ai-detection/app/main.py

import os
import sys
import json
import base64
import subprocess
from io import BytesIO
import numpy as np
from PIL import Image, ImageDraw
import cv2
import albumentations as A
import ctypes
import time
import socket
import threading

from fastapi import FastAPI, HTTPException, BackgroundTasks
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse, JSONResponse
from pydantic import BaseModel

from inference import FruitVegetableDetector, CLASSES

app = FastAPI(title="POS Object Detection API", description="Fruit & Vegetable checkout detection microservice")

# Initialize detector
detector = FruitVegetableDetector()

# Global variable to track active training process
current_training_process = None

import re

# Paths
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
STATUS_FILE = os.path.join(BASE_DIR, "training_status.json")

# Data Models
class DetectionRequest(BaseModel):
    image: str  # Base64 encoded image or Data URL

class TrainRequest(BaseModel):
    epochs: int = 15
    batch_size: int = 8
    lr: float = 0.001

class CaptureRequest(BaseModel):
    image: str
    class_name: str
    bbox: dict = None  # Optional client-side bounding box dictionary

class SaveArticlesRequest(BaseModel):
    articles: dict

class ScanRequest(BaseModel):
    class_name: str

class CalibrateRequest(BaseModel):
    image: str

class DeleteCaptureRequest(BaseModel):
    filename: str


# Serve frontend static files
static_path = os.path.join(os.path.dirname(__file__), "static")
os.makedirs(static_path, exist_ok=True)

# Helper function to read training status
def get_training_status():
    if not os.path.exists(STATUS_FILE):
        return {
            "status": "idle",
            "epoch": 0,
            "total_epochs": 0,
            "train_loss": [],
            "val_loss": [],
            "progress": 0.0,
            "message": "No training history found."
        }
    try:
        with open(STATUS_FILE, "r") as f:
            return json.load(f)
    except Exception:
        return {"status": "error", "message": "Failed to read status file."}

# Helper to write status
def write_training_status(status_dict):
    with open(STATUS_FILE, "w") as f:
        json.dump(status_dict, f, indent=2)

def is_safe_filename(filename: str) -> bool:
    return re.match(r"^user_capture_\d+\.jpg$", filename) is not None

def calculate_auto_crop_bbox(image_rgb: np.ndarray, background_path: str, threshold: int = 25) -> list:
    """
    Compares the current image with the saved background.
    Returns normalized YOLO bounding box [x_center, y_center, width, height] if found, else None.
    """
    if not os.path.exists(background_path):
        return None
    try:
        # Load background image
        bg_img = cv2.imread(background_path)
        if bg_img is None:
            return None
        # Convert to grayscale
        bg_gray = cv2.cvtColor(bg_img, cv2.COLOR_BGR2GRAY)
        
        # Ensure they have the same dimensions
        h_img, w_img, _ = image_rgb.shape
        if bg_gray.shape[0] != h_img or bg_gray.shape[1] != w_img:
            bg_gray = cv2.resize(bg_gray, (w_img, h_img))
            
        # Convert current image to grayscale (assuming input is RGB)
        curr_gray = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2GRAY)
        
        # Calculate absolute difference
        diff = cv2.absdiff(bg_gray, curr_gray)
        
        # Apply thresholding
        _, thresh = cv2.threshold(diff, threshold, 255, cv2.THRESH_BINARY)
        
        # Morphological operations to remove noise and fill holes
        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
        thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
        thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
        
        # Find contours
        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        if not contours:
            return None
            
        # Filter contours by size and find the largest
        large_contours = [c for c in contours if cv2.contourArea(c) > 150]
        if not large_contours:
            return None
            
        largest_contour = max(large_contours, key=cv2.contourArea)
        x, y, w, h = cv2.boundingRect(largest_contour)
        
        # Add padding around the bounding box
        padding = 15
        x_min = max(0, x - padding)
        y_min = max(0, y - padding)
        x_max = min(w_img, x + w + padding)
        y_max = min(h_img, y + h + padding)
        
        box_w = x_max - x_min
        box_h = y_max - y_min
        
        # Calculate YOLO format values
        x_center = (x_min + box_w / 2.0) / w_img
        y_center = (y_min + box_h / 2.0) / h_img
        norm_w = box_w / w_img
        norm_h = box_h / h_img
        
        return [x_center, y_center, norm_w, norm_h]
    except Exception as e:
        print(f"Error in calculate_auto_crop_bbox: {e}")
        return None


@app.post("/api/detect")
async def detect_objects(payload: DetectionRequest):
    """
    Accepts base64 encoded frame, runs ONNX inference, and returns detections.
    """
    try:
        data_url = payload.image
        if "," in data_url:
            data_url = data_url.split(",")[1]
            
        img_bytes = base64.b64decode(data_url)
        img = Image.open(BytesIO(img_bytes)).convert("RGB")
        img_np = np.array(img)
        
        # Check if empty background calibration exists and compare
        bg_path = os.path.join(BASE_DIR, "dataset", "background.jpg")
        if os.path.exists(bg_path):
            # Use a slightly lower threshold (20) to ensure we capture any real item placed
            bbox = calculate_auto_crop_bbox(img_np, bg_path, threshold=20)
            if bbox is None:
                # Scale is empty or no significant object placed
                return {
                    "status": "success",
                    "predictions": []
                }
        
        predictions = detector.detect(img_np)
        
        return {
            "status": "success",
            "predictions": predictions
        }
    except Exception as e:
        return JSONResponse(
            status_code=400,
            content={"status": "error", "message": f"Inference failed: {str(e)}"}
        )

def bg_generate_dataset():
    try:
        from data_generator import build_dataset
        build_dataset(base_dir=os.path.join(BASE_DIR, "dataset"), train_count=200, val_count=50)
        write_training_status({
            "status": "idle",
            "epoch": 0,
            "total_epochs": 0,
            "train_loss": [],
            "val_loss": [],
            "progress": 100.0,
            "message": "Dataset generation completed successfully!"
        })
    except Exception as e:
        write_training_status({
            "status": "failed",
            "epoch": 0,
            "total_epochs": 0,
            "train_loss": [],
            "val_loss": [],
            "progress": 0.0,
            "message": f"Dataset generation failed: {str(e)}"
        })

@app.post("/api/generate_dataset")
async def trigger_dataset_generation(background_tasks: BackgroundTasks):
    """
    Triggers dataset generation asynchronously.
    """
    status = get_training_status()
    if status.get("status") in ["training", "generating_data"]:
        raise HTTPException(status_code=400, detail="An operation is already in progress.")
        
    write_training_status({
        "status": "generating_data",
        "epoch": 0,
        "total_epochs": 0,
        "train_loss": [],
        "val_loss": [],
        "progress": 10.0,
        "message": "Starting synthetic data generation process..."
    })
    
    background_tasks.add_task(bg_generate_dataset)
    
    return {"status": "success", "message": "Dataset generation started in the background."}

@app.post("/api/train")
async def trigger_training(req: TrainRequest):
    """
    Triggers model training.
    """
    global current_training_process
    status = get_training_status()
    if status.get("status") in ["training", "generating_data"]:
        raise HTTPException(status_code=400, detail="Training or data generation is already in progress.")
        
    write_training_status({
        "status": "training",
        "epoch": 0,
        "total_epochs": req.epochs,
        "train_loss": [],
        "val_loss": [],
        "progress": 5.0,
        "message": "Initializing training background process..."
    })
    
    # Launch training process in background with reduced CPU priority
    # so the FastAPI server stays responsive during CPU-heavy training
    cmd = [
        sys.executable, 
        os.path.join(BASE_DIR, "train.py"),
        "--epochs", str(req.epochs),
        "--batch-size", str(req.batch_size),
        "--lr", str(req.lr)
    ]
    
    env = os.environ.copy()
    env["PYTHONUTF8"] = "1"
    proc = subprocess.Popen(cmd, cwd=BASE_DIR, env=env)
    current_training_process = proc
    
    # On Windows: lower the training process priority so the web server
    # remains responsive during CPU-intensive training
    if sys.platform == "win32":
        try:
            import ctypes
            BELOW_NORMAL_PRIORITY_CLASS = 0x00004000
            ctypes.windll.kernel32.SetPriorityClass(
                ctypes.windll.kernel32.OpenProcess(0x0200, False, proc.pid),
                BELOW_NORMAL_PRIORITY_CLASS
            )
            print(f"Training process (PID {proc.pid}) set to BELOW_NORMAL priority.")
        except Exception as e:
            print(f"Could not set process priority: {e}")
    
    return {"status": "success", "message": f"Training initiated for {req.epochs} epochs."}

@app.get("/api/train_status")
async def get_status():
    """
    Returns current training status.
    """
    global current_training_process
    status = get_training_status()
    
    # Auto-detect if process died unexpectedly
    if status.get("status") == "training":
        if current_training_process is None or current_training_process.poll() is not None:
            status["status"] = "failed"
            status["message"] = "Training-Prozess unerwartet beendet."
            write_training_status(status)
            current_training_process = None
            
    return status

@app.post("/api/cancel_training")
async def cancel_training():
    global current_training_process
    if current_training_process is None or current_training_process.poll() is not None:
        # Fallback: kill via PowerShell command on Windows
        if sys.platform == "win32":
            try:
                import subprocess as sp
                sp.run("powershell -Command \"Get-CimInstance Win32_Process | Where-Object { $_.CommandLine -like '*train.py*' } | ForEach-Object { Stop-Process -Id $_.ProcessId -Force }\"", shell=True, capture_output=True)
            except Exception:
                pass
        
        status = get_training_status()
        status["status"] = "failed"
        status["message"] = "Training vom Benutzer abgebrochen."
        write_training_status(status)
        current_training_process = None
        
        return {"status": "success", "message": "Training abgebrochen."}
        
    try:
        current_training_process.terminate()
        current_training_process.wait(timeout=2.0)
    except Exception:
        try:
            current_training_process.kill()
        except Exception:
            pass
            
    current_training_process = None
    
    # Read status, set to failed
    status = get_training_status()
    status["status"] = "failed"
    status["message"] = "Training vom Benutzer abgebrochen."
    write_training_status(status)
    
    return {"status": "success", "message": "Training erfolgreich abgebrochen."}

@app.post("/api/export")
async def trigger_export():
    """
    Exports trained PyTorch weights to ONNX format and reloads the detector.
    """
    pytorch_path = os.path.join(BASE_DIR, "models", "model.pt")
    if not os.path.exists(pytorch_path):
        raise HTTPException(status_code=404, detail="No trained PyTorch weights found at models/model.pt. Train a model first.")
        
    try:
        # Run export script synchronously since it is fast (few seconds)
        env = os.environ.copy()
        env["PYTHONUTF8"] = "1"
        cmd = [sys.executable, os.path.join(BASE_DIR, "export.py")]
        result = subprocess.run(cmd, cwd=BASE_DIR, capture_output=True, text=True, check=True, env=env)
        
        # Reload the detector model
        detector.load_model()
        
        return {
            "status": "success", 
            "message": "Model successfully exported to ONNX and loaded into memory.",
            "log": result.stdout
        }
    except Exception as e:
        return JSONResponse(
            status_code=500,
            content={"status": "error", "message": f"ONNX Export failed: {str(e)}"}
        )

@app.get("/api/augmented_preview")
async def get_augmented_preview():
    """
    Generates a sample preview image from the generator, applies augmentations,
    draws bounding boxes on both, and returns them side-by-side.
    """
    try:
        from data_generator import generate_sample
        
        # Generate a raw sample image and its YOLO annotations
        img, annotations = generate_sample(width=320, height=320)
        img_np = np.array(img)
        
        # Setup the same augmentation pipeline used in training
        transform = A.Compose([
            A.ShiftScaleRotate(shift_limit=0.1, scale_limit=0.15, rotate_limit=30, p=1.0),
            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=1.0),
            A.GaussNoise(p=1.0)
        ], bbox_params=A.BboxParams(format='pascal_voc', label_fields=['category_ids']))
        
        # Parse annotations to box absolute coords
        boxes = []
        classes = []
        for ann in annotations:
            parts = ann.strip().split()
            if len(parts) == 5:
                class_id = int(parts[0])
                xc, yc, w, h = map(float, parts[1:])
                xmin = (xc - w/2) * 320
                ymin = (yc - h/2) * 320
                xmax = (xc + w/2) * 320
                ymax = (yc + h/2) * 320
                boxes.append([xmin, ymin, xmax, ymax])
                classes.append(class_id)
                
        boxes = np.array(boxes, dtype=np.float32).reshape(-1, 4)
        
        # Create augmented image
        augmented_np = img_np.copy()
        aug_boxes = boxes.copy()
        if len(boxes) > 0:
            try:
                augmented = transform(image=img_np, bboxes=boxes, category_ids=classes)
                augmented_np = augmented["image"]
                aug_boxes = np.array(augmented["bboxes"], dtype=np.float32).reshape(-1, 4)
            except Exception:
                pass
                
        # Draw bounding boxes on original image
        orig_draw = img.copy()
        draw_tool = ImageDraw.Draw(orig_draw)
        for idx, box in enumerate(boxes):
            draw_tool.rectangle(box.tolist(), outline=(255, 0, 0), width=3)
            draw_tool.text((box[0]+2, box[1]+2), CLASSES[classes[idx]], fill=(255, 0, 0))
            
        # Draw bounding boxes on augmented image
        aug_img = Image.fromarray(augmented_np)
        draw_tool_aug = ImageDraw.Draw(aug_img)
        for idx, box in enumerate(aug_boxes):
            draw_tool_aug.rectangle(box.tolist(), outline=(0, 255, 0), width=3)
            draw_tool_aug.text((box[0]+2, box[1]+2), CLASSES[classes[idx]], fill=(0, 255, 0))
            
        # Create side-by-side preview canvas
        preview = Image.new("RGB", (640, 320))
        preview.paste(orig_draw, (0, 0))
        preview.paste(aug_img, (320, 0))
        
        # Buffer and return as base64
        buffered = BytesIO()
        preview.save(buffered, format="JPEG")
        preview_base64 = base64.b64encode(buffered.getvalue()).decode()
        
        return {
            "status": "success",
            "image": f"data:image/jpeg;base64,{preview_base64}"
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to generate preview: {str(e)}")

@app.get("/api/simulated_frame")
async def get_simulated_frame():
    """
    Generates a raw synthetic scale frame (without annotations) for client-side POS testing.
    """
    try:
        from data_generator import generate_sample
        # Generate raw sample image (640x480)
        img, _ = generate_sample(width=640, height=480)
        
        buffered = BytesIO()
        img.save(buffered, format="JPEG")
        img_base64 = base64.b64encode(buffered.getvalue()).decode()
        
        return {
            "status": "success",
            "image": f"data:image/jpeg;base64,{img_base64}"
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to generate simulated frame: {str(e)}")

@app.post("/api/calibrate_background")
async def calibrate_background(payload: CalibrateRequest):
    """
    Saves a webcam snapshot of the empty scale to use as the background baseline.
    """
    try:
        data_url = payload.image
        if "," in data_url:
            data_url = data_url.split(",")[1]
            
        img_bytes = base64.b64decode(data_url)
        img = Image.open(BytesIO(img_bytes)).convert("RGB")
        
        bg_path = os.path.join(BASE_DIR, "dataset", "background.jpg")
        os.makedirs(os.path.dirname(bg_path), exist_ok=True)
        img.save(bg_path, "JPEG", quality=95)
        
        return {
            "status": "success",
            "message": "Hintergrund erfolgreich kalibriert und als Null-Linie gespeichert!"
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Hintergrundkalibrierung fehlgeschlagen: {str(e)}")

@app.get("/api/has_background")
async def has_background():
    """
    Checks if a baseline background image exists and returns metadata.
    """
    bg_path = os.path.join(BASE_DIR, "dataset", "background.jpg")
    exists = os.path.exists(bg_path)
    timestamp = None
    if exists:
        timestamp = os.path.getmtime(bg_path)
    return {
        "has_background": exists,
        "timestamp": timestamp
    }

@app.post("/api/reset_background")
async def reset_background():
    """
    Removes the baseline background image calibration.
    """
    bg_path = os.path.join(BASE_DIR, "dataset", "background.jpg")
    if os.path.exists(bg_path):
        try:
            os.remove(bg_path)
        except Exception as e:
            raise HTTPException(status_code=500, detail=f"Fehler beim Zurücksetzen: {str(e)}")
    return {"status": "success", "message": "Hintergrundkalibrierung zurückgesetzt."}

@app.post("/api/reset_dataset")
async def reset_dataset():
    """
    Deletes all user-captured images, labels, and the baseline background image calibration.
    """
    images_dir = os.path.join(BASE_DIR, "dataset", "images", "train")
    labels_dir = os.path.join(BASE_DIR, "dataset", "labels", "train")
    bg_path = os.path.join(BASE_DIR, "dataset", "background.jpg")
    
    count_deleted = 0
    try:
        # Delete images starting with user_capture_
        if os.path.exists(images_dir):
            for f in os.listdir(images_dir):
                if f.startswith("user_capture_"):
                    try:
                        os.remove(os.path.join(images_dir, f))
                        count_deleted += 1
                    except Exception:
                        pass
                    
        # Delete labels starting with user_capture_
        if os.path.exists(labels_dir):
            for f in os.listdir(labels_dir):
                if f.startswith("user_capture_"):
                    try:
                        os.remove(os.path.join(labels_dir, f))
                    except Exception:
                        pass
                    
        # Delete background
        if os.path.exists(bg_path):
            try:
                os.remove(bg_path)
            except Exception:
                pass
            
        return {
            "status": "success",
            "message": f"Datenbank erfolgreich zurückgesetzt! {count_deleted} selbst erfasste Bilder gelöscht."
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Fehler beim Zurücksetzen: {str(e)}")

@app.post("/api/reset_model")
async def reset_model():
    """
    Deletes the trained PyTorch model and exported ONNX model,
    resets training status, and reloads the detector.
    """
    pytorch_path = os.path.join(BASE_DIR, "models", "model.pt")
    onnx_path = os.path.join(BASE_DIR, "models", "model.onnx")
    
    try:
        deleted_any = False
        if os.path.exists(pytorch_path):
            os.remove(pytorch_path)
            deleted_any = True
        if os.path.exists(onnx_path):
            os.remove(onnx_path)
            deleted_any = True
            
        # Re-initialize training status to idle
        write_training_status({
            "status": "idle",
            "epoch": 0,
            "total_epochs": 0,
            "train_loss": [],
            "val_loss": [],
            "progress": 0.0,
            "message": "AI model has been reset to default/simulated state."
        })
        
        # Reload detector so it goes back to simulated mode
        detector.load_model()
        
        return {
            "status": "success",
            "message": "KI-Modell erfolgreich zurückgesetzt. Das System läuft nun wieder im Simulations-Modus."
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Fehler beim Zurücksetzen des Modells: {str(e)}")

@app.post("/api/capture_training_image")
async def capture_training_image(payload: CaptureRequest):
    """
    Saves a webcam snapshot and generates a YOLO format bounding box label.
    Uses OpenCV background subtraction if calibrated, falls back to client bbox or default center.
    """
    try:
        class_name = payload.class_name
        if class_name not in CLASSES:
            raise HTTPException(status_code=400, detail=f"Ungültige Produktklasse: {class_name}")
            
        class_idx = CLASSES.index(class_name)
        
        # Decode base64
        data_url = payload.image
        if "," in data_url:
            data_url = data_url.split(",")[1]
        img_bytes = base64.b64decode(data_url)
        img = Image.open(BytesIO(img_bytes)).convert("RGB")
        img_np = np.array(img)
        
        # Save image with unique filename
        import time
        timestamp = int(time.time() * 1000)
        filename = f"user_capture_{timestamp}.jpg"
        
        images_dir = os.path.join(BASE_DIR, "dataset", "images", "train")
        labels_dir = os.path.join(BASE_DIR, "dataset", "labels", "train")
        os.makedirs(images_dir, exist_ok=True)
        os.makedirs(labels_dir, exist_ok=True)
        
        img.save(os.path.join(images_dir, filename), quality=95)
        
        # Calculate bounding box using background subtraction
        bg_path = os.path.join(BASE_DIR, "dataset", "background.jpg")
        yolo_box = calculate_auto_crop_bbox(img_np, bg_path)
        
        if yolo_box is None:
            # Fallback to client-side bounding box if provided
            if payload.bbox and all(k in payload.bbox for k in ["xmin", "ymin", "xmax", "ymax"]):
                h_img, w_img, _ = img_np.shape
                xmin = payload.bbox["xmin"]
                ymin = payload.bbox["ymin"]
                xmax = payload.bbox["xmax"]
                ymax = payload.bbox["ymax"]
                box_w = xmax - xmin
                box_h = ymax - ymin
                x_center = (xmin + box_w / 2.0) / w_img
                y_center = (ymin + box_h / 2.0) / h_img
                norm_w = box_w / w_img
                norm_h = box_h / h_img
                yolo_box = [x_center, y_center, norm_w, norm_h]
                
        if yolo_box is None:
            # Fallback to default centered box
            yolo_box = [0.500000, 0.500000, 0.650000, 0.650000]
            
        # Save YOLO label file
        label_filename = f"user_capture_{timestamp}.txt"
        with open(os.path.join(labels_dir, label_filename), "w") as f:
            f.write(f"{class_idx} {yolo_box[0]:.6f} {yolo_box[1]:.6f} {yolo_box[2]:.6f} {yolo_box[3]:.6f}\n")
            
        # Count total user captured images
        user_images = [f for f in os.listdir(images_dir) if f.startswith("user_capture_")]
        count = len(user_images)
        
        return {
            "status": "success",
            "message": f"Bild erfolgreich als Training für {class_name} erfasst!",
            "count": count,
            "filename": filename
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Fehler bei Bild-Erfassung: {str(e)}")

@app.post("/api/delete_captured_image")
async def delete_captured_image(payload: DeleteCaptureRequest):
    """
    Deletes a user-captured image and its corresponding YOLO label file.
    """
    filename = payload.filename
    if not is_safe_filename(filename):
        raise HTTPException(status_code=400, detail="Ungültiger Dateiname.")
        
    images_dir = os.path.join(BASE_DIR, "dataset", "images", "train")
    labels_dir = os.path.join(BASE_DIR, "dataset", "labels", "train")
    
    img_path = os.path.join(images_dir, filename)
    lbl_path = os.path.join(labels_dir, filename.replace(".jpg", ".txt"))
    
    deleted = False
    try:
        if os.path.exists(img_path):
            os.remove(img_path)
            deleted = True
        if os.path.exists(lbl_path):
            os.remove(lbl_path)
            deleted = True
            
        if not deleted:
            raise HTTPException(status_code=404, detail="Datei nicht gefunden.")
            
        # Recount total user captured images
        user_images = [f for f in os.listdir(images_dir) if f.startswith("user_capture_")]
        count = len(user_images)
        
        return {
            "status": "success",
            "message": f"Bild {filename} und Label erfolgreich gelöscht.",
            "count": count
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Fehler beim Löschen der Datei: {str(e)}")

@app.get("/api/captured_images/{filename}")
async def get_captured_image(filename: str):
    """
    Serves a user-captured training image file securely.
    """
    if not is_safe_filename(filename):
        raise HTTPException(status_code=400, detail="Ungültiger Dateiname.")
        
    img_path = os.path.join(BASE_DIR, "dataset", "images", "train", filename)
    if not os.path.exists(img_path):
        raise HTTPException(status_code=404, detail="Bild nicht gefunden.")
        
    return FileResponse(img_path)


@app.get("/api/capture_count")
async def get_capture_count():
    """
    Returns the number of user captured training images.
    """
    images_dir = os.path.join(BASE_DIR, "dataset", "images", "train")
    if not os.path.exists(images_dir):
        return {"count": 0}
    user_images = [f for f in os.listdir(images_dir) if f.startswith("user_capture_")]
    return {"count": len(user_images)}

# --- BARCODE SCANNER EMULATION (Windows Keyboard Hook) ---
KEYEVENTF_KEYUP = 0x0002
VK_RETURN = 0x0D
ARTICLES_FILE = os.path.join(BASE_DIR, "articles.json")

def simulate_keyboard_type(text: str):
    """
    Types the EAN code and presses Enter globally on Windows.
    Uses hardware scan codes mapped from virtual key codes for highest compatibility.
    """
    try:
        # Give a small 200ms delay to allow cashier window to gain focus if clicked manually
        time.sleep(0.2)
        
        KEYEVENTF_KEYUP = 0x0002
        MAPVK_VK_TO_VSC = 0
        
        for char in text:
            vk = None
            if '0' <= char <= '9':
                vk = 0x30 + int(char)
            elif 'A' <= char <= 'Z':
                vk = ord(char)
            elif 'a' <= char <= 'z':
                vk = ord(char.upper())
                
            if vk is not None:
                # Map to hardware scan code
                scan_code = ctypes.windll.user32.MapVirtualKeyW(vk, MAPVK_VK_TO_VSC)
                
                # Key Down
                ctypes.windll.user32.keybd_event(vk, scan_code, 0, 0)
                time.sleep(0.001)
                
                # Key Up
                ctypes.windll.user32.keybd_event(vk, scan_code, KEYEVENTF_KEYUP, 0)
                time.sleep(0.001)
                
        # Send VK_RETURN (Enter) with scan code
        vk_ret = 0x0D
        scan_ret = ctypes.windll.user32.MapVirtualKeyW(vk_ret, MAPVK_VK_TO_VSC)
        
        ctypes.windll.user32.keybd_event(vk_ret, scan_ret, 0, 0)
        time.sleep(0.001)
        ctypes.windll.user32.keybd_event(vk_ret, scan_ret, KEYEVENTF_KEYUP, 0)
        
        return True
    except Exception as e:
        print(f"Failed to type keyboard input: {e}", file=sys.stderr)
        return False

@app.get("/api/background_image")
async def get_background_image():
    """
    Serves the calibrated background image.
    """
    bg_path = os.path.join(BASE_DIR, "dataset", "background.jpg")
    if not os.path.exists(bg_path):
        raise HTTPException(status_code=404, detail="Hintergrund nicht kalibriert.")
    return FileResponse(bg_path)

@app.get("/api/articles")
async def get_articles():
    if not os.path.exists(ARTICLES_FILE):
        return {}
    try:
        with open(ARTICLES_FILE, "r") as f:
            return json.load(f)
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Fehler beim Laden der Artikeldatenbank: {str(e)}")

@app.post("/api/articles")
async def save_articles(payload: SaveArticlesRequest):
    try:
        with open(ARTICLES_FILE, "w") as f:
            json.dump(payload.articles, f, indent=2)
        from data_generator import reload_classes
        reload_classes()
        return {"status": "success", "message": "Artikeldatenbank erfolgreich gespeichert!"}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Fehler beim Speichern der Artikeldatenbank: {str(e)}")

# --- TCP SOCKET CLIENT SENDER ---
TCP_PORT = 9000

def send_ean_via_tcp(ean_code: str):
    """
    Sends the EAN code to the cash register over TCP on port 9000.
    Since the cash register is the server (listening on port 9000), we connect
    as a client, transmit the barcode, and close the connection.
    """
    message = (ean_code + "\r\n").encode('utf-8')
    
    print(f"TCP Client Mode: Connecting to cash register at 127.0.0.1:{TCP_PORT}...", flush=True)
    try:
        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
            s.settimeout(0.5) # Fast timeout
            s.connect(('127.0.0.1', TCP_PORT))
            s.sendall(message)
            print(f"TCP Client: Successfully sent EAN {ean_code} to cash register.", flush=True)
            return True
    except Exception as e:
        print(f"TCP Client: Connection to cash register failed (is register listening on port {TCP_PORT}?): {e}", flush=True)
        return False

@app.post("/api/scan_ean")
async def scan_ean_endpoint(payload: ScanRequest):
    try:
        class_name = payload.class_name
        if not os.path.exists(ARTICLES_FILE):
            raise HTTPException(status_code=404, detail="Artikeldatenbank existiert nicht.")
            
        with open(ARTICLES_FILE, "r") as f:
            articles = json.load(f)
            
        if class_name not in articles:
            raise HTTPException(status_code=404, detail=f"Klasse {class_name} nicht in Artikeldatenbank.")
            
        ean_code = articles[class_name].get("ean", "")
        if not ean_code:
            raise HTTPException(status_code=400, detail=f"Kein EAN-Code für {class_name} hinterlegt.")
            
        # 1. Send via TCP (Direct socket integration)
        tcp_sent = send_ean_via_tcp(ean_code)
        
        # 2. Parallel keyboard emulation fallback on Windows
        keyboard_sent = simulate_keyboard_type(ean_code)
        
        if tcp_sent or keyboard_sent:
            methods = []
            if tcp_sent:
                methods.append("TCP")
            if keyboard_sent:
                methods.append("Tastatur")
            method_desc = " + ".join(methods)
            
            return {
                "status": "success",
                "class": class_name,
                "name": articles[class_name].get("name", class_name),
                "ean": ean_code,
                "message": f"Barcode {ean_code} gesendet via {method_desc}!"
            }
        else:
            raise HTTPException(status_code=500, detail="Übertragung fehlgeschlagen (TCP und Tastaturemulation gescheitert).")
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

# Mount static files (must be after api definitions)
app.mount("/", StaticFiles(directory=static_path, html=True), name="static")

@app.get("/")
async def root():
    return FileResponse(os.path.join(static_path, "index.html"))