species_ids = [s.species_id for s in species_counts if s.count >= min_images]

        if species_ids:
            valid_species_ids = [s for s in valid_species_ids if s in species_ids]

        if not valid_species_ids:
            export.status = "failed"
            export.error_message = "No species meet the criteria"
            export.completed_at = datetime.utcnow()
            db.commit()
            return {"error": "No species meet the criteria"}

        # Create export directory
        export_dir = Path(settings.exports_path) / f"export_{export_id}"
        train_dir = export_dir / "Training"
        test_dir = export_dir / "Testing"
        train_dir.mkdir(parents=True, exist_ok=True)
        test_dir.mkdir(parents=True, exist_ok=True)

        total_images = 0
        species_count = 0

        # Process each valid species
        for i, species_id in enumerate(valid_species_ids):
            species = db.query(Species).filter(Species.id == species_id).first()
            if not species:
                continue

            # Get images for this species
            images_query = query.filter(Image.species_id == species_id)
            if licenses:
                images_query = images_query.filter(Image.license.in_(licenses))
            if min_quality:
                images_query = images_query.filter(Image.quality_score >= min_quality)

            images = images_query.all()
            if len(images) < min_images:
                continue

            species_count += 1

            # Create species folders
            species_name = species.scientific_name.replace(" ", "_")
            (train_dir / species_name).mkdir(exist_ok=True)
            (test_dir / species_name).mkdir(exist_ok=True)

            # Shuffle and split
            random.shuffle(images)
            split_idx = int(len(images) * export.train_split)
            train_images = images[:split_idx]
            test_images = images[split_idx:]

            # Copy images
            for j, img in enumerate(train_images):
                if img.local_path and os.path.exists(img.local_path):
                    ext = Path(img.local_path).suffix or ".jpg"
                    dest = train_dir / species_name / f"img_{j:05d}{ext}"
                    shutil.copy2(img.local_path, dest)
                    total_images += 1

            for j, img in enumerate(test_images):
                if img.local_path and os.path.exists(img.local_path):
                    ext = Path(img.local_path).suffix or ".jpg"
                    dest = test_dir / species_name / f"img_{j:05d}{ext}"
                    shutil.copy2(img.local_path, dest)
                    total_images += 1

            # Update progress
            self.update_state(
                state="PROGRESS",
                meta={
                    "current": i + 1,
                    "total": len(valid_species_ids),
                    "species": species.scientific_name,
                }
            )

        # Create zip file
        zip_path = Path(settings.exports_path) / f"export_{export_id}.zip"
        with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zipf:
            for root, dirs, files in os.walk(export_dir):
                for file in files:
                    file_path = Path(root) / file
                    arcname = file_path.relative_to(export_dir)
                    zipf.write(file_path, arcname)

        # Clean up directory
        shutil.rmtree(export_dir)

        # Update export record
        export.status = "completed"
        export.file_path = str(zip_path)
        export.file_size = zip_path.stat().st_size
        export.species_count = species_count
        export.image_count = total_images
        export.completed_at = datetime.utcnow()
        db.commit()

        return {
            "status": "completed",
            "species_count": species_count,
            "image_count": total_images,
            "file_size": export.file_size,
        }

    except Exception as e:
        if export:
            export.status = "failed"
            export.error_message = str(e)
            export.completed_at = datetime.utcnow()
            db.commit()
        raise
    finally:
        db.close()