// backend\embed.go

package main

import (
	"embed"
	"os"
	"path/filepath"
)

//go:embed ml/*.py ml/detection_labels.json ai_server.py .env recorder-cert.pem recorder-key.pem yolo26n-pose.pt
var embeddedMLFiles embed.FS

func embeddedWriteFileIfNeeded(dstPath string, b []byte, perm os.FileMode) error {
	if fi, err := os.Stat(dstPath); err == nil && fi != nil && !fi.IsDir() && fi.Size() == int64(len(b)) {
		return nil
	}

	return os.WriteFile(dstPath, b, perm)
}

func trainingEmbeddedMLDir() (string, error) {
	dir := filepath.Join(os.TempDir(), "nsfwapp-ml")

	if err := os.MkdirAll(dir, 0755); err != nil {
		return "", err
	}

	files := []string{
		"predict_detector_model.py",
		"train_detector_model.py",
		"detection_labels.json",
		"predict_pose_model.py",
		"train_pose_model.py",
		"predict_videomae_model.py",
		"train_videomae_model.py",
	}

	// Falls du die alten Scene-Skripte noch embedded hast, kannst du sie optional mitkopieren.
	optionalFiles := []string{
		"predict_scene_model.py",
		"train_scene_model.py",
	}

	for _, name := range append(files, optionalFiles...) {
		srcPath := filepath.ToSlash(filepath.Join("ml", name))

		b, err := embeddedMLFiles.ReadFile(srcPath)
		if err != nil {
			// Pflichtdateien müssen vorhanden sein.
			if name == "detection_labels.json" ||
				name == "predict_detector_model.py" ||
				name == "train_detector_model.py" {
				return "", err
			}

			// Optionale alte Dateien ignorieren.
			continue
		}

		dstPath := filepath.Join(dir, name)
		if err := embeddedWriteFileIfNeeded(dstPath, b, 0644); err != nil {
			return "", err
		}
	}

	if _, err := embeddedPoseModelPath(); err != nil {
		return "", err
	}

	return dir, nil
}

func embeddedMLScriptExists(name string) bool {
	srcPath := filepath.ToSlash(filepath.Join("ml", name))
	_, err := embeddedMLFiles.ReadFile(srcPath)
	return err == nil
}

func embeddedAIServerDir() (string, error) {
	dir := filepath.Join(os.TempDir(), "nsfwapp-ai-server")

	if err := os.MkdirAll(dir, 0755); err != nil {
		return "", err
	}

	b, err := embeddedMLFiles.ReadFile("ai_server.py")
	if err != nil {
		return "", err
	}

	dstPath := filepath.Join(dir, "ai_server.py")
	if err := embeddedWriteFileIfNeeded(dstPath, b, 0644); err != nil {
		return "", err
	}

	return dir, nil
}

func embeddedPoseModelPath() (string, error) {
	dir := filepath.Join(os.TempDir(), "nsfwapp-ml")

	if err := os.MkdirAll(dir, 0755); err != nil {
		return "", err
	}

	b, err := embeddedMLFiles.ReadFile("yolo26n-pose.pt")
	if err != nil {
		return "", err
	}

	dstPath := filepath.Join(dir, "yolo26n-pose.pt")
	if err := embeddedWriteFileIfNeeded(dstPath, b, 0644); err != nil {
		return "", err
	}

	return dstPath, nil
}

func embeddedDotEnvBytes() ([]byte, error) {
	return embeddedMLFiles.ReadFile(".env")
}

func embeddedTLSCertBytes() ([]byte, error) {
	return embeddedMLFiles.ReadFile("recorder-cert.pem")
}

func embeddedTLSKeyBytes() ([]byte, error) {
	return embeddedMLFiles.ReadFile("recorder-key.pem")
}

func embeddedTLSFilesDir() (string, string, error) {
	dir := filepath.Join(os.TempDir(), "nsfwapp-tls")

	if err := os.MkdirAll(dir, 0700); err != nil {
		return "", "", err
	}

	certBytes, err := embeddedTLSCertBytes()
	if err != nil {
		return "", "", err
	}

	keyBytes, err := embeddedTLSKeyBytes()
	if err != nil {
		return "", "", err
	}

	certPath := filepath.Join(dir, "recorder-cert.pem")
	keyPath := filepath.Join(dir, "recorder-key.pem")

	if err := os.WriteFile(certPath, certBytes, 0600); err != nil {
		return "", "", err
	}

	if err := os.WriteFile(keyPath, keyBytes, 0600); err != nil {
		return "", "", err
	}

	return certPath, keyPath, nil
}