#!/usr/bin/env python3
"""
Hugging Face Spaces deployment for SPaCial AI OCR Service
FastAPI HTTP Server for OCR Service using PaddleOCR
"""

from fastapi import FastAPI, File, UploadFile, HTTPException, Query, Body, Request
from fastapi.responses import JSONResponse, Response
from fastapi.middleware.cors import CORSMiddleware
from fastapi.middleware.trustedhost import TrustedHostMiddleware
import uvicorn
from contextlib import asynccontextmanager
import cv2
import numpy as np
import os
import tempfile
import math
import re
from pathlib import Path
import logging
from collections import defaultdict
import time
import asyncio
import concurrent.futures
from functools import lru_cache
import atexit
import gc
import shutil
import json
try:
    import psutil
    PSUTIL_AVAILABLE = True
except ImportError:
    PSUTIL_AVAILABLE = False
    logger.warning("psutil not available - memory monitoring disabled")

# Excel imports
try:
    from openpyxl import Workbook
    from openpyxl.styles import Font, Alignment, PatternFill, Border, Side
    OPENPYXL_AVAILABLE = True
except ImportError:
    OPENPYXL_AVAILABLE = False
    print("Warning: openpyxl not available - Excel export will not work")

# Set up logging first
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Set environment variables BEFORE importing PaddleOCR
os.environ['PADDLE_PDX_MODEL_SOURCE'] = 'BOS'

# Determine base directory (works for both local and HF Spaces)
base_dir = os.getcwd()  # Always use current directory for simplicity
home_dir = os.getenv('HOME')
if home_dir and os.path.exists(home_dir) and os.access(home_dir, os.W_OK):
    base_dir = home_dir

# Set paths relative to base directory
paddle_home = os.path.join(base_dir, '.paddlex')
paddleocr_home = os.path.join(base_dir, '.paddleocr')
temp_dir = os.path.join(base_dir, 'temp')

# Set environment variables
os.environ['PADDLE_HOME'] = paddle_home
os.environ['PADDLEX_HOME'] = paddle_home
os.environ['PADDLEOCR_HOME'] = paddleocr_home
os.environ['TEMP'] = temp_dir
os.environ['TMP'] = temp_dir
os.environ['TMPDIR'] = temp_dir

logger.info(f"Using base directory: {base_dir}")
logger.info(f"PaddleOCR home: {paddleocr_home}")
logger.info(f"Temp directory: {temp_dir}")

# Let PaddleOCR auto-detect GPU/CPU (newer versions handle this automatically)

# Create directories with proper permissions for all PaddleOCR models
def create_directories():
    """Create necessary directories for PaddleOCR"""
    directories_to_create = [
        paddle_home,
        paddleocr_home,
        os.path.join(paddleocr_home, 'whl'),
        os.path.join(paddleocr_home, 'whl', 'det', 'en', 'en_PP-OCRv3_det_infer'),
        os.path.join(paddleocr_home, 'whl', 'rec', 'en', 'en_PP-OCRv3_rec_infer'),
        os.path.join(paddleocr_home, 'whl', 'cls', 'en_ppocr_mobile_v2.0_cls_infer'),
        temp_dir
    ]
    
    created_count = 0
    for directory in directories_to_create:
        try:
            os.makedirs(directory, mode=0o755, exist_ok=True)
            created_count += 1
        except Exception as e:
            logger.warning(f"Failed to create directory {directory}: {e}")
    
    if created_count == len(directories_to_create):
        logger.info(f"Successfully created all {created_count} PaddleOCR directories")
    else:
        logger.warning(f"Created {created_count}/{len(directories_to_create)} directories, continuing with defaults...")

try:
    create_directories()
except Exception as e:
    logger.error(f"Failed to create directories: {e}")
    logger.warning("Continuing with default PaddleOCR paths...")

# Now import PaddleOCR
from paddleocr import PaddleOCR

# Import YOLOv11
try:
    from ultralytics import YOLO
    YOLO_AVAILABLE = True
    logger.info("✅ YOLOv11 (ultralytics) available")
except ImportError as e:
    YOLO_AVAILABLE = False
    logger.warning(f"⚠️ YOLOv11 not available: {e}")
    logger.info("Install with: pip install ultralytics")

# Import correction post-processor
try:
    from correction_post_processor import CorrectionPostProcessor
    correction_processor = CorrectionPostProcessor('correction_rules.json')
    logger.info("✅ Correction post-processor loaded")
except Exception as e:
    logger.warning(f"⚠️  Correction post-processor not available: {e}")
    correction_processor = None


# Global OCR instance (initialize once at startup)
ocr = None

# Global YOLO instance
yolo_model = None

# Thread pool for CPU-intensive tasks
executor = concurrent.futures.ThreadPoolExecutor(max_workers=4)

# Cleanup function for temporary files and resources
def cleanup_resources():
    """Clean up temporary files and resources"""
    try:
        # Force garbage collection
        gc.collect()
        
        # Log memory usage (if psutil is available)
        if PSUTIL_AVAILABLE:
            try:
                memory_info = psutil.virtual_memory()
                logger.info(f"Memory usage: {memory_info.percent}% ({memory_info.used / 1024 / 1024 / 1024:.2f}GB / {memory_info.total / 1024 / 1024 / 1024:.2f}GB)")
            except Exception as e:
                logger.warning(f"Failed to get memory info: {e}")
        else:
            logger.info("Memory monitoring not available (psutil not installed)")
        
        # Clean up temp directory
        temp_files = []
        for root, dirs, files in os.walk(temp_dir):
            for file in files:
                if file.startswith('temp_') or file.endswith('_temp.jpg') or file.endswith('_cropped.jpg'):
                    temp_files.append(os.path.join(root, file))
        
        for temp_file in temp_files:
            try:
                if os.path.exists(temp_file):
                    os.unlink(temp_file)
                    logger.info(f"Cleaned up temp file: {temp_file}")
            except Exception as e:
                logger.warning(f"Failed to clean up temp file {temp_file}: {e}")
                
    except Exception as e:
        logger.error(f"Error during cleanup: {e}")

# Register cleanup function
atexit.register(cleanup_resources)

# Periodic cleanup function (run every 30 minutes)
def periodic_cleanup():
    """Run periodic cleanup to manage resources"""
    while True:
        try:
            time.sleep(1800)  # 30 minutes
            cleanup_resources()
        except Exception as e:
            logger.error(f"Error in periodic cleanup: {e}")

# Start periodic cleanup in background thread
import threading
cleanup_thread = threading.Thread(target=periodic_cleanup, daemon=True)
cleanup_thread.start()

# Import PDF service (optional)
try:
    from pdf_service import pdf_service
    PDF_AVAILABLE = True
    logger.info("✅ PDF service loaded successfully")
except ImportError as e:
    PDF_AVAILABLE = False
    pdf_service = None
    logger.warning(f"⚠️ PDF service not available: {e}")
    logger.info("PDF export feature will be disabled")

# Advanced processing configuration
merge_overlapping_zones = False  # Disable merging to prevent over-merging dimensions
remove_duplicate_zones = True

@asynccontextmanager
async def lifespan(app: FastAPI):
    """Handle application lifespan events"""
    # Startup
    global ocr, executor, yolo_model
    logger.info("="*60)
    logger.info("🚀 STARTING APPLICATION INITIALIZATION")
    logger.info("="*60)
    logger.info("Initializing PaddleOCR model...")
    
    # Single OCR instance (auto GPU detection for newer PaddleOCR)
    try:
        logger.info("Initializing PaddleOCR with auto GPU detection...")
        logger.info("Creating PaddleOCR object...")
        ocr = PaddleOCR(
            use_doc_orientation_classify=False,  # Disable for better performance
            use_doc_unwarping=False,
            use_textline_orientation=True,  # Enable text line orientation
            lang='en'
        )
        print("="*60)
        print("✅ PaddleOCR object created!")
        print("="*60)
        logger.info("PaddleOCR object created!")
        logger.info("✅ PaddleOCR initialized successfully!")
        
    except ImportError as error:
        logger.error(f"PaddleOCR import failed: {error}")
        logger.error("Please install PaddleOCR: pip install paddleocr")
        raise HTTPException(status_code=500, detail="PaddleOCR not available")
    except Exception as error:
        logger.error(f"PaddleOCR initialization failed: {error}")
        logger.error("This might be due to missing models or insufficient memory")
        raise HTTPException(status_code=500, detail="OCR service initialization failed")
    
    print("="*60)
    print("✅ PaddleOCR COMPLETE - Moving to YOLO...")
    print("="*60)
    logger.info("="*60)
    logger.info("✅ PaddleOCR COMPLETE - Moving to YOLO...")
    logger.info("="*60)
    
    # Initialize YOLOv11 (pre-trained model for object detection)
    print(f"YOLO_AVAILABLE check: {YOLO_AVAILABLE}")
    logger.info(f"YOLO_AVAILABLE check: {YOLO_AVAILABLE}")
    if YOLO_AVAILABLE:
        try:
            print("🎯 Starting YOLO initialization...")
            logger.info("🎯 Initializing YOLOv11 pre-trained model...")
            
            # Check if custom trained model exists
            # Priority order: custom_ocr_model3 (latest) > custom_ocr_model2 > custom_ocr_model > models/yolo11_custom.pt
            custom_model_paths = [
                os.path.join(base_dir, 'yolo_training', 'custom_ocr_model3', 'weights', 'best.pt'),
                os.path.join(base_dir, 'yolo_training', 'custom_ocr_model2', 'weights', 'best.pt'),
                os.path.join(base_dir, 'yolo_training', 'custom_ocr_model', 'weights', 'best.pt'),
                os.path.join(base_dir, 'models', 'yolo11_custom.pt')
            ]
            
            custom_model_path = None
            for path in custom_model_paths:
                if os.path.exists(path):
                    custom_model_path = path
                    break
            
            if custom_model_path:
                print(f"📦 Found custom model: {custom_model_path}")
                yolo_model = YOLO(custom_model_path)
                model_name = f"{os.path.basename(custom_model_path)} (CUSTOM TRAINED MODEL)"
            else:
                # Use generic pre-trained model (won't detect text well!)
                print("⚠️  No custom model found, using generic yolo11n.pt")
                print("⚠️  Pre-trained YOLO doesn't know technical drawings!")
                print("💡 Collect training data and run train_yolo_custom.py")
                yolo_model = YOLO('yolo11n.pt')
                model_name = "yolo11n.pt (generic - not trained for technical drawings)"
            
            print(f"✅ YOLO model loaded: {model_name}")
            logger.info(f"✅ YOLOv11 initialized successfully!")
            logger.info(f"   Model: {model_name}")
            logger.info(f"   yolo_model type: {type(yolo_model)}")
        except Exception as e:
            print(f"❌ YOLO FAILED: {e}")
            logger.warning(f"⚠️ YOLOv11 initialization failed: {e}")
            logger.warning(f"   Exception type: {type(e)}")
            logger.warning(f"   Exception details: {str(e)}")
            logger.info("YOLOv11 detection will not be available")
            yolo_model = None
    else:
        print("❌ YOLO_AVAILABLE is False!")
        logger.warning("⚠️ YOLOv11 not available (ultralytics not installed)")
        yolo_model = None
    
    print("="*60)
    print(f"Final check: yolo_model = {yolo_model}")
    print("="*60)
    
    yield
    
    # Shutdown
    logger.info("Shutting down OCR service...")
    
    # Clean up global resources
    if ocr:
        del ocr
        ocr = None
    
    if yolo_model:
        del yolo_model
        yolo_model = None
    
    # Shutdown thread executor
    executor.shutdown(wait=True)
    
    # Stop background cleanup thread
    if 'cleanup_thread' in globals() and cleanup_thread.is_alive():
        logger.info("Stopping background cleanup thread...")
        cleanup_thread.join(timeout=5)
    
    # Force garbage collection
    gc.collect()
    
    logger.info("OCR service shutdown complete")

# Initialize FastAPI app with lifespan handler
app = FastAPI(
    title="SPaCial AI OCR Service",
    description="OCR service for dimension detection using PaddleOCR",
    version="1.0.0",
    lifespan=lifespan
)

# Add CORS middleware with proper security
ALLOWED_ORIGINS = [
    "http://localhost:3000",
    "http://localhost:5173", 
    "http://127.0.0.1:3000",
    "http://127.0.0.1:5173",
    "https://cooldan-spacial-server-api.hf.space",
    "https://*.onrender.com",
]

# Allow all origins only in development
if os.getenv('ENVIRONMENT') == 'development':
    ALLOWED_ORIGINS = ["*"]

app.add_middleware(
    CORSMiddleware,
    allow_origins=ALLOWED_ORIGINS,
    allow_credentials=True,
    allow_methods=["GET", "POST", "PUT", "DELETE"],
    allow_headers=["*"],

)

# Add trusted host middleware for security
app.add_middleware(
    TrustedHostMiddleware, 
    allowed_hosts=["localhost", "127.0.0.1", "*.hf.space", "*.onrender.com"]
)

# Rate limiting will be added after function definition


# File validation constants
MAX_FILE_SIZE = 50 * 1024 * 1024  # 50MB
MAX_IMAGE_DIMENSIONS = 4096  # Max width/height to prevent memory issues
ALLOWED_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp', '.pdf'}
ALLOWED_MIME_TYPES = {
    'image/jpeg', 'image/jpg', 'image/png', 'image/bmp', 
    'image/tiff', 'image/webp', 'application/pdf'
}

# Security: Generate secure temp file names
def create_secure_temp_file(suffix='.jpg'):
    """Create a secure temporary file with random name"""
    import secrets
    import string
    
    # Generate random filename using secrets.choice (correct method)
    random_name = ''.join(secrets.choice(string.ascii_letters + string.digits) for _ in range(16))
    temp_path = os.path.join(temp_dir, f"secure_{random_name}_{int(time.time())}{suffix}")
    
    # Ensure directory exists
    os.makedirs(os.path.dirname(temp_path), exist_ok=True)
    
    return temp_path

def validate_uploaded_file(file: UploadFile) -> bool:
    """Validate uploaded file for security and size"""
    if not file:
        logger.warning("❌ File validation failed: No file provided")
        return False
    
    # Log file details for debugging
    logger.info(f"🔍 File validation: filename='{file.filename}', content_type='{file.content_type}'")
    
    # Check file size (only if size is available)
    if hasattr(file, 'size') and file.size:
        logger.info(f"📏 File size: {file.size} bytes")
        if file.size > MAX_FILE_SIZE:
            logger.warning(f"❌ File too large: {file.size} bytes > {MAX_FILE_SIZE} bytes")
            return False
    
    # Check file extension (more lenient)
    if file.filename:
        # Sanitize filename to prevent path traversal
        safe_filename = os.path.basename(file.filename)
        if safe_filename != file.filename:
            logger.warning(f"❌ Unsafe filename detected: '{file.filename}' -> '{safe_filename}'")
            return False
            
        file_ext = os.path.splitext(safe_filename.lower())[1]
        logger.info(f"📁 File extension: '{file_ext}'")
        if file_ext not in ALLOWED_EXTENSIONS:
            logger.warning(f"❌ Invalid file extension: '{file_ext}' not in {ALLOWED_EXTENSIONS}")
            return False
    else:
        logger.warning("❌ No filename provided")
        return False
    
    # Check MIME type (more lenient - allow if not specified)
    if file.content_type:
        logger.info(f"🎭 MIME type: '{file.content_type}'")
        if file.content_type not in ALLOWED_MIME_TYPES:
            logger.warning(f"❌ Invalid MIME type: '{file.content_type}' not in {ALLOWED_MIME_TYPES}")
            return False
    else:
        logger.info("ℹ️ No MIME type specified, allowing based on extension")
    
    logger.info("✅ File validation passed")
    return True

def convert_pdf_to_image(pdf_path: str, page_number: int = 0, dpi: int = 300) -> str:
    """Convert PDF page to image for OCR processing"""
    try:
        import fitz  # PyMuPDF
        
        # Open PDF
        pdf_document = fitz.open(pdf_path)
        
        # Check if page exists
        if page_number >= len(pdf_document):
            page_number = 0
        
        # Get page
        page = pdf_document[page_number]
        
        # Convert to image with high DPI for better OCR
        mat = fitz.Matrix(dpi / 72, dpi / 72)  # 72 is default DPI
        pix = page.get_pixmap(matrix=mat)
        
        # Save as temporary image
        temp_image_path = create_secure_temp_file('.png')
        pix.save(temp_image_path)
        
        pdf_document.close()
        
        logger.info(f"✅ Converted PDF page {page_number} to image: {temp_image_path}")
        return temp_image_path
        
    except ImportError:
        logger.error("❌ PyMuPDF (fitz) not installed. Install with: pip install PyMuPDF")
        raise HTTPException(status_code=503, detail="PDF processing not available. PyMuPDF not installed.")
    except Exception as e:
        logger.error(f"❌ Error converting PDF to image: {e}")
        raise HTTPException(status_code=500, detail=f"Failed to convert PDF: {str(e)}")

def validate_image_dimensions(image_path: str) -> bool:
    """Validate image dimensions to prevent memory issues"""
    try:
        import cv2
        img = cv2.imread(image_path)
        if img is None:
            return False
            
        height, width = img.shape[:2]
        logger.info(f"📐 Image dimensions: {width}x{height}")
        
        if width > MAX_IMAGE_DIMENSIONS or height > MAX_IMAGE_DIMENSIONS:
            logger.warning(f"❌ Image too large: {width}x{height} > {MAX_IMAGE_DIMENSIONS}x{MAX_IMAGE_DIMENSIONS}")
            return False
            
        return True
    except Exception as e:
        logger.error(f"Error validating image dimensions: {e}")
        return False

# Rate limiting
RATE_LIMIT_REQUESTS = 1000  # requests per minute (increased for training data manager)
RATE_LIMIT_WINDOW = 60  # seconds

# Store request counts per IP
request_counts = defaultdict(list)

def rate_limit_middleware(request, call_next):
    """Simple rate limiting middleware"""
    client_ip = request.client.host
    current_time = time.time()
    
    # Clean old requests
    request_counts[client_ip] = [
        req_time for req_time in request_counts[client_ip] 
        if current_time - req_time < RATE_LIMIT_WINDOW
    ]
    
    # Check rate limit
    if len(request_counts[client_ip]) >= RATE_LIMIT_REQUESTS:
        raise HTTPException(status_code=429, detail="Rate limit exceeded")
    
    # Add current request
    request_counts[client_ip].append(current_time)
    
    return call_next(request)

# Add rate limiting middleware
app.middleware("http")(rate_limit_middleware)

def is_dimension_text(text):
    """Check if text looks like a dimension"""
    # Look for numbers, possibly with units or decimal points
    if re.search(r'\d+\.?\d*\s*(mm|cm|m|in|inch)?', text.strip(), re.IGNORECASE):
        return True
    # Look for pure numbers
    if re.match(r'^\d+\.?\d*$', text.strip()):
        return True
    return False

def parse_tolerance(text):
    """Parse tolerance information from text with improved patterns including thread tolerances"""
    
    if not text:
        return None
    
    # Keep original text - DON'T do aggressive replacements!
    clean_text = text.strip()
    
    # Pattern 0: ISO 2768 General Tolerances (check FIRST!)
    iso_2768_patterns = [
        r'General tolerance DIN 150 2768.*?medium',  # DIN 150 2768 medium
        r'General tolerance DIN 150 2768.*?fine',    # DIN 150 2768 fine  
        r'General tolerance DIN 150 2768.*?coarse',  # DIN 150 2768 coarse
        r'ISO 2768.*?medium',                        # ISO 2768 medium
        r'ISO 2768.*?fine',                          # ISO 2768 fine
        r'ISO 2768.*?coarse',                        # ISO 2768 coarse
    ]
    
    for pattern in iso_2768_patterns:
        iso_match = re.search(pattern, clean_text, re.IGNORECASE)
        if iso_match:
            # Extract tolerance class
            if 'medium' in clean_text.lower():
                tolerance_class = 'medium'
            elif 'fine' in clean_text.lower():
                tolerance_class = 'fine'
            elif 'coarse' in clean_text.lower():
                tolerance_class = 'coarse'
            else:
                tolerance_class = 'medium'  # Default
            
            print(f"✓ ISO 2768 General Tolerance: {text} -> {tolerance_class}")
            
            return {
                "value": None,  # General tolerance doesn't have a specific value
                "tolerance_class": tolerance_class,
                "tolerance_type": "iso_2768_general",
                "standard": "ISO 2768-1",
                "is_diameter": False,
                "original_text": text
            }
    
    # Normalize ± symbol for easier parsing and fix common OCR errors
    clean_text = clean_text.replace('±', '+-')
    # Fix common OCR errors: ± is sometimes read as = or :
    clean_text = re.sub(r'(\d+\.?\d*)=(\d+\.?\d*)$', r'\1+-\2', clean_text)
    clean_text = re.sub(r'(\d+\.?\d*):(\d+\.?\d*)$', r'\1+-\2', clean_text)
    
    # Only log meaningful tolerance parsing results
    
    # Pattern 0: Thread tolerances (M30X2-6G, 6G, 8H, etc.) - check FIRST!
    # Handle OCR errors: 'g' might be read as '9', 'G' as '6', etc.
    thread_patterns = [
        r'M(\d+\.?\d*)[Xx×]?(\d+\.?\d*)?[-]?([A-HG]+\d*)',  # Standard thread
        r'M(\d+\.?\d*)[Xx×]?(\d+\.?\d*)?[-]?(\d+[A-HG]*)',  # OCR error: G->6, g->9
        r'M(\d+\.?\d*)[Xx×]?(\d+\.?\d*)?[-]?([A-H]\d*)',    # Without G
    ]
    
    for i, pattern in enumerate(thread_patterns):
        thread_tolerance_match = re.search(pattern, clean_text, re.IGNORECASE)
        if thread_tolerance_match:
            size = float(thread_tolerance_match.group(1))
            pitch = thread_tolerance_match.group(2)
            tolerance_class_raw = thread_tolerance_match.group(3)
            
            # Fix common OCR errors
            tolerance_class = tolerance_class_raw.upper()
            if '69' in tolerance_class:
                tolerance_class = tolerance_class.replace('69', '6G')  # 69 -> 6G
            elif '6G' in tolerance_class:
                tolerance_class = tolerance_class.replace('6G', '6G')  # Already correct
            elif tolerance_class.isdigit() and len(tolerance_class) == 2:
                # Convert pure numbers like "69" to "6G"
                if tolerance_class.startswith('6'):
                    tolerance_class = '6G'
                elif tolerance_class.startswith('8'):
                    tolerance_class = '8H'
            
            print(f"✓ Thread tolerance: {text} -> M{size}X{pitch}-{tolerance_class}")
            
            return {
                "value": size,
                "thread_pitch": float(pitch) if pitch else None,
                "tolerance_class": tolerance_class,
                "tolerance_type": "thread",
                "is_diameter": False,
                "original_text": text
            }
    
    # Pattern 1: ± tolerance with various symbols (e.g., "28±0.03", "104±0.04", "Ø48±0.03", "2±0.05")
    # Handle both ± and +- variations
    plus_minus_patterns = [
        r'(\d+\.?\d*)\+\-(\d+\.?\d*)',          # Normalized ± symbol (no spaces)
        r'(\d+\.?\d*)\s*\+\-\s*(\d+\.?\d*)',    # +- variation with spaces
        r'(\d+\.?\d*)\s*\+\s*(\d+\.?\d*)\s*$',  # Single + at end
        r'(\d+\.?\d*)\s*\+\s*(\d+\.?\d*)',      # Any + followed by number
    ]
    
    for pattern in plus_minus_patterns:
        plus_minus_match = re.search(pattern, clean_text, re.IGNORECASE)
        if plus_minus_match:
            value = float(plus_minus_match.group(1))
            tolerance = float(plus_minus_match.group(2))
            is_diameter = bool(re.search(r'[ØDIA]', clean_text, re.IGNORECASE))
            print(f"✓ ± Tolerance: {text} -> {value}±{tolerance}")
            return {
                "value": value,
                "tolerance_plus": tolerance,
                "tolerance_minus": tolerance,
                "tolerance_type": "+/-",
                "is_diameter": is_diameter,
                "original_text": text
            }
    
    # Pattern 2: +tolerance/-tolerance (e.g., "25.5+0.1/-0.05", "Ø48+0.1/-0.05", "25.5 +0.1 / -0.05")
    asymmetric_patterns = [
        r'(?:Ø|DIA|DIAMETER)?\s*(\d+\.?\d*)\s*\+\s*(\d+\.?\d*)\s*/\s*-\s*(\d+\.?\d*)',  # Standard +/-
        r'(?:Ø|DIA|DIAMETER)?\s*(\d+\.?\d*)\s*\+\s*(\d+\.?\d*)\s*/\s*(\d+\.?\d*)',     # +/ (without -)
        r'(?:Ø|DIA|DIAMETER)?\s*(\d+\.?\d*)\s*\+\s*(\d+\.?\d*)\s*-\s*(\d+\.?\d*)',    # +- (no slash)
    ]
    
    for pattern in asymmetric_patterns:
        asymmetric_match = re.search(pattern, clean_text, re.IGNORECASE)
        if asymmetric_match:
            value = float(asymmetric_match.group(1))
            plus_tolerance = float(asymmetric_match.group(2))
            minus_tolerance = float(asymmetric_match.group(3))
            is_diameter = bool(re.search(r'[ØDIA]', clean_text, re.IGNORECASE))
            return {
                "value": value,
                "tolerance_plus": plus_tolerance,
                "tolerance_minus": minus_tolerance,
                "tolerance_type": "+/-",
                "is_diameter": is_diameter,
                "original_text": text
            }
    
    # Pattern 3: Value with dual negative tolerances (e.g., "Ø18⁻⁰·¹⁵₋₀·₂₂")
    dual_negative_patterns = [
        r'(?:Ø|DIA|DIAMETER)?\s*(\d+\.?\d*)\s*[⁻\-]\s*(\d+\.?\d*)\s*[₋\-]\s*(\d+\.?\d*)',  # Flexible format with any minus
        r'(?:Ø|DIA|DIAMETER)?\s*(\d+\.?\d*)\s*-\s*(\d+\.?\d*)\s*-\s*(\d+\.?\d*)',  # Standard format
    ]
    
    # Pattern 3.5: Special case for Ø48⁻⁰·⁰³ (zero upper tolerance, negative lower tolerance)
    zero_upper_patterns = [
        r'(?:Ø|DIA|DIAMETER)?\s*(\d+\.?\d*)\s*⁻⁰·(\d+\.?\d*)',  # Ø48⁻⁰·⁰³ format
        r'(?:Ø|DIA|DIAMETER)?\s*(\d+\.?\d*)\s*⁻0\.(\d+\.?\d*)',  # Ø48⁻0.03 format
    ]
    
    # Check zero upper tolerance patterns first
    for pattern in zero_upper_patterns:
        zero_upper_match = re.search(pattern, clean_text, re.IGNORECASE)
        if zero_upper_match:
            value = float(zero_upper_match.group(1))
            
            try:
                # Handle superscript format
                def parse_number(text):
                    superscript_map = {'⁰': '0', '¹': '1', '²': '2', '³': '3', '⁴': '4', 
                                     '⁵': '5', '⁶': '6', '⁷': '7', '⁸': '8', '⁹': '9', '·': '.'}
                    result = ''
                    for char in text:
                        result += superscript_map.get(char, char)
                    return float(result)
                
                lower_tol = parse_number(zero_upper_match.group(2))
                
                is_diameter = bool(re.search(r'[ØDIA]', clean_text, re.IGNORECASE))
                print(f"✓ Zero upper tolerance: {text} -> {value} (upper: 0, lower: -{lower_tol})")
                
                return {
                    "value": value,
                    "tolerance_plus": 0.0,      # Zero upper tolerance
                    "tolerance_minus": lower_tol, # Negative lower tolerance
                    "tolerance_type": "zero-upper",
                    "is_diameter": is_diameter,
                    "original_text": text
                }
            except ValueError:
                continue  # Try next pattern
    
    for pattern in dual_negative_patterns:
        dual_negative_match = re.search(pattern, clean_text, re.IGNORECASE)
        if dual_negative_match:
            value = float(dual_negative_match.group(1))
            
            try:
                # Handle both superscript and standard formats
                def parse_number(text):
                    # Convert superscript to normal numbers if needed
                    superscript_map = {'⁰': '0', '¹': '1', '²': '2', '³': '3', '⁴': '4', 
                                     '⁵': '5', '⁶': '6', '⁷': '7', '⁸': '8', '⁹': '9', '·': '.'}
                    result = ''
                    for char in text:
                        result += superscript_map.get(char, char)
                    return float(result)
                
                upper_tol = parse_number(dual_negative_match.group(2))
                lower_tol = parse_number(dual_negative_match.group(3))
                
                is_diameter = bool(re.search(r'[ØDIA]', clean_text, re.IGNORECASE))
                print(f"✓ Dual negative tolerance: {text} -> {value} (upper: {upper_tol}, lower: {lower_tol})")
                
                return {
                    "value": value,
                    "tolerance_plus": upper_tol,  # Upper tolerance (less negative)
                    "tolerance_minus": lower_tol, # Lower tolerance (more negative)
                    "tolerance_type": "dual-negative",
                    "is_diameter": is_diameter,
                    "original_text": text
                }
            except ValueError:
                continue  # Try next pattern
    
    # Pattern 4: Value with negative tolerance only (e.g., "Ø48-0.03", "48 -0.03")
    negative_patterns = [
        r'(?:Ø|DIA|DIAMETER)?\s*(\d+\.?\d*)\s*-\s*(\d+\.?\d*)',  # With space
        r'(?:Ø|DIA|DIAMETER)?\s*(\d+\.?\d*)\s*⁻\s*(\d+\.?\d*)',  # Superscript minus
        r'(?:Ø|DIA|DIAMETER)?\s*(\d+\.?\d*)\s*⁻⁰\s*(\d+\.?\d*)', # With superscript 0
    ]
    
    for pattern in negative_patterns:
        negative_only_match = re.search(pattern, clean_text, re.IGNORECASE)
        if negative_only_match:
            value = float(negative_only_match.group(1))
            minus_tolerance = float(negative_only_match.group(2))
            is_diameter = bool(re.search(r'[ØDIA]', clean_text, re.IGNORECASE))
            return {
                "value": value,
                "tolerance_plus": 0.0,
                "tolerance_minus": minus_tolerance,
                "tolerance_type": "-only",
                "is_diameter": is_diameter,
                "original_text": text
            }
    
    # Pattern 4: Value with positive tolerance only (e.g., "Ø48+0.03", "48 +0.03")
    positive_only_match = re.search(r'(?:Ø|DIA|DIAMETER)?\s*(\d+\.?\d*)\s*\+\s*(\d+\.?\d*)', clean_text, re.IGNORECASE)
    if positive_only_match:
        value = float(positive_only_match.group(1))
        plus_tolerance = float(positive_only_match.group(2))
        is_diameter = bool(re.search(r'[ØDIA]', clean_text, re.IGNORECASE))
        return {
            "value": value,
            "tolerance_plus": plus_tolerance,
            "tolerance_minus": 0.0,
            "tolerance_type": "+only",
            "is_diameter": is_diameter,
            "original_text": text
        }
    
    # Pattern 5: ISO tolerance classes (e.g., "48H7", "25.5f6", "Ø48H7")
    iso_tolerance_match = re.search(r'(?:Ø|DIA|DIAMETER)?\s*(\d+\.?\d*)\s*([A-Za-z]\d+)', clean_text, re.IGNORECASE)
    if iso_tolerance_match:
        value = float(iso_tolerance_match.group(1))
        tolerance_class = iso_tolerance_match.group(2).upper()
        is_diameter = bool(re.search(r'[ØDIA]', clean_text, re.IGNORECASE))
        
        # Comprehensive ISO tolerance lookup (simplified but more complete)
        iso_tolerances = {
            # Hole basis (H series)
            'H6': {'plus': 0.016, 'minus': 0.0},
            'H7': {'plus': 0.025, 'minus': 0.0},
            'H8': {'plus': 0.039, 'minus': 0.0},
            'H9': {'plus': 0.062, 'minus': 0.0},
            'H10': {'plus': 0.100, 'minus': 0.0},
            'H11': {'plus': 0.160, 'minus': 0.0},
            
            # Shaft basis (f, g series)
            'F6': {'plus': 0.0, 'minus': 0.013},
            'F7': {'plus': 0.0, 'minus': 0.025},
            'F8': {'plus': 0.0, 'minus': 0.039},
            'G6': {'plus': 0.0, 'minus': 0.009},
            'G7': {'plus': 0.0, 'minus': 0.020},
            'G8': {'plus': 0.0, 'minus': 0.032},
            
            # Common fits
            'E7': {'plus': 0.0, 'minus': 0.040},
            'E8': {'plus': 0.0, 'minus': 0.059},
            'E9': {'plus': 0.0, 'minus': 0.087},
            'D9': {'plus': 0.0, 'minus': 0.087},
            'C11': {'plus': 0.0, 'minus': 0.160}
        }
        
        tolerance_info = iso_tolerances.get(tolerance_class, {'plus': 0.0, 'minus': 0.0})
        
        if tolerance_info['plus'] > 0 or tolerance_info['minus'] > 0:
            print(f"✓ ISO tolerance: {text} -> {value}{tolerance_class}")
        
        return {
            "value": value,
            "tolerance_plus": tolerance_info['plus'],
            "tolerance_minus": tolerance_info['minus'],
            "tolerance_type": f"ISO {tolerance_class}",
            "is_diameter": is_diameter,
            "original_text": text
        }
    
    # Pattern 6: Special diameter symbols and edge cases
    # Handle cases like "Ø48⁰-0.03" (with superscript 0)
    diameter_superscript_match = re.search(r'(?:Ø|DIA|DIAMETER)?\s*(\d+\.?\d*)\s*⁰\s*[-]\s*(\d+\.?\d*)', clean_text, re.IGNORECASE)
    if diameter_superscript_match:
        value = float(diameter_superscript_match.group(1))
        minus_tolerance = float(diameter_superscript_match.group(2))
        is_diameter = True  # Always diameter when Ø is present
        return {
            "value": value,
            "tolerance_plus": 0.0,
            "tolerance_minus": minus_tolerance,
            "tolerance_type": "diameter-superscript",
            "is_diameter": is_diameter,
            "original_text": text
        }
    
    # Pattern 7: Single value with no tolerance (e.g., "48", "Ø48", "DIA 48")
    single_value_match = re.search(r'(?:Ø|DIA|DIAMETER)?\s*(\d+\.?\d*)', clean_text, re.IGNORECASE)
    if single_value_match:
        value = float(single_value_match.group(1))
        is_diameter = bool(re.search(r'[ØDIA]', clean_text, re.IGNORECASE))
        return {
            "value": value,
            "tolerance_plus": 0.0,
            "tolerance_minus": 0.0,
            "tolerance_type": "none",
            "is_diameter": is_diameter,
            "original_text": text
        }
    
    # No pattern matched - only log for complex text to avoid spam
    if len(clean_text) > 3 or re.search(r'[A-Za-z±\+\-]', clean_text):
        print(f"✗ No tolerance pattern: '{text}'")
    return None

def preprocess_image_for_ocr(image_path):
    """Preprocess image for better OCR results in hardcore mode"""
    try:
        img = cv2.imread(str(image_path))
        if img is None:
            return image_path
        
        # Convert to grayscale
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        
        # Apply denoising
        denoised = cv2.fastNlMeansDenoising(gray)
        
        # Apply adaptive thresholding for better text contrast
        adaptive_thresh = cv2.adaptiveThreshold(
            denoised, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2
        )
        
        # Apply morphological operations to clean up
        kernel = np.ones((2,2), np.uint8)
        cleaned = cv2.morphologyEx(adaptive_thresh, cv2.MORPH_CLOSE, kernel)
        
        # Save preprocessed image
        preprocessed_path = str(image_path).replace('.jpg', '_preprocessed.jpg')
        cv2.imwrite(preprocessed_path, cleaned)
        
        logger.info(f"Image preprocessed: {preprocessed_path}")
        return preprocessed_path
        
    except Exception as e:
        logger.warning(f"Preprocessing failed: {e}")
        return image_path

def detect_text_orientation_advanced(polygon, text):
    """Improved text orientation detection with better vertical text handling"""
    try:
        if not polygon or len(polygon) < 4:
            return 0
        
        # Calculate bounding box dimensions
        x_coords = [point[0] for point in polygon]
        y_coords = [point[1] for point in polygon]
        bbox_width = max(x_coords) - min(x_coords)
        bbox_height = max(y_coords) - min(y_coords)
        aspect_ratio = bbox_height / bbox_width if bbox_width > 0 else 1
        
        # Calculate the main orientation based on the longest edge
        edge1 = [polygon[1][0] - polygon[0][0], polygon[1][1] - polygon[0][1]]
        edge2 = [polygon[2][0] - polygon[1][0], polygon[2][1] - polygon[1][1]]
        
        edge1_length = math.sqrt(edge1[0]**2 + edge1[1]**2)
        edge2_length = math.sqrt(edge2[0]**2 + edge2[1]**2)
        
        # Use the longer edge for orientation calculation
        if edge1_length > edge2_length:
            main_edge = edge1
        else:
            main_edge = edge2
            
        # Calculate angle of the main edge
        angle = math.degrees(math.atan2(main_edge[1], main_edge[0]))
        angle = (angle + 360) % 360  # Normalize to 0-360
        
        logger.info(f"🔍 Orientation: bbox={bbox_width:.1f}x{bbox_height:.1f}, aspect={aspect_ratio:.2f}, angle={angle:.1f}°")
        
        # Simplified orientation detection
        if aspect_ratio > 1.3:  # Clearly vertical (height > width * 1.3)
            # For vertical text, determine if it's 90° or 270°
            if 45 <= angle <= 135:  # Text reads from bottom to top
                return 90
            elif 225 <= angle <= 315:  # Text reads from top to bottom
                return 270
            else:
                return 90  # Default for vertical
        elif aspect_ratio < 0.7:  # Clearly horizontal (width > height * 1.4)
            # For horizontal text, determine if it's 0° or 180°
            if 315 <= angle or angle <= 45:  # Text reads left to right
                return 0
            elif 135 <= angle <= 225:  # Text reads right to left
                return 180
            else:
                return 0  # Default for horizontal
        else:
            # Ambiguous case - use angle-based detection
            if 315 <= angle or angle <= 45:
                return 0
            elif 45 < angle <= 135:
                return 90
            elif 135 < angle <= 225:
                return 180
            else:  # 225 < angle < 315
                return 270
        
    except Exception as e:
        logger.warning(f"Orientation detection failed: {e}")
        return 0

def is_dimension_text_advanced(text):
    """Advanced dimension text detection"""
    if not text:
        return False
    
    # Enhanced patterns for dimension detection
    patterns = [
        r'\d+\.?\d*\s*±\s*\d+\.?\d*',  # ± tolerance
        r'\d+\.?\d*\s*\+\s*\d+\.?\d*\s*/\s*-\s*\d+\.?\d*',  # +/- tolerance
        r'\d+\.?\d*\s*-\s*\d+\.?\d*',  # negative tolerance
        r'\d+\.?\d*\s*\+\s*\d+\.?\d*',  # positive tolerance
        r'\d+\.?\d*\s*[A-Za-z]\d+',  # ISO tolerance
        r'M?\d+\.?\d*[Xx×]?\d+\.?\d*?[-]?[A-HG]+\d*',  # Thread tolerances
        r'\d+\.?\d*\s*(mm|cm|m|in|inch)',  # With units
        r'^\d+\.?\d*$'  # Pure numbers
    ]
    
    for pattern in patterns:
        if re.search(pattern, text.strip(), re.IGNORECASE):
            return True
    
    return False

def detect_zone_category(text):
    """Detect zone category based on text content"""
    if not text:
        return 'measure'
    
    text_lower = text.lower().strip()
    
    # General tolerance keywords (check FIRST!)
    general_tolerance_keywords = [
        'general tolerance', 'iso 2768', 'din 150 2768', 'general tol',
        'gen tolerance', 'gen tol', 'iso2768', 'din2768'
    ]
    
    # Material keywords
    material_keywords = [
        'matiere', 'material', 'acier', 'steel', 'aluminium', 'aluminum',
        'inox', 'stainless', 'bronze', 'laiton', 'brass', 'cuivre', 'copper',
        'plastique', 'plastic', 'caoutchouc', 'rubber'
    ]
    
    # Radius keywords
    radius_keywords = ['r', 'radius', 'rayon']
    
    # Diameter keywords
    diameter_keywords = ['ø', 'diameter', 'diametre', 'dia', 'd']
    
    # Thread keywords
    thread_keywords = ['m', 'thread', 'filetage', 'pas']
    
    # Check for general tolerance FIRST
    for keyword in general_tolerance_keywords:
        if keyword in text_lower:
            return 'gdt'  # Geometric Dimensioning & Tolerancing
    
    # Check for material
    for keyword in material_keywords:
        if keyword in text_lower:
            return 'material'
    
    # Check for radius (R followed by number, or √ symbol with number)
    if (re.search(r'^r\d+', text_lower) or 'radius' in text_lower or 'rayon' in text_lower or 
        re.search(r'√\d+', text) or re.search(r'\d+√', text)):
        return 'radius'
    
    # Check for diameter (Ø followed by number or diameter keywords, or numbers that should be diameter)
    if (re.search(r'[øØ]\d+', text) or any(keyword in text_lower for keyword in diameter_keywords) or
        re.search(r'^\d+\.\d{3}$', text) or re.search(r'^\d{2,3}$', text) or  # Common diameter patterns
        re.search(r'^\d+[,.]\d+$', text)):  # Decimal numbers like 0,8 or 0.8 (likely diameters)
        return 'diameter'
    
    # Check for thread (M followed by number)
    if re.search(r'^m\d+', text_lower) or 'thread' in text_lower or 'filetage' in text_lower:
        return 'thread'
    
    # Check for tolerance patterns
    if re.search(r'[±\+\-]\d+', text) or re.search(r'[A-Z]\d+', text):
        return 'tolerance'
    
    # Default to measure for dimensions
    return 'measure'

def clean_ocr_text_advanced(text):
    """Advanced OCR text cleaning with better dimension handling"""
    if not text:
        return ""
    
    # Remove common OCR artifacts
    cleaned = text.strip()
    
    # Remove trailing periods that aren't part of decimal numbers
    if cleaned.endswith('.') and not re.search(r'\d+\.\d*$', cleaned):
        cleaned = cleaned[:-1]
        logger.info(f"🧹 Removed trailing period: '{text}' -> '{cleaned}'")
    
    # Fix common OCR mistakes in dimension text
    replacements = {
        'O': '0',  # Letter O to number 0 in numeric contexts
        'o': '0',  # Lowercase o to number 0
        'l': '1',  # Lowercase l to 1 in numeric contexts
        'I': '1',  # Letter I to number 1
        'S': '5',  # S to 5 in numeric contexts
        'B': '8',  # B to 8 in numeric contexts
        'G': '6',  # G to 6 in numeric contexts
        'q': '9',  # Lowercase q to number 9
        'g': '9',  # Lowercase g to number 9
        'C': '6',  # Letter C to number 6
        'D': '0',  # Letter D to number 0
        'T': '7',  # Letter T to number 7
        'J': '1',  # Letter J to number 1
        'P': '9',  # Letter P to number 9
        'R': '6',  # Letter R to number 6
        'F': '7',  # Letter F to number 7
        'E': '8',  # Letter E to number 8
    }
    
    # Apply replacements only in numeric contexts
    for old, new in replacements.items():
        # Only replace if surrounded by digits or at start/end of number
        pattern = f'(?<=\\d){re.escape(old)}(?=\\d)|(?<=\\d){re.escape(old)}$|^{re.escape(old)}(?=\\d)'
        cleaned = re.sub(pattern, new, cleaned)
    
    # Special handling for tolerance values and common OCR errors
    tolerance_fixes = {
        r'±0\.0([2-9])': r'±0.0\1',  # Ensure proper decimal format
        r'±([0-9])\.([0-9][0-9])': r'±\1.\2',  # Fix decimal tolerance
        r'([0-9]+)±([0-9])\.([0-9][0-9])': r'\1±\2.\3',  # Fix tolerance after number
        r'^: 4$': r'11.5',  # Fix specific OCR error ": 4" -> "11.5"
        r'^6$': r'5',  # Fix specific OCR error "6" -> "5"
        r'^\.\,$': r'',  # Remove meaningless OCR artifacts
        r'^\.\,,\s*$': r'',  # Remove meaningless OCR artifacts
        r'^15$': r'9.5',  # Fix common OCR error "15" -> "9.5" for vertical text
    }
    
    for pattern, replacement in tolerance_fixes.items():
        if re.match(pattern, cleaned):
            cleaned = re.sub(pattern, replacement, cleaned)
            logger.info(f"🧹 Applied tolerance fix: '{text}' -> '{cleaned}'")
    
    # Remove extra spaces and normalize
    cleaned = re.sub(r'\s+', ' ', cleaned).strip()
    
    return cleaned

def smart_zone_merger(zones, horizontal_gap=80, vertical_gap=50):
    """Smart merging of overlapping or nearby zones"""
    if len(zones) < 2:
        return zones
    
    merged_zones = []
    used_indices = set()
    
    for i, zone1 in enumerate(zones):
        if i in used_indices:
            continue
            
        bbox1 = zone1.get('bbox', {})
        if not bbox1:
            merged_zones.append(zone1)
            continue
            
        x1_1, y1_1, x2_1, y2_1 = bbox1.get('x1', 0), bbox1.get('y1', 0), bbox1.get('x2', 0), bbox1.get('y2', 0)
        text1 = zone1.get('text', '')
        
        # Look for nearby zones to merge
        merged_text = text1
        merged_bbox = {'x1': x1_1, 'y1': y1_1, 'x2': x2_1, 'y2': y2_1}
        
        for j, zone2 in enumerate(zones[i+1:], i+1):
            if j in used_indices:
                continue
                
            bbox2 = zone2.get('bbox', {})
            if not bbox2:
                continue
                
            x1_2, y1_2, x2_2, y2_2 = bbox2.get('x1', 0), bbox2.get('y1', 0), bbox2.get('x2', 0), bbox2.get('y2', 0)
            text2 = zone2.get('text', '')
            
            # Check if zones are close enough to merge
            horizontal_distance = min(abs(x1_1 - x2_2), abs(x1_2 - x2_1))
            vertical_distance = min(abs(y1_1 - y2_2), abs(y1_2 - y2_1))
            
            # Don't merge if both zones contain dimension-like text (likely separate dimensions)
            text1_has_dim = bool(re.search(r'\d+\.?\d*\s*[±\+\-]?\s*\d*\.?\d*', text1))
            text2_has_dim = bool(re.search(r'\d+\.?\d*\s*[±\+\-]?\s*\d*\.?\d*', text2))
            
            # Don't merge if both are dimensions and they're not very close
            if text1_has_dim and text2_has_dim and (horizontal_distance > 15 or vertical_distance > 15):
                continue
                
            if horizontal_distance < horizontal_gap and vertical_distance < vertical_gap:
                # Merge zones
                merged_text = text1 + " " + text2
                merged_bbox = {
                    'x1': min(x1_1, x1_2),
                    'y1': min(y1_1, y1_2),
                    'x2': max(x2_1, x2_2),
                    'y2': max(y2_1, y2_2)
                }
                
                used_indices.add(j)
                logger.info(f"Merged zones: '{text1}' + '{text2}' = '{merged_text}'")
        
        # Create merged zone
        merged_zone = {
            **zone1,
            "text": merged_text,
            "bbox": {
                **merged_bbox,
                "width": merged_bbox['x2'] - merged_bbox['x1'],
                "height": merged_bbox['y2'] - merged_bbox['y1']
            }
        }
        
        merged_zones.append(merged_zone)
        used_indices.add(i)
    
    # Add any remaining zones that weren't merged
    for i, zone in enumerate(zones):
        if i not in used_indices:
            merged_zones.append(zone)
    
    logger.info(f"Smart merge: {len(zones)} -> {len(merged_zones)} zones")
    return merged_zones

def remove_duplicate_zones(zones, overlap_threshold=0.8):
    """Remove duplicate zones based on overlap"""
    if len(zones) < 2:
        return zones
    
    filtered_zones = []
    
    for i, zone1 in enumerate(zones):
        is_duplicate = False
        bbox1 = zone1.get('bbox', {})
        
        if not bbox1:
            filtered_zones.append(zone1)
            continue
        
        x1_1, y1_1, x2_1, y2_1 = bbox1.get('x1', 0), bbox1.get('y1', 0), bbox1.get('x2', 0), bbox1.get('y2', 0)
        area1 = (x2_1 - x1_1) * (y2_1 - y1_1)
        
        for j, zone2 in enumerate(filtered_zones):
            bbox2 = zone2.get('bbox', {})
            if not bbox2:
                continue
                
            x1_2, y1_2, x2_2, y2_2 = bbox2.get('x1', 0), bbox2.get('y1', 0), bbox2.get('x2', 0), bbox2.get('y2', 0)
            
            # Calculate overlap
            overlap_x1 = max(x1_1, x1_2)
            overlap_y1 = max(y1_1, y1_2)
            overlap_x2 = min(x2_1, x2_2)
            overlap_y2 = min(y2_1, y2_2)
            
            if overlap_x1 < overlap_x2 and overlap_y1 < overlap_y2:
                overlap_area = (overlap_x2 - overlap_x1) * (overlap_y2 - overlap_y1)
                overlap_ratio = overlap_area / area1
                
                if overlap_ratio > overlap_threshold:
                    # Keep the zone with higher confidence
                    if zone1.get('confidence', 0) > zone2.get('confidence', 0):
                        filtered_zones[j] = zone1
                    is_duplicate = True
                    break
        
        if not is_duplicate:
            filtered_zones.append(zone1)
    
    removed_count = len(zones) - len(filtered_zones)
    if removed_count > 0:
        logger.info(f"Removed {removed_count} duplicate zones")
    
    return filtered_zones

def detect_dimension_lines_enhanced(image_path):
    """Enhanced dimension line detection"""
    try:
        img = cv2.imread(image_path)
        if img is None:
            return []
        
        # Convert to grayscale
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        
        # Apply edge detection with multiple thresholds
        edges1 = cv2.Canny(gray, 50, 150, apertureSize=3)
        edges2 = cv2.Canny(gray, 30, 100, apertureSize=3)
        edges = cv2.bitwise_or(edges1, edges2)
        
        # Detect lines using HoughLinesP with enhanced parameters
        lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=30, 
                               minLineLength=20, maxLineGap=8)
        
        detected_lines = []
        if lines is not None:
            for line in lines:
                x1, y1, x2, y2 = line[0]
                length = ((x2 - x1) ** 2 + (y2 - y1) ** 2) ** 0.5
                
                # Filter out very short lines
                if length > 15:
                    # Calculate angle
                    angle = math.atan2(y2 - y1, x2 - x1) * 180 / math.pi
                    if angle < 0:
                        angle += 180
                    
                    detected_lines.append({
                        'x1': float(x1), 'y1': float(y1),
                        'x2': float(x2), 'y2': float(y2),
                        'length': float(length),
                        'angle': float(angle)
                    })
        
        return detected_lines
        
    except Exception as e:
        logger.error(f"Enhanced line detection failed: {e}")
        return []

def merge_vertical_text(zones):
    """Merge nearby vertical text that might have been split by OCR"""
    if len(zones) < 2:
        return zones
    
    merged_zones = []
    used_indices = set()
    
    for i, zone1 in enumerate(zones):
        if i in used_indices:
            continue
            
        bbox1 = zone1.get('bbox', [])
        if len(bbox1) < 4:
            merged_zones.append(zone1)
            continue
            
        x1_1, y1_1, x2_1, y2_1 = bbox1[0], bbox1[1], bbox1[2], bbox1[3]
        text1 = zone1.get('text', '')
        
        # Look for nearby zones to merge
        merged_text = text1
        merged_bbox = [x1_1, y1_1, x2_1, y2_1]
        
        for j, zone2 in enumerate(zones[i+1:], i+1):
            if j in used_indices:
                continue
                
            bbox2 = zone2.get('bbox', [])
            if len(bbox2) < 4:
                continue
                
            x1_2, y1_2, x2_2, y2_2 = bbox2[0], bbox2[1], bbox2[2], bbox2[3]
            text2 = zone2.get('text', '')
            
            # Check if zones are vertically close and horizontally aligned
            vertical_distance = abs((y1_1 + y2_1) / 2 - (y1_2 + y2_2) / 2)
            horizontal_overlap = not (x2_1 < x1_2 or x2_2 < x1_1)
            
            # If zones are close vertically and overlap horizontally, merge them
            if vertical_distance < 30 and horizontal_overlap:
                # Merge text
                if y1_2 < y1_1:  # zone2 is above zone1
                    merged_text = text2 + text1
                    merged_bbox = [min(x1_1, x1_2), min(y1_1, y1_2), 
                                 max(x2_1, x2_2), max(y2_1, y2_2)]
                else:  # zone1 is above zone2
                    merged_text = text1 + text2
                    merged_bbox = [min(x1_1, x1_2), min(y1_1, y1_2), 
                                 max(x2_1, x2_2), max(y2_1, y2_2)]
                
                used_indices.add(j)
                logger.info(f"Merged vertical text: '{text1}' + '{text2}' = '{merged_text}'")
        
        # Create merged zone
        merged_zone = {
            "id": f"ocr_zone_{len(merged_zones)}",
            "text": merged_text,
            "confidence": zone1.get('confidence', 0.9),
            "bbox": merged_bbox,
            "x": float(merged_bbox[0]),
            "y": float(merged_bbox[1]),
            "width": float(merged_bbox[2] - merged_bbox[0]),
            "height": float(merged_bbox[3] - merged_bbox[1]),
            "orientation": zone1.get('orientation', 0),
            "rotation": 0
        }
        
        merged_zones.append(merged_zone)
        used_indices.add(i)
    
    # Add any remaining zones that weren't merged
    for i, zone in enumerate(zones):
        if i not in used_indices:
            merged_zones.append(zone)
    
    logger.info(f"Merged {len(zones)} zones into {len(merged_zones)} zones")
    return merged_zones

def crop_image_to_rectangle(image_path, rectangle_bounds):
    """Crop image to the specified rectangle bounds and rotate if needed"""
    try:
        import cv2
        
        # Load image
        img = cv2.imread(image_path)
        if img is None:
            logger.error(f"Failed to load image: {image_path}")
            return None
        
        # Get rectangle coordinates
        x1 = int(rectangle_bounds.get('x1', 0))
        y1 = int(rectangle_bounds.get('y1', 0))
        x2 = int(rectangle_bounds.get('x2', 0))
        y2 = int(rectangle_bounds.get('y2', 0))
        
        # Validate coordinates
        if x1 >= x2 or y1 >= y2:
            logger.error(f"Invalid rectangle coordinates: ({x1}, {y1}, {x2}, {y2})")
            return None
        
        # Ensure coordinates are within image bounds
        height, width = img.shape[:2]
        x1 = max(0, min(x1, width))
        y1 = max(0, min(y1, height))
        x2 = max(0, min(x2, width))
        y2 = max(0, min(y2, height))
        
        if x1 >= x2 or y1 >= y2:
            logger.error(f"Rectangle coordinates out of bounds: ({x1}, {y1}, {x2}, {y2})")
            return None
        
        # Crop image
        cropped_img = img[y1:y2, x1:x2]
        
        # Check if rectangle is taller than wide (likely vertical text)
        crop_height, crop_width = cropped_img.shape[:2]
        aspect_ratio = crop_height / crop_width if crop_width > 0 else 1
        
        was_rotated = False
        # If aspect ratio > 1.2, rotate to horizontal for better OCR
        if aspect_ratio > 1.2:
            logger.info(f"Rotating cropped image (aspect_ratio={aspect_ratio:.2f}) for better OCR")
            # Rotate 90 degrees counter-clockwise to make vertical text horizontal
            rotated_img = cv2.rotate(cropped_img, cv2.ROTATE_90_COUNTERCLOCKWISE)
            cropped_img = rotated_img
            was_rotated = True
        
        # Save cropped (and possibly rotated) image
        cropped_path = image_path.replace('.jpg', '_cropped.jpg')
        cv2.imwrite(cropped_path, cropped_img)
        
        logger.info(f"Cropped image from ({x1}, {y1}, {x2}, {y2}) to {cropped_path}")
        return cropped_path, was_rotated
        
    except Exception as e:
        logger.error(f"Failed to crop image: {e}")
        return None, False

def detect_and_process_vertical_text(img):
    """Detect vertical text regions and process them with rotation for better OCR"""
    try:
        logger.info("🔍 Detecting vertical text regions...")
        
        # Convert to grayscale
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        
        # Use edge detection to find text-like regions
        edges = cv2.Canny(gray, 50, 150)
        
        # Find contours
        contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        
        vertical_zones = []
        logger.info(f"🔍 Found {len(contours)} contours from edge detection")
        
        for i, contour in enumerate(contours):
            # Get bounding rectangle
            x, y, w, h = cv2.boundingRect(contour)
            
            # Filter by size (text-like dimensions)
            if w < 10 or h < 10 or w > 200 or h > 200:
                continue
            
            # Check if region looks like vertical text (height > width)
            aspect_ratio = h / w if w > 0 else 0
            logger.info(f"🔍 Contour {i}: w={w}, h={h}, aspect_ratio={aspect_ratio:.2f}")
            
            if aspect_ratio < 1.2:  # More lenient threshold
                continue
            
            # Extract region
            region = img[y:y+h, x:x+w]
            
            # Rotate region 90 degrees counter-clockwise for better OCR
            rotated_region = cv2.rotate(region, cv2.ROTATE_90_COUNTERCLOCKWISE)
            
            # Save rotated region temporarily
            temp_path = f"temp_vertical_{x}_{y}.jpg"
            cv2.imwrite(temp_path, rotated_region)
            
            try:
                # Run OCR on rotated region
                result = ocr.predict(temp_path)
                
                if result and result[0]:
                    for detection in result[0]:
                        if detection and len(detection) >= 2:
                            text = detection[1][0] if detection[1] else ""
                            confidence = detection[1][1] if detection[1] and len(detection[1]) > 1 else 0
                            
                            if confidence > 0.3 and text.strip():  # Only keep confident detections
                                # Convert coordinates back to original image
                                # The rotated region coordinates need to be mapped back
                                rotated_bbox = detection[0]
                                
                                # Map rotated coordinates back to original image
                                # Since we rotated 90° counter-clockwise, we need to rotate back
                                orig_x1 = x + (h - rotated_bbox[2][1])  # y becomes x
                                orig_y1 = y + rotated_bbox[0][0]       # x becomes y
                                orig_x2 = x + (h - rotated_bbox[0][1])
                                orig_y2 = y + rotated_bbox[2][0]
                                
                                zone = {
                                    "text": text,
                                    "confidence": confidence,
                                    "bbox": {
                                        "x1": int(orig_x1),
                                        "y1": int(orig_y1),
                                        "x2": int(orig_x2),
                                        "y2": int(orig_y2),
                                        "width": int(orig_x2 - orig_x1),
                                        "height": int(orig_y2 - orig_y1)
                                    },
                                    "text_orientation": 90,  # Mark as vertical
                                    "rotation": 90,
                                    "is_dimension": is_dimension_text_advanced(text),
                                    "tolerance_info": parse_tolerance(text)
                                }
                                
                                vertical_zones.append(zone)
                                logger.info(f"🔍 Found vertical text: '{text}' at ({orig_x1},{orig_y1},{orig_x2},{orig_y2})")
                
                # Clean up temp file
                os.unlink(temp_path)
                
            except Exception as e:
                logger.error(f"Error processing vertical region: {e}")
                if os.path.exists(temp_path):
                    os.unlink(temp_path)
        
        logger.info(f"🔍 Found {len(vertical_zones)} vertical text zones")
        return vertical_zones
        
    except Exception as e:
        logger.error(f"Error in vertical text detection: {e}")
        return []

def smart_text_completion(zones):
    """Smart text completion for common patterns like .5 -> 11.5"""
    logger.info("🧠 Running smart text completion...")
    
    # Common patterns to look for
    patterns = {
        '.5': ['11.5', '9.5', '7.5', '5.5', '3.5', '1.5'],
        '.0': ['10.0', '20.0', '30.0', '40.0', '50.0'],
        '.25': ['11.25', '9.25', '7.25'],
        '.75': ['11.75', '9.75', '7.75'],
        '5': ['15', '25', '35', '45', '55'],
        '0': ['10', '20', '30', '40', '50']
    }
    
    completed_zones = []
    
    for zone in zones:
        text = zone.get('text', '').strip()
        completed_zone = zone.copy()
        
        # Check if text matches a pattern
        for pattern, completions in patterns.items():
            if text == pattern:
                logger.info(f"🧠 Found pattern '{pattern}', trying completions: {completions}")
                
                # Try to find nearby zones that could complete this pattern
                bbox = zone.get('bbox', {})
                if bbox:
                    zone_center_x = (bbox.get('x1', 0) + bbox.get('x2', 0)) / 2
                    zone_center_y = (bbox.get('y1', 0) + bbox.get('y2', 0)) / 2
                    
                    # Look for nearby zones that could be the missing part
                    for other_zone in zones:
                        if other_zone == zone:
                            continue
                            
                        other_bbox = other_zone.get('bbox', {})
                        if not other_bbox:
                            continue
                            
                        other_center_x = (other_bbox.get('x1', 0) + other_bbox.get('x2', 0)) / 2
                        other_center_y = (other_bbox.get('y1', 0) + other_bbox.get('y2', 0)) / 2
                        
                        distance = ((other_center_x - zone_center_x) ** 2 + (other_center_y - zone_center_y) ** 2) ** 0.5
                        
                        # If zones are close (within 100px), try to combine them
                        if distance < 100:
                            other_text = other_zone.get('text', '').strip()
                            
                            # Try different combinations
                            for completion in completions:
                                if completion.startswith(other_text) and completion.endswith(text):
                                    logger.info(f"🧠 Smart completion: '{other_text}' + '{text}' = '{completion}'")
                                    
                                    # Update the zone with completed text
                                    completed_zone['text'] = completion
                                    completed_zone['confidence'] = min(zone.get('confidence', 0), other_zone.get('confidence', 0))
                                    
                                    # Merge bounding boxes
                                    merged_bbox = {
                                        'x1': min(bbox.get('x1', 0), other_bbox.get('x1', 0)),
                                        'y1': min(bbox.get('y1', 0), other_bbox.get('y1', 0)),
                                        'x2': max(bbox.get('x2', 0), other_bbox.get('x2', 0)),
                                        'y2': max(bbox.get('y2', 0), other_bbox.get('y2', 0))
                                    }
                                    merged_bbox['width'] = merged_bbox['x2'] - merged_bbox['x1']
                                    merged_bbox['height'] = merged_bbox['y2'] - merged_bbox['y1']
                                    completed_zone['bbox'] = merged_bbox
                                    
                                    break
                            break
        
        completed_zones.append(completed_zone)
    
    logger.info(f"🧠 Smart completion processed {len(zones)} zones")
    return completed_zones

def merge_nearby_text_zones(zones):
    """Merge nearby text zones that might be parts of the same number"""
    if len(zones) <= 1:
        return zones
    
    merged_zones = []
    used_indices = set()
    
    for i, zone1 in enumerate(zones):
        if i in used_indices:
            continue
            
        text1 = zone1.get('text', '')
        bbox1 = zone1.get('bbox', {})
        orientation1 = zone1.get('text_orientation', 0)
        
        if not bbox1:
            merged_zones.append(zone1)
            continue
            
        # Find nearby zones to merge (within 50 pixels)
        merge_candidates = []
        for j, zone2 in enumerate(zones[i+1:], i+1):
            if j in used_indices:
                continue
                
            text2 = zone2.get('text', '')
            bbox2 = zone2.get('bbox', {})
            orientation2 = zone2.get('text_orientation', 0)
            
            if not bbox2:
                continue
                
            # Check if orientations are similar (within 45 degrees)
            orientation_diff = abs(orientation1 - orientation2)
            if orientation_diff > 45 and orientation_diff < 315:  # Account for 0/360 wrap
                continue
                
            # Calculate distance between zone centers
            center1_x = (bbox1.get('x1', 0) + bbox1.get('x2', 0)) / 2
            center1_y = (bbox1.get('y1', 0) + bbox1.get('y2', 0)) / 2
            center2_x = (bbox2.get('x1', 0) + bbox2.get('x2', 0)) / 2
            center2_y = (bbox2.get('y1', 0) + bbox2.get('y2', 0)) / 2
            
            distance = ((center1_x - center2_x) ** 2 + (center1_y - center2_y) ** 2) ** 0.5
            
            # If zones are close and text looks like it could be merged
            if distance < 50 and (text1.isdigit() or text1 in '.-') and (text2.isdigit() or text2 in '.-'):
                merge_candidates.append((j, zone2, distance))
        
        if merge_candidates:
            # Sort by distance and merge with the closest candidate
            merge_candidates.sort(key=lambda x: x[2])
            closest_j, closest_zone, _ = merge_candidates[0]
            
            # Determine merge order based on position
            if orientation1 == 90:  # Vertical text
                # For vertical text, merge top to bottom
                if bbox1.get('y1', 0) < closest_zone.get('bbox', {}).get('y1', 0):
                    merged_text = text1 + text2
                else:
                    merged_text = text2 + text1
            else:  # Horizontal text
                # For horizontal text, merge left to right
                if bbox1.get('x1', 0) < closest_zone.get('bbox', {}).get('x1', 0):
                    merged_text = text1 + text2
                else:
                    merged_text = text2 + text1
            
            # Create merged zone
            merged_bbox = {
                'x1': min(bbox1.get('x1', 0), closest_zone.get('bbox', {}).get('x1', 0)),
                'y1': min(bbox1.get('y1', 0), closest_zone.get('bbox', {}).get('y1', 0)),
                'x2': max(bbox1.get('x2', 0), closest_zone.get('bbox', {}).get('x2', 0)),
                'y2': max(bbox1.get('y2', 0), closest_zone.get('bbox', {}).get('y2', 0))
            }
            
            merged_zone = {
                'id': f"merged_zone_{i}_{closest_j}",
                'text': merged_text,
                'confidence': min(zone1.get('confidence', 0), closest_zone.get('confidence', 0)),
                'bbox': merged_bbox,
                'text_orientation': orientation1,
                'is_dimension': is_dimension_text_advanced(merged_text),
                'tolerance_info': parse_tolerance(merged_text)
            }
            
            merged_zones.append(merged_zone)
            used_indices.add(i)
            used_indices.add(closest_j)
            
            logger.info(f"Merged nearby zones: '{text1}' + '{text2}' = '{merged_text}'")
        else:
            merged_zones.append(zone1)
            used_indices.add(i)
    
    return merged_zones

def create_overlay_image(image_path, zones, lines=None, dimension_lines=None):
    """Create overlay image with bounding boxes and lines (using direct coordinates, no rotation)"""
    try:
        # Load original image
        img = cv2.imread(str(image_path))
        if img is None:
            return None
        
        # Create overlay with better visualization
        overlay = img.copy()
        
        # Create a separate overlay for semi-transparent rectangles
        overlay_color = np.zeros_like(img)
        
        # Draw lines first (so they appear behind text boxes)
        if lines:
            for line in lines:
                x1, y1, x2, y2 = int(line['x1']), int(line['y1']), int(line['x2']), int(line['y2'])
                cv2.line(overlay, (x1, y1), (x2, y2), (128, 128, 128), 1)  # Gray lines
        
        # Draw dimension lines in different color
        if dimension_lines:
            for dim_line in dimension_lines:
                line = dim_line['closest_line']
                x1, y1, x2, y2 = int(line['x1']), int(line['y1']), int(line['x2']), int(line['y2'])
                cv2.line(overlay, (x1, y1), (x2, y2), (0, 255, 255), 2)  # Yellow for dimension lines
        
        # Draw clean bounding boxes for each zone
        for i, zone in enumerate(zones):
            bbox = zone.get("bbox", {})
            if not bbox:
                continue
            
            # Extract coordinates with NaN and type safety
            x1 = bbox.get("x1", 0)
            y1 = bbox.get("y1", 0)
            x2 = bbox.get("x2", 0)
            y2 = bbox.get("y2", 0)
            
            # Validate coordinates
            if not all(isinstance(coord, (int, float)) and not math.isnan(coord) for coord in [x1, y1, x2, y2]):
                logger.warning(f"Invalid coordinates in zone {i}: x1={x1}, y1={y1}, x2={x2}, y2={y2}")
                continue
            
            # Ensure coordinates are within image bounds
            x1 = max(0, min(int(x1), img.shape[1] - 1))
            y1 = max(0, min(int(y1), img.shape[0] - 1))
            x2 = max(0, min(int(x2), img.shape[1]))
            y2 = max(0, min(int(y2), img.shape[0]))
            
            # Skip if box is too small
            if x2 - x1 < 1 or y2 - y1 < 1:
                continue
            
            # Color for this zone (cycling through colors)
            colors = [(0, 255, 0), (255, 0, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255), (0, 255, 255)]
            color = colors[i % len(colors)]
            
            # Draw semi-transparent rectangle
            cv2.rectangle(overlay_color, 
                         (x1, y1), 
                         (x2, y2), 
                         color, -1)  # Filled rectangle
            
            # Draw clean border
            cv2.rectangle(overlay, 
                         (x1, y1), 
                         (x2, y2), 
                         color, 2)  # Border only
            
            # Draw clean text label with background
            label_text = f"{i+1}. {zone.get('text', '')} ({int(zone.get('confidence', 0)*100)}%)"
            font_scale = 0.5
            thickness = 1
            (text_width, text_height), _ = cv2.getTextSize(label_text, cv2.FONT_HERSHEY_SIMPLEX, font_scale, thickness)
            
            # Position label above the box
            label_x = x1
            label_y = max(y1 - 10, text_height + 5)
            
            # Ensure label is within image bounds
            if label_y > 0 and label_x >= 0:
                # Draw text background
                cv2.rectangle(overlay, 
                             (label_x - 2, label_y - text_height - 2), 
                             (label_x + text_width + 2, label_y + 2), 
                             (255, 255, 255), -1)  # White background
                
                # Draw text border for better readability
                cv2.putText(overlay, label_text, 
                           (label_x, label_y), 
                           cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255), thickness + 1)  # White border
                cv2.putText(overlay, label_text, 
                           (label_x, label_y), 
                           cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 0), thickness)  # Black text
        
        # Blend the colored overlay with the original image
        alpha = 0.3
        overlay = cv2.addWeighted(overlay, 1, overlay_color, alpha, 0)
                    
        # Save overlay image
        overlay_path = str(image_path).replace('.', '_overlay.')
        cv2.imwrite(overlay_path, overlay)
        
        return overlay_path
        
    except Exception as e:
        logger.error(f"Error creating overlay: {e}")
        import traceback
        logger.error(f"Traceback: {traceback.format_exc()}")
        return None

def process_ocr_result(result, mode="fast"):
    """Process OCR result and extract zones"""
    zones = []
    
    if not result or len(result) == 0:
        return zones
    
    logger.info(f"🔍 OCR result type: {type(result)}, length: {len(result)}")
    
    # Check if it's predict format (with .json attribute)
    if hasattr(result[0], 'json'):
        zones = process_predict_format(result)
    else:
        zones = process_ocr_format(result)
    
    return zones

def process_predict_format(result):
    """Process predict() format OCR results"""
    zones = []
    
    for res in result:
        if hasattr(res, 'json') and 'res' in res.json:
            res_data = res.json['res']
            if 'rec_texts' in res_data and 'rec_scores' in res_data:
                texts = res_data['rec_texts']
                scores = res_data['rec_scores']
                dt_polys = res_data.get('dt_polys', [])
                
                logger.info(f"📝 Found {len(texts)} texts: {texts}")
                logger.info(f"📊 Found {len(scores)} scores: {[f'{s:.3f}' for s in scores]}")
                logger.info(f"📐 Found {len(dt_polys)} polygons")
                
                # Debug: Log each text with its polygon coordinates
                for i, (text, score, poly) in enumerate(zip(texts, scores, dt_polys)):
                    logger.info(f"DEBUG Text {i}: '{text}' (score: {score:.3f}) at polygon: {poly}")
                
                for i, (text, score) in enumerate(zip(texts, scores)):
                    zone = create_zone_from_predict(text, score, dt_polys, i)
                    if zone:
                        zones.append(zone)
    
    return zones

def process_ocr_format(result):
    """Process ocr() format OCR results"""
    zones = []
    
    for line in result:
        if line:
            for item in line:
                if len(item) >= 2:
                    bbox = item[0]
                    text_info = item[1]
                    
                    # Handle text that might be tuple or string
                    if isinstance(text_info, (tuple, list)) and len(text_info) >= 2:
                        text = text_info[0]
                        confidence = text_info[1]
                    elif isinstance(text_info, str):
                        text = text_info
                        confidence = 0.9
                    else:
                        continue
                    
                    if isinstance(text, str) and text.strip():
                        zone = create_zone_from_ocr(text, confidence, bbox, len(zones))
                        if zone:
                            zones.append(zone)
    
    return zones

def create_zone_from_predict(text, score, dt_polys, index):
    """Create zone from predict format data with improved confidence handling"""
    if not text.strip() or index >= len(dt_polys):
        return None
    
    # Filter out meaningless text
    if (len(text.strip()) < 1 or 
        text.strip() in ['.', '-', ',', ':', ';'] or 
        score <= 0.3):
        return None
    
    poly = dt_polys[index]
    
    # Convert polygon to bounding box
    x_coords = [p[0] for p in poly]
    y_coords = [p[1] for p in poly]
    x1, x2 = min(x_coords), max(x_coords)
    y1, y2 = min(y_coords), max(y_coords)
    
    # Validate coordinates
    if (math.isnan(x1) or math.isnan(x2) or math.isnan(y1) or math.isnan(y2) or
        x1 >= x2 or y1 >= y2 or x2 - x1 < 1 or y2 - y1 < 1):
        logger.warning(f"❌ Invalid coordinates for text '{text}'")
        return None
    
    # Detect orientation
    text_orientation = detect_text_orientation_advanced(poly, text)
    
    # Calculate bounding box dimensions for confidence adjustment
    bbox_width = x2 - x1
    bbox_height = y2 - y1
    aspect_ratio = bbox_height / bbox_width if bbox_width > 0 else 1
    
    # Adjust confidence for vertical text (they often have lower raw scores)
    adjusted_score = float(score)
    if text_orientation in [90, 270] and aspect_ratio > 1.2:
        # Boost confidence for vertical text that looks like dimensions
        if is_dimension_text_advanced(text):
            adjusted_score = min(0.95, adjusted_score + 0.2)
            logger.info(f"🔍 Boosted confidence for vertical dimension '{text}': {score:.3f} -> {adjusted_score:.3f}")
    
    # Clean text - remove trailing periods that aren't part of decimal numbers
    clean_text = text.strip()
    if clean_text.endswith('.') and not re.search(r'\d+\.\d*$', clean_text):
        clean_text = clean_text[:-1]
        logger.info(f"🧹 Cleaned trailing period: '{text}' -> '{clean_text}'")
    
    # Detect category automatically
    detected_category = detect_zone_category(clean_text)
    
    # Create zone
    zone = {
        "id": f"ocr_zone_{index}",
        "text": clean_text,
        "confidence": adjusted_score,
        "bbox": {
            "x1": int(x1),
            "y1": int(y1),
            "x2": int(x2),
            "y2": int(y2),
            "width": int(x2 - x1),
            "height": int(y2 - y1)
        },
        "polygon": poly,
        "text_orientation": text_orientation,
        "rotation": text_orientation,  # Also send as 'rotation' for frontend compatibility
        "is_dimension": is_dimension_text_advanced(clean_text),
        "tolerance_info": parse_tolerance(clean_text),
        "category": detected_category
    }
    
    # Log tolerance parsing results
    tolerance_info = zone.get("tolerance_info")
    if tolerance_info:
        print(f"🔍 ZONE TOLERANCE: '{clean_text}' -> {tolerance_info}")
    
    logger.info(f"✅ Zone {index} CREATED: text='{clean_text}', orient={text_orientation}°, conf={adjusted_score:.3f}, bbox=({x1},{y1},{x2},{y2})")
    return zone

def create_zone_from_ocr(text, confidence, bbox, index):
    """Create zone from ocr format data"""
    # Extract coordinates from bbox
    x_coords = [point[0] for point in bbox]
    y_coords = [point[1] for point in bbox]
    x1, x2 = min(x_coords), max(x_coords)
    y1, y2 = min(y_coords), max(y_coords)
    
    # Validate coordinates
    if (math.isnan(x1) or math.isnan(x2) or math.isnan(y1) or math.isnan(y2) or
        x1 >= x2 or y1 >= y2 or x2 - x1 < 1 or y2 - y1 < 1):
        return None
    
    # Basic orientation detection
    text_orientation = 0
    width_bbox = x2 - x1
    height_bbox = y2 - y1
    if height_bbox > width_bbox * 1.5:
        text_orientation = 90
    
    # Detect category automatically
    detected_category = detect_zone_category(text)
    
    # Create zone
    zone = {
        "id": f"ocr_zone_{index}",
        "text": text,
        "confidence": float(confidence),
        "bbox": {
            "x1": int(x1),
            "y1": int(y1),
            "x2": int(x2),
            "y2": int(y2),
            "width": int(x2 - x1),
            "height": int(y2 - y1)
        },
        "polygon": bbox,
        "text_orientation": text_orientation,
        "is_dimension": is_dimension_text(text),
        "tolerance_info": parse_tolerance(text),
        "category": detected_category
    }
    
    # Log tolerance parsing results
    tolerance_info = zone.get("tolerance_info")
    if tolerance_info:
        print(f"🔍 ZONE TOLERANCE: '{text}' -> {tolerance_info}")
    
    return zone

def resize_image_for_speed(image_path, max_dimension=1024):
    """Resize large images to speed up OCR processing"""
    img = cv2.imread(str(image_path))
    if img is None:
        return image_path
    
    height, width = img.shape[:2]
    max_size = max(height, width)
    
    # Only resize if image is larger than max_dimension
    if max_size > max_dimension:
        scale = max_dimension / max_size
        new_width = int(width * scale)
        new_height = int(height * scale)
        
        logger.info(f"🚀 Resizing image from {width}x{height} to {new_width}x{new_height} for faster OCR")
        
        resized_img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_AREA)
        
        # Save resized image to temporary file
        temp_path = create_secure_temp_file('.jpg')
        cv2.imwrite(temp_path, resized_img)
        
        return temp_path
    
    return image_path

def process_image(image_path, mode="fast", rotation=0):
    """Process a single image and return OCR results with specified mode"""
    logger.info(f">>> process_image CALLED! image_path={image_path}, mode={mode}, rotation={rotation}")
    
    # Resize image for faster processing (except in hardcore mode)
    if mode != "hardcore":
        image_path = resize_image_for_speed(image_path, max_dimension=1024)
    
    # Preprocessing if hardcore mode
    if mode == "hardcore":
        logger.info("🔥 HARDCORE MODE: Applying image preprocessing...")
        original_path = image_path
        image_path = preprocess_image_for_ocr(image_path)
        logger.info(f"🔥 Preprocessing complete: {original_path} -> {image_path}")
    
    try:
        # Get image dimensions for coordinate transformation
        img = cv2.imread(str(image_path))
        if img is None:
            logger.error(f"Failed to load image: {image_path}")
            return {"zones": [], "metadata": {"error": "Failed to load image"}}
        
        height, width = img.shape[:2]
        
        # Apply rotation if specified
        if rotation != 0:
            center = (width // 2, height // 2)
            rotation_matrix = cv2.getRotationMatrix2D(center, rotation, 1.0)
            img = cv2.warpAffine(img, rotation_matrix, (width, height))
            
            # Save rotated image temporarily
            import tempfile
            rotated_fd, rotated_path = tempfile.mkstemp(suffix='_rotated.jpg')
            os.close(rotated_fd)  # Close the file descriptor
            cv2.imwrite(rotated_path, img)
            logger.info(f"Saved rotated image to: {rotated_path}")
            image_path = rotated_path
        
        # Use single OCR model
        logger.info(f"Processing image with OCR mode: {mode}, rotation: {rotation}")
        
        # OCR with parameters according to mode
        try:
            if mode in ["accurate", "hardcore"]:
                logger.info("🔥 Using HARDCORE OCR parameters for maximum detection")
                result = ocr.predict(str(image_path))
            else:
                logger.info("⚡ Using FAST mode OCR parameters")
                result = ocr.predict(str(image_path))
        except Exception as ocr_error:
            logger.error(f"❌ OCR processing failed: {ocr_error}")
            logger.error(f"❌ Error type: {type(ocr_error).__name__}")
            
            # Try fallback with minimal parameters
            try:
                logger.info("🔄 Trying fallback OCR with minimal parameters...")
                result = ocr.predict(str(image_path))
            except Exception as fallback_error:
                logger.error(f"❌ Fallback OCR also failed: {fallback_error}")
                return {"zones": [], "metadata": {"error": f"OCR processing failed: {str(ocr_error)}"}}
        
        # Process OCR result using helper functions
        zones = process_ocr_result(result, mode)
        
        # ENHANCED VERTICAL TEXT DETECTION
        logger.info("🔍 Running enhanced vertical text detection...")
        logger.info(f"🔍 Image shape: {img.shape}, zones before vertical detection: {len(zones)}")
        
        # First, check existing zones for vertical text that might be misclassified
        logger.info("🔍 Checking existing zones for vertical text...")
        for i, zone in enumerate(zones):
            bbox = zone.get('bbox', {})
            if bbox:
                w = bbox.get('width', 0)
                h = bbox.get('height', 0)
                aspect_ratio = h / w if w > 0 else 0
                logger.info(f"🔍 Zone {i}: '{zone.get('text', '')}' w={w}, h={h}, aspect_ratio={aspect_ratio:.2f}")
                
                # If aspect ratio suggests vertical text but orientation is wrong, try re-OCR
                if aspect_ratio > 1.2 and zone.get('text_orientation', 0) == 0:
                    logger.info(f"🔍 Zone {i} might be vertical text - aspect ratio {aspect_ratio:.2f} but orientation 0°")
                    # Try re-OCR with 90° rotation
                    try:
                        # Create a temporary rotated version of this zone
                        zone_img = img[bbox.get('y1', 0):bbox.get('y2', 0), bbox.get('x1', 0):bbox.get('x2', 0)]
                        rotated_zone = cv2.rotate(zone_img, cv2.ROTATE_90_COUNTERCLOCKWISE)
                        
                        # Save and OCR the rotated zone
                        temp_path = f"temp_zone_{i}_rotated.jpg"
                        cv2.imwrite(temp_path, rotated_zone)
                        
                        try:
                            result = ocr.predict(temp_path)
                            if result and result[0]:
                                for detection in result[0]:
                                    if detection and len(detection) >= 2:
                                        rotated_text = detection[1][0] if detection[1] else ""
                                        rotated_conf = detection[1][1] if detection[1] and len(detection[1]) > 1 else 0
                                    
                                    if rotated_conf > zone.get('confidence', 0):
                                        logger.info(f"🔍 Zone {i} rotated OCR better: '{zone.get('text', '')}' → '{rotated_text}' ({rotated_conf:.2f})")
                                        # Update the zone with rotated results
                                        zone['text'] = rotated_text
                                        zone['confidence'] = rotated_conf
                                        zone['text_orientation'] = 90
                                        zone['rotation'] = 90
                        except Exception as ocr_error:
                            logger.error(f"🔍 OCR error for rotated zone {i}: {ocr_error}")
                        
                        # Clean up
                        if os.path.exists(temp_path):
                            os.unlink(temp_path)
                            
                    except Exception as e:
                        logger.error(f"🔍 Error re-OCR zone {i}: {e}")
        
        try:
            vertical_zones = detect_and_process_vertical_text(img)
            logger.info(f"🔍 Vertical detection completed, found {len(vertical_zones)} zones")
            if vertical_zones:
                logger.info(f"🔍 Vertical zones details: {[z.get('text', '') for z in vertical_zones]}")
            zones.extend(vertical_zones)
            logger.info(f"🔍 Added {len(vertical_zones)} vertical text zones to total zones")
        except Exception as e:
            logger.error(f"🔍 Error in vertical text detection: {e}")
            import traceback
            logger.error(f"🔍 Traceback: {traceback.format_exc()}")
        
        # Smart text completion for common patterns like .5 -> 11.5
        zones = smart_text_completion(zones)
        
        # Merge nearby text zones (for cases like "11.5" split into "1", "1", ".", "5")
        zones = merge_nearby_text_zones(zones)
        
        # Apply post-processing
        zones = apply_post_processing(zones)
        
        # Clean up rotated image if created
        if rotation != 0 and os.path.exists(str(image_path)) and '_rotated' in str(image_path):
            try:
                os.unlink(str(image_path))
            except OSError:
                pass
        
        # Create overlay image
        overlay_path = create_overlay_image(image_path, zones)
        
        # Return result
        result = {
            "zones": zones,
            "metadata": {
                "total_zones": len(zones),
                "original_zones_detected": len(zones),
                "zones_merged": 0,
                "overlay_path": overlay_path,
                "detected_angle": 0
            }
        }
        
        logger.info(f"📊 FINAL RESULT: {len(zones)} zones detected")
        return result
        
    except Exception as e:
        logger.error(f"Error processing image: {e}")
        import traceback
        logger.error(f"Traceback: {traceback.format_exc()}")
        return {
            "zones": [],
            "metadata": {"error": str(e)}
        }

def apply_post_processing(zones):
    """Apply post-processing to zones"""
    # Apply correction post-processing if available
    logger.info(f"🔧 Post-processing {len(zones)} zones...")
    if correction_processor:
        try:
            zones_before_corrections = len(zones)
            zones = correction_processor.process_zones(zones)
            post_processing_stats = correction_processor.get_stats()
            logger.info(f"🔧 Applied corrections: {post_processing_stats['total_corrected']} out of {post_processing_stats['total_processed']} zones")
        except Exception as e:
            logger.warning(f"❌ Failed to apply corrections: {e}")
    else:
        logger.info("🔧 No correction processor available")
    
    # Clean OCR text
    logger.info(f"🧹 Cleaning {len(zones)} zones...")
    for i, zone in enumerate(zones):
        original_text = zone.get('text', '')
        cleaned_text = clean_ocr_text_advanced(original_text)
        zone['text'] = cleaned_text
        if original_text != cleaned_text:
            logger.info(f"🧹 Zone {i}: '{original_text}' -> '{cleaned_text}'")
    
    # Remove duplicates
    logger.info(f"🗑️ Checking for duplicate zones (threshold=0.9)...")
    if remove_duplicate_zones:
        zones_before = len(zones)
        zones = remove_duplicate_zones(zones, overlap_threshold=0.9)
        if len(zones) < zones_before:
            logger.info(f"🗑️ Removed {zones_before - len(zones)} duplicate zones")
        else:
            logger.info(f"🗑️ No duplicate zones found")
    
    return zones

async def process_image_async(image_path, mode="fast", rotation=0):
    """Async wrapper for process_image function"""
    loop = asyncio.get_event_loop()
    return await loop.run_in_executor(executor, process_image, image_path, mode, rotation)


@app.get("/yolo/status")
async def yolo_status():
    """Check YOLO availability"""
    return {
        "yolo_available": YOLO_AVAILABLE,
        "yolo_model_loaded": yolo_model is not None,
        "yolo_model_type": str(type(yolo_model)) if yolo_model else None
    }

@app.get("/")
async def root():
    """Health check endpoint"""
    return {
        "message": "SPaCial AI OCR Service",
        "status": "running",
        "ocr_initialized": ocr is not None,
        "service": "SPaCial AI OCR Service",
        "version": "1.0.0"
    }

@app.post("/ocr/process")
async def process_ocr(
    file: UploadFile = File(...), 
    mode: str = Query("fast", description="OCR mode: 'fast' for position detection, 'accurate' or 'hardcore' for maximum text detection"),
    rotation: int = Query(0, description="Rotation angle in degrees (0, 90, 180, 270)")
):
    """
    Process uploaded image with OCR
    Returns detected zones with bounding boxes and text
    
    Parameters:
    - file: Image file to process
    - mode: 'fast' (default) or 'accurate'/'hardcore' for maximum detection
    - rotation: Rotation angle in degrees (0, 90, 180, 270)
    """
    if ocr is None:
        raise HTTPException(status_code=503, detail="OCR service not initialized")
    
    # Validate input parameters
    if mode not in ["fast", "accurate", "hardcore"]:
        raise HTTPException(status_code=400, detail="Invalid mode. Must be 'fast', 'accurate', or 'hardcore'")
    
    if rotation not in [0, 90, 180, 270]:
        raise HTTPException(status_code=400, detail="Invalid rotation. Must be 0, 90, 180, or 270 degrees")
    
    # Validate file for security
    if not validate_uploaded_file(file):
        raise HTTPException(status_code=400, detail="Invalid file: must be a valid image file under 10MB")
    
    # Check if file is PDF
    is_pdf = file.filename and file.filename.lower().endswith('.pdf')
    
    # Save uploaded file to secure temporary location
    temp_path = create_secure_temp_file('.pdf' if is_pdf else '.jpg')
    try:
        content = await file.read()
        with open(temp_path, 'wb') as temp_file:
            temp_file.write(content)
    except Exception as e:
        logger.error(f"Failed to save uploaded file: {e}")
        raise HTTPException(status_code=500, detail="Failed to process uploaded file")
    
    # If PDF, convert to image first
    if is_pdf:
        logger.info(f"📄 PDF detected, converting to image...")
        try:
            image_path = convert_pdf_to_image(temp_path, page_number=0, dpi=300)
            # Clean up original PDF temp file
            try:
                os.unlink(temp_path)
            except:
                pass
            temp_path = image_path
            logger.info(f"✅ PDF converted to image: {temp_path}")
        except Exception as e:
            logger.error(f"Failed to convert PDF: {e}")
            raise HTTPException(status_code=500, detail=f"Failed to convert PDF: {str(e)}")
    
    try:
        logger.info(f"🚀 API CALL: Processing {'PDF' if is_pdf else 'image'}: {file.filename}, mode: {mode}, rotation: {rotation}")
        logger.info(f"📁 Temp file saved: {temp_path}")
        
        # Validate image dimensions
        if not validate_image_dimensions(temp_path):
            raise HTTPException(status_code=400, detail="Image dimensions too large. Maximum allowed: 4096x4096 pixels")
        
        # Process image with specified mode and rotation
        result = await process_image_async(temp_path, mode, rotation)
        logger.info(f"✅ API RESPONSE: Returning {len(result.get('zones', []))} zones")
        
        logger.info(f"OCR result: {len(result.get('zones', []))} zones found")
        
        # If it was a PDF, include the converted image as base64 for frontend display
        if is_pdf:
            import base64
            with open(temp_path, 'rb') as img_file:
                image_base64 = base64.b64encode(img_file.read()).decode('utf-8')
                result['converted_image'] = f"data:image/png;base64,{image_base64}"
                logger.info("✅ Added converted PDF image to response")
        
        return JSONResponse(content=result)
        
    except Exception as e:
        logger.error(f"Error processing uploaded file: {e}")
        import traceback
        logger.error(f"Traceback: {traceback.format_exc()}")
        
        # Return structured error response
        raise HTTPException(
            status_code=500, 
            detail={
                "error": "Image processing failed",
                "message": str(e),
                "type": type(e).__name__
            }
        )
    finally:
        # Always clean up temporary file
        if 'temp_path' in locals() and os.path.exists(temp_path):
            try:
                os.unlink(temp_path)
                logger.info(f"Cleaned up temp file: {temp_path}")
            except OSError as cleanup_error:
                logger.warning(f"Failed to clean up temp file {temp_path}: {cleanup_error}")

@app.post("/ocr/process-path")
async def process_ocr_path(image_path: str = Query(...), mode: str = Query("fast", description="OCR mode: 'fast' for position detection, 'accurate' for hard text search")):
    """
    Process image from file path
    Returns detected zones with bounding boxes and text
    """
    if ocr is None:
        raise HTTPException(status_code=503, detail="OCR service not initialized")
    
    if not os.path.exists(image_path):
        raise HTTPException(status_code=404, detail="Image file not found")
    
    try:
        result = process_image(image_path, mode)
        return JSONResponse(content=result)
        
    except Exception as e:
        logger.error(f"Error processing image path: {e}")
        raise HTTPException(status_code=500, detail=f"Error processing image: {str(e)}")

@app.post("/ocr/process-center")
async def process_center_point(request: dict = Body(...)):
    """Process center point to find dimension and fit box to text"""
    try:
        logger.info(f"Processing center point request: {list(request.keys())}")
        
        # Extract data from request
        image_data = request.get('image', '')
        center_point = request.get('center_point', {})
        center_x = center_point.get('x', 0)
        center_y = center_point.get('y', 0)
        rectangle_bounds = request.get('rectangle_bounds', {})
        use_rectangle = request.get('use_rectangle', False)
        rotation = request.get('rotation', 0)  # Add rotation support
        
        logger.info(f"Center point: ({center_x}, {center_y}), Image data length: {len(image_data) if image_data else 0}")
        if use_rectangle and rectangle_bounds:
            logger.info(f"Using rectangle bounds: {rectangle_bounds}")
        logger.info(f"Rotation parameter: {rotation}°")
        
        if not image_data:
            raise HTTPException(status_code=400, detail="No image data provided")
        
        # Decode base64 image
        import base64
        try:
            if image_data.startswith('data:image'):
                image_data = image_data.split(',')[1]
            
            image_bytes = base64.b64decode(image_data)
            logger.info(f"Successfully decoded image, size: {len(image_bytes)} bytes")
        except Exception as e:
            logger.error(f"Failed to decode base64 image: {e}")
            raise HTTPException(status_code=400, detail="Invalid image data format")
        
        # Save temporary image
        with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as tmp_file:
            tmp_file.write(image_bytes)
            tmp_path = tmp_file.name
        
        try:
            if use_rectangle and rectangle_bounds:
                # RECTANGLE MODE: Use user's rectangle directly
                logger.info(f"RECTANGLE MODE: Using user's rectangle bounds: {rectangle_bounds}")
                
                # Load image and crop to user's rectangle
                img = cv2.imread(tmp_path)
                if img is None:
                    raise HTTPException(status_code=400, detail="Failed to load image")
                
                # Extract rectangle bounds
                rect_x1 = int(rectangle_bounds.get('x1', 0))
                rect_y1 = int(rectangle_bounds.get('y1', 0))
                rect_x2 = int(rectangle_bounds.get('x2', 0))
                rect_y2 = int(rectangle_bounds.get('y2', 0))
                
                # Validate bounds
                img_height, img_width = img.shape[:2]
                rect_x1 = max(0, min(rect_x1, img_width))
                rect_y1 = max(0, min(rect_y1, img_height))
                rect_x2 = max(rect_x1, min(rect_x2, img_width))
                rect_y2 = max(rect_y1, min(rect_y2, img_height))
                
                logger.info(f"Cropping image to rectangle: ({rect_x1}, {rect_y1}) to ({rect_x2}, {rect_y2})")
                
                # Crop image to rectangle
                cropped_img = img[rect_y1:rect_y2, rect_x1:rect_x2]
                
                # Apply rotation if specified
                if rotation != 0:
                    logger.info(f"Rotating cropped image by {rotation}°")
                    height, width = cropped_img.shape[:2]
                    center = (width // 2, height // 2)
                    rotation_matrix = cv2.getRotationMatrix2D(center, rotation, 1.0)
                    cropped_img = cv2.warpAffine(cropped_img, rotation_matrix, (width, height))
                
                # Save cropped image temporarily
                cropped_path = tmp_path.replace('.jpg', '_cropped.jpg')
                cv2.imwrite(cropped_path, cropped_img)
                
                # Run OCR on cropped image
                logger.info(f"Running OCR on cropped image with rotation: {rotation}°")
                result = await process_image_async(cropped_path, mode="hardcore", rotation=0)  # No additional rotation
                
                # Clean up cropped image
                if os.path.exists(cropped_path):
                    os.unlink(cropped_path)
                
                if result and result.get('zones'):
                    # Use the first (and likely only) zone from cropped image
                    zone = result['zones'][0]
                    
                    # Adjust coordinates back to original image
                    zone_bbox = zone.get('bbox', {})
                    adjusted_bbox = {
                        'x1': rect_x1 + zone_bbox.get('x1', 0),
                        'y1': rect_y1 + zone_bbox.get('y1', 0),
                        'x2': rect_x1 + zone_bbox.get('x2', 0),
                        'y2': rect_y1 + zone_bbox.get('y2', 0),
                        'width': zone_bbox.get('width', 0),
                        'height': zone_bbox.get('height', 0)
                    }
                    
                    logger.info(f"RECTANGLE RESULT: '{zone.get('text', '')}' at adjusted bbox: {adjusted_bbox}")
                    
                    return {
                        "zone": {
                            "text": zone.get('text', ''),
                            "confidence": zone.get('confidence', 0),
                            "bbox": adjusted_bbox,
                            "x": adjusted_bbox.get('x1', 0),
                            "y": adjusted_bbox.get('y1', 0),
                            "width": adjusted_bbox.get('width', 0),
                            "height": adjusted_bbox.get('height', 0),
                            "tolerance_info": zone.get('tolerance_info', {}),
                            "text_orientation": zone.get('text_orientation', rotation),
                            "rotation": zone.get('rotation', rotation)
                        },
                        "message": f"Found dimension: '{zone.get('text', '')}' in user's rectangle",
                        "center_point": {"x": center_x, "y": center_y},
                        "found_zone": zone.get('text', ''),
                        "fitted": True
                    }
                else:
                    logger.warning("No text found in user's rectangle - creating empty zone")
                    
                    # Create empty zone with the user's rectangle bounds
                    empty_zone_bbox = {
                        'x1': rect_x1,
                        'y1': rect_y1,
                        'x2': rect_x2,
                        'y2': rect_y2,
                        'width': rect_x2 - rect_x1,
                        'height': rect_y2 - rect_y1
                    }
                    
                    return {
                        "zone": {
                            "text": "[No Text]",
                            "confidence": 0.0,
                            "bbox": empty_zone_bbox,
                            "x": rect_x1,
                            "y": rect_y1,
                            "width": empty_zone_bbox['width'],
                            "height": empty_zone_bbox['height'],
                            "tolerance_info": {},
                            "text_orientation": rotation,
                            "rotation": rotation,
                            "is_empty": True  # Flag to identify empty zones
                        },
                        "message": "Empty zone created (no text detected)",
                        "center_point": {"x": center_x, "y": center_y},
                        "found_zone": "[No Text]",
                        "fitted": True
                    }
            else:
                # POINT MODE: Use existing logic for center point detection
                logger.info(f"POINT MODE: Processing center point ({center_x}, {center_y})")
                result = await process_image_async(tmp_path, mode="fast", rotation=rotation)
                
                if not result or not result.get('zones'):
                    logger.warning("No zones found in processed result")
                    return {"zones": [], "message": "No text detected"}
                
                zones = result['zones']
                logger.info(f"Found {len(zones)} zones, looking for best match...")
                
                best_zone = None
                best_score = -1
                
                for zone in zones:
                    bbox = zone.get('bbox', {})
                    if not bbox:
                        continue
                    
                    zone_center_x = (bbox.get('x1', 0) + bbox.get('x2', 0)) / 2
                    zone_center_y = (bbox.get('y1', 0) + bbox.get('y2', 0)) / 2
                    zone_confidence = zone.get('confidence', 0)
                    
                    # Calculate distance score
                    distance = ((center_x - zone_center_x) ** 2 + (center_y - zone_center_y) ** 2) ** 0.5
                    score = zone_confidence / (1 + distance / 100)
                    
                    logger.info(f"POINT: Zone '{zone.get('text', '')}' dist={distance:.1f}, conf={zone_confidence:.3f}, score={score:.3f}")
                    
                    if score > best_score:
                        best_score = score
                        best_zone = zone
                
                min_score = 0.05
                logger.info(f"BEST ZONE: {best_zone.get('text', 'None') if best_zone else 'None'}, score={best_score:.3f}, threshold={min_score}")
                
                if best_zone and best_score >= min_score:
                    zone_bbox = best_zone.get('bbox', {})
                    return {
                        "zone": {
                            "text": best_zone.get('text', ''),
                            "confidence": best_zone.get('confidence', 0),
                            "bbox": zone_bbox,
                            "x": zone_bbox.get('x1', 0),
                            "y": zone_bbox.get('y1', 0),
                            "width": zone_bbox.get('width', 0),
                            "height": zone_bbox.get('height', 0),
                            "tolerance_info": best_zone.get('tolerance_info', {})
                        },
                        "message": f"Found dimension: '{best_zone.get('text', '')}' (score: {best_score:.3f})",
                        "center_point": {"x": center_x, "y": center_y},
                        "found_zone": best_zone.get('text', ''),
                        "fitted": True
                    }
                else:
                    return {
                        "zones": [],
                        "message": f"No dimension found (best score: {best_score:.3f}, threshold: {min_score})",
                        "center_point": {"x": center_x, "y": center_y}
                    }
                
        finally:
            # Clean up temporary file
            if os.path.exists(tmp_path):
                os.unlink(tmp_path)
                
    except Exception as e:
        logger.error(f"Error processing center point: {e}")
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/yolo/detect-zones")
async def yolo_detect_zones(
    file: UploadFile = File(...),
    confidence: float = Query(0.01, description="Confidence threshold for YOLO detection"),
    use_obb: bool = Query(True, description="Use Oriented Bounding Boxes for rotated text")
):
    """
    Use YOLOv11 to detect text regions, then use PaddleOCR to read them
    Hybrid approach: YOLO for fast detection + PaddleOCR for text recognition
    """
    logger.info(f"🎯 YOLO endpoint called! YOLO_AVAILABLE={YOLO_AVAILABLE}, yolo_model={yolo_model is not None}")
    
    if not YOLO_AVAILABLE or yolo_model is None:
        raise HTTPException(status_code=503, detail=f"YOLOv11 not available. YOLO_AVAILABLE={YOLO_AVAILABLE}, yolo_model={'None' if yolo_model is None else 'exists'}")
    
    if not ocr:
        raise HTTPException(status_code=503, detail="OCR service not initialized")
    
    tmp_path = None
    try:
        # Save uploaded file
        with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as tmp_file:
            content = await file.read()
            tmp_file.write(content)
            tmp_path = tmp_file.name
        
        logger.info(f"🎯 YOLO: Detecting zones in {file.filename}...")
        
        # Use YOLO for detection
        results = yolo_model.predict(
            source=tmp_path,
            conf=confidence,
            verbose=False,
            device='cpu'  # Use CPU by default, can be changed to 'cuda' for GPU
        )
        
        # Load image for OCR
        img = cv2.imread(tmp_path)
        if img is None:
            raise HTTPException(status_code=400, detail="Failed to load image")
        
        detected_zones = []
        
        # Process each detection
        for result in results:
            boxes = result.boxes
            for box in boxes:
                # Get bounding box coordinates
                x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
                conf = float(box.conf[0])
                cls = int(box.cls[0])
                
                # Crop the detected region
                x1_int, y1_int = int(x1), int(y1)
                x2_int, y2_int = int(x2), int(y2)
                
                # Ensure coordinates are within image bounds
                x1_int = max(0, x1_int)
                y1_int = max(0, y1_int)
                x2_int = min(img.shape[1], x2_int)
                y2_int = min(img.shape[0], y2_int)
                
                if x2_int <= x1_int or y2_int <= y1_int:
                    continue
                
                cropped = img[y1_int:y2_int, x1_int:x2_int]
                
                # Save cropped region temporarily for OCR
                with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as crop_tmp:
                    cv2.imwrite(crop_tmp.name, cropped)
                    crop_tmp_path = crop_tmp.name
                
                try:
                    # Run OCR on the cropped region using predict
                    ocr_result = ocr.predict(crop_tmp_path)
                    
                    # Extract text from OCR result
                    text = ""
                    ocr_confidence = 0
                    if ocr_result and ocr_result[0]:
                        for detection in ocr_result[0]:
                            if detection and len(detection) >= 2:
                                detected_text = detection[1]
                                if isinstance(detected_text, (list, tuple)) and len(detected_text) >= 2:
                                    text += str(detected_text[0]) + " "
                                    ocr_confidence = max(ocr_confidence, float(detected_text[1]))
                                elif isinstance(detected_text, str):
                                    text += detected_text + " "
                    
                    text = text.strip()
                finally:
                    # Clean up temporary cropped file
                    if os.path.exists(crop_tmp_path):
                        os.unlink(crop_tmp_path)
                
                if text:  # Only add zones with detected text
                    detected_zones.append({
                        "text": text,
                        "confidence": float(ocr_confidence),
                        "yolo_confidence": conf,
                        "bbox": {
                            "x1": float(x1),
                            "y1": float(y1),
                            "x2": float(x2),
                            "y2": float(y2),
                            "width": float(x2 - x1),
                            "height": float(y2 - y1)
                        },
                        "category": result.names[cls] if hasattr(result, 'names') else "text",
                        "source": "yolo_paddleocr"
                    })
        
        logger.info(f"✅ YOLO: Detected {len(detected_zones)} text zones")
        
        return {
            "zones": detected_zones,
            "metadata": {
                "total_zones": len(detected_zones),
                "confidence_threshold": confidence,
                "model": "YOLOv11n + PaddleOCR",
                "use_obb": use_obb
            }
        }
        
    except Exception as e:
        logger.error(f"Error in YOLO detection: {e}")
        raise HTTPException(status_code=500, detail=str(e))
    finally:
        if tmp_path and os.path.exists(tmp_path):
            os.unlink(tmp_path)

@app.post("/ocr/process-with-lines")
async def process_with_lines(request: dict = Body(...)):
    """Process image with OCR and detect dimension lines"""
    try:
        # Extract data from request
        image_data = request.get('image', '')
        
        if not image_data:
            raise HTTPException(status_code=400, detail="No image data provided")
        
        # Decode base64 image
        import base64
        if image_data.startswith('data:image'):
            image_data = image_data.split(',')[1]
        
        image_bytes = base64.b64decode(image_data)
        
        # Save temporary image
        with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as tmp_file:
            tmp_file.write(image_bytes)
            tmp_path = tmp_file.name
        
        try:
            # Process image with OCR
            result = await process_image_async(tmp_path, mode="fast")
            
            if not result or not result.get('zones'):
                return {"zones": [], "lines": [], "dimension_lines": [], "message": "No text detected"}
            
            # Detect lines in the image
            detected_lines = detect_dimension_lines(tmp_path)
            
            # Find lines near dimensions
            dimension_lines = find_lines_near_dimensions(result['zones'], detected_lines)
            
            # Create overlay image with lines
            overlay_image = create_overlay_image(tmp_path, result['zones'], detected_lines, dimension_lines)
            
            # Convert overlay to base64
            overlay_base64 = None
            if overlay_image is not None:
                import base64
                _, buffer = cv2.imencode('.jpg', overlay_image)
                overlay_base64 = base64.b64encode(buffer).decode('utf-8')
            
            return {
                "zones": result['zones'],
                "lines": detected_lines,
                "dimension_lines": dimension_lines,
                "overlay_image": overlay_base64,
                "message": f"Found {len(result['zones'])} text zones, {len(detected_lines)} lines, {len(dimension_lines)} dimension-line pairs"
            }
                
        finally:
            # Clean up temporary file
            if os.path.exists(tmp_path):
                os.unlink(tmp_path)
                
    except Exception as e:
        logger.error(f"Error processing image with lines: {e}")
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/ocr/get-text-properties")
async def get_text_properties(request: dict = Body(...)):
    """Get original text properties for precise box fitting"""
    try:
        # Extract data from request
        image_data = request.get('image', '')
        bbox = request.get('bbox', {})
        
        if not image_data or not bbox:
            raise HTTPException(status_code=400, detail="Image data and bbox required")
        
        # Decode base64 image
        import base64
        if image_data.startswith('data:image'):
            image_data = image_data.split(',')[1]
        
        image_bytes = base64.b64decode(image_data)
        
        # Save temporary image
        with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as tmp_file:
            tmp_file.write(image_bytes)
            tmp_path = tmp_file.name
        
        try:
            # Process image with OCR
            result = await process_image_async(tmp_path, mode="fast")
            
            if not result or not result.get('zones'):
                return {"error": "No text detected"}
            
            # Find the zone that matches the bbox
            target_zone = None
            for zone in result['zones']:
                zone_bbox = zone.get('bbox', {})
                if (isinstance(zone_bbox, dict) and 
                    abs(zone_bbox.get('x1', 0) - bbox.get('x1', 0)) < 10 and
                    abs(zone_bbox.get('y1', 0) - bbox.get('y1', 0)) < 10):
                    target_zone = zone
                    break
            
            if not target_zone:
                return {"error": "Matching zone not found"}
            
            # Extract text properties
            text_properties = {
                "text": target_zone.get('text', ''),
                "confidence": target_zone.get('confidence', 0),
                "bbox": target_zone.get('bbox', {}),
                "orientation": target_zone.get('orientation', 0),
                "rotation": target_zone.get('rotation', 0),
                "width": target_zone.get('width', 0),
                "height": target_zone.get('height', 0)
            }
            
            return text_properties
                
        finally:
            # Clean up temporary file
            if os.path.exists(tmp_path):
                os.unlink(tmp_path)
                
    except Exception as e:
        logger.error(f"Error getting text properties: {e}")
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/ocr/process-baseline")
async def process_baseline(request: dict = Body(...)):
    """Process baseline to find text and fit precise box"""
    try:
        # Extract data from request
        image_data = request.get('image', '')
        baseline = request.get('baseline', {})
        x1 = baseline.get('x1', 0)
        y1 = baseline.get('y1', 0)
        x2 = baseline.get('x2', 0)
        y2 = baseline.get('y2', 0)
        
        if not image_data:
            raise HTTPException(status_code=400, detail="No image data provided")
        
        # Decode base64 image
        import base64
        if image_data.startswith('data:image'):
            image_data = image_data.split(',')[1]
        
        image_bytes = base64.b64decode(image_data)
        
        # Save temporary image
        with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as tmp_file:
            tmp_file.write(image_bytes)
            tmp_path = tmp_file.name
        
        try:
            # Process full image with OCR
            result = process_image(tmp_path, mode="fast")
            
            if not result or not result.get('zones'):
                return {"zones": [], "message": "No text detected"}
            
            # Find text zones that intersect with the baseline
            zones = result['zones']
            intersecting_zones = []
            
            for zone in zones:
                bbox = zone.get('bbox', {})
                if not bbox:
                    continue
                
                # Check if zone intersects with baseline
                if intersects_with_line(bbox, x1, y1, x2, y2):
                    # Calculate distance from zone center to baseline
                    zone_center_x = (bbox.get('x1', 0) + bbox.get('x2', 0)) / 2
                    zone_center_y = (bbox.get('y1', 0) + bbox.get('y2', 0)) / 2
                    distance = distance_point_to_line(zone_center_x, zone_center_y, x1, y1, x2, y2)
                    
                    intersecting_zones.append({
                        'zone': zone,
                        'distance': distance
                    })
            
            # Sort by distance to baseline (closest first)
            intersecting_zones.sort(key=lambda x: x['distance'])
            
            if intersecting_zones:
                # Return the closest zone
                closest = intersecting_zones[0]
                zone = closest['zone']
                zone_bbox = zone.get('bbox', {})
                
                # Ensure we return the zone with proper bbox coordinates
                if isinstance(zone_bbox, dict):
                    return {
                        "zone": {
                            "text": zone.get('text', ''),
                            "confidence": zone.get('confidence', 0),
                            "bbox": zone_bbox,
                            "x": zone_bbox.get('x1', 0),
                            "y": zone_bbox.get('y1', 0),
                            "width": zone_bbox.get('width', 0),
                            "height": zone_bbox.get('height', 0),
                            "tolerance_info": zone.get('tolerance_info', {})
                        },
                        "message": f"Found text along baseline (distance: {closest['distance']:.1f}px)",
                        "baseline": {"x1": x1, "y1": y1, "x2": x2, "y2": y2},
                        "found_zone": zone.get('text', ''),
                        "fitted": True
                    }
                else:
                    return {
                        "zone": zone,
                        "message": f"Found text along baseline (distance: {closest['distance']:.1f}px)",
                        "baseline": {"x1": x1, "y1": y1, "x2": x2, "y2": y2},
                        "found_zone": zone.get('text', ''),
                        "fitted": True
                    }
            else:
                return {
                    "zones": [],
                    "message": "No text found along baseline",
                    "baseline": {"x1": x1, "y1": y1, "x2": x2, "y2": y2}
                }
                
        finally:
            # Clean up temporary file
            if os.path.exists(tmp_path):
                os.unlink(tmp_path)
                
    except Exception as e:
        logger.error(f"Error processing baseline: {e}")
        raise HTTPException(status_code=500, detail=str(e))

def intersects_with_line(bbox, x1, y1, x2, y2):
    """Check if bounding box intersects with line"""
    bx1, by1 = bbox.get('x1', 0), bbox.get('y1', 0)
    bx2, by2 = bbox.get('x2', 0), bbox.get('y2', 0)
    
    # Check if any corner of the box is close to the line
    corners = [(bx1, by1), (bx2, by1), (bx2, by2), (bx1, by2)]
    
    for cx, cy in corners:
        distance = distance_point_to_line(cx, cy, x1, y1, x2, y2)
        if distance < 20:  # Within 20 pixels
            return True
    
    return False

def distance_point_to_line(px, py, x1, y1, x2, y2):
    """Calculate distance from point to line"""
    # Line vector
    line_dx = x2 - x1
    line_dy = y2 - y1
    
    # Point vector
    point_dx = px - x1
    point_dy = py - y1
    
    # Calculate distance using cross product
    if line_dx == 0 and line_dy == 0:
        # Line is a point
        return ((px - x1) ** 2 + (py - y1) ** 2) ** 0.5
    
    # Distance = |cross_product| / |line_vector|
    cross_product = abs(point_dx * line_dy - point_dy * line_dx)
    line_length = (line_dx ** 2 + line_dy ** 2) ** 0.5
    
    return cross_product / line_length

def detect_dimension_lines(image_path):
    """Detect lines in the image that could be dimension lines"""
    import cv2
    import numpy as np
    
    # Read image
    img = cv2.imread(image_path)
    if img is None:
        return []
    
    # Convert to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
    # Apply edge detection
    edges = cv2.Canny(gray, 50, 150, apertureSize=3)
    
    # Detect lines using HoughLinesP
    lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=50, minLineLength=30, maxLineGap=10)
    
    detected_lines = []
    if lines is not None:
        for line in lines:
            x1, y1, x2, y2 = line[0]
            length = ((x2 - x1) ** 2 + (y2 - y1) ** 2) ** 0.5
            
            # Filter out very short lines
            if length > 20:
                # Calculate angle
                angle = math.atan2(y2 - y1, x2 - x1) * 180 / math.pi
                if angle < 0:
                    angle += 180
                
                detected_lines.append({
                    'x1': float(x1),
                    'y1': float(y1),
                    'x2': float(x2),
                    'y2': float(y2),
                    'length': float(length),
                    'angle': float(angle)
                })
    
    return detected_lines

def find_lines_near_dimensions(zones, lines, max_distance=50):
    """Find lines that are close to dimension text"""
    dimension_lines = []
    
    for zone in zones:
        bbox = zone.get('bbox', {})
        if not bbox:
            continue
        
        # Get zone center
        zone_center_x = (bbox.get('x1', 0) + bbox.get('x2', 0)) / 2
        zone_center_y = (bbox.get('y1', 0) + bbox.get('y2', 0)) / 2
        
        # Find closest lines
        nearby_lines = []
        for line in lines:
            distance = distance_point_to_line(zone_center_x, zone_center_y, 
                                            line['x1'], line['y1'], line['x2'], line['y2'])
            
            if distance < max_distance:
                nearby_lines.append({
                    'line': line,
                    'distance': distance
                })
        
        # Sort by distance
        nearby_lines.sort(key=lambda x: x['distance'])
        
        if nearby_lines:
            dimension_lines.append({
                'zone': zone,
                'closest_line': nearby_lines[0]['line'],
                'distance': nearby_lines[0]['distance'],
                'all_nearby_lines': [nl['line'] for nl in nearby_lines[:3]]  # Top 3 closest
            })
    
    return dimension_lines

# Telegram Bot Integration
import requests
import json
from fastapi import Request
from fastapi.responses import FileResponse
from fastapi.staticfiles import StaticFiles

# Mount static files with error handling
static_dir = os.path.join(os.path.dirname(__file__), "static")
if os.path.exists(static_dir):
    app.mount("/static", StaticFiles(directory=static_dir), name="static")
    logger.info(f"Static files mounted from: {static_dir}")
else:
    logger.warning(f"Static directory not found: {static_dir}")
    # Create static directory if it doesn't exist
    try:
        os.makedirs(static_dir, exist_ok=True)
        app.mount("/static", StaticFiles(directory=static_dir), name="static")
        logger.info(f"Created and mounted static directory: {static_dir}")
    except Exception as e:
        logger.error(f"Failed to create static directory: {e}")

def send_telegram_message(message, chat_id=None):
    """Send message to Telegram"""
    bot_token = os.getenv('TELEGRAM_BOT_TOKEN')
    if not bot_token:
        return False
    
    target_chat_id = chat_id or os.getenv('TELEGRAM_CHAT_ID')
    if not target_chat_id:
        return False
    
    # Try multiple Telegram API endpoints for better connectivity
    api_urls = [
        "https://api.telegram.org",
        "https://api.telegram.org:443"
    ]
    
    for api_url in api_urls:
        try:
            url = f"{api_url}/bot{bot_token}/sendMessage"
            data = {
                'chat_id': target_chat_id,
                'text': message,
                'parse_mode': 'Markdown'
            }
            
            # Add timeout and retry logic
            session = requests.Session()
            session.timeout = 10
            
            response = session.post(url, data=data, timeout=10)
            if response.status_code == 200:
                logger.info(f"Successfully sent Telegram message via {api_url}")
                return True
                
        except Exception as e:
            logger.warning(f"Failed to send via {api_url}: {e}")
            continue
    
    logger.error("Failed to send Telegram message via all endpoints")
    return False

def send_telegram_photo(image_path, caption="", chat_id=None):
    """Send photo to Telegram"""
    bot_token = os.getenv('TELEGRAM_BOT_TOKEN')
    if not bot_token:
        return False
    
    target_chat_id = chat_id or os.getenv('TELEGRAM_CHAT_ID')
    if not target_chat_id:
        return False
    
    url = f"https://api.telegram.org/bot{bot_token}/sendPhoto"
    
    try:
        with open(image_path, 'rb') as photo:
            files = {'photo': photo}
            data = {
                'chat_id': target_chat_id,
                'caption': caption
            }
            response = requests.post(url, files=files, data=data)
            return response.status_code == 200
    except Exception as e:
        logger.error(f"Failed to send Telegram photo: {e}")
        return False

def send_telegram_document(file_path, caption="", chat_id=None):
    """Send document to Telegram"""
    bot_token = os.getenv('TELEGRAM_BOT_TOKEN')
    if not bot_token:
        return False
    
    target_chat_id = chat_id or os.getenv('TELEGRAM_CHAT_ID')
    if not target_chat_id:
        return False
    
    url = f"https://api.telegram.org/bot{bot_token}/sendDocument"
    
    try:
        with open(file_path, 'rb') as document:
            files = {'document': document}
            data = {
                'chat_id': target_chat_id,
                'caption': caption
            }
            response = requests.post(url, files=files, data=data)
            return response.status_code == 200
    except Exception as e:
        logger.error(f"Failed to send Telegram document: {e}")
        return False

def send_correction_to_telegram(correction_data):
    """Send correction data to Telegram channel"""
    try:
        # Create message
        message = f"""📊 New Training Data Received

🆔 Image ID: {correction_data.get('image_id', 'unknown')}
👤 User: {correction_data.get('user_id', 'unknown')}
⏰ Time: {correction_data.get('timestamp', 'unknown')}

📈 Statistics:
• Original zones: {len(correction_data.get('original_zones', []))}
• Corrected zones: {len(correction_data.get('corrected_zones', []))}
• Text fixed: {sum(1 for zone in correction_data.get('corrected_zones', []) if zone.get('correction_type') == 'text_fixed')}
• Boxes moved: {sum(1 for zone in correction_data.get('corrected_zones', []) if zone.get('correction_type') == 'box_moved')}
• New zones added: {sum(1 for zone in correction_data.get('corrected_zones', []) if zone.get('correction_type') == 'new_zone')}
• Zones deleted: {sum(1 for zone in correction_data.get('corrected_zones', []) if zone.get('correction_type') == 'deleted')}
• Validated (OK): {sum(1 for zone in correction_data.get('corrected_zones', []) if zone.get('correction_type') == 'validated')}

✅ Data saved for model training"""

        # Send message
        send_telegram_message(message)
        
        # Create JSON file with training data
        import tempfile
        with tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as f:
            json.dump(correction_data, f, indent=2)
            json_path = f.name
        
        # Send JSON file
        send_telegram_document(json_path, "Training data JSON")
        
        # Clean up
        os.unlink(json_path)
        
        return True
    except Exception as e:
        logger.error(f"Failed to send correction to Telegram: {e}")
        return False

@app.get("/telegram/status")
async def telegram_status():
    """Check Telegram bot configuration"""
    bot_token = os.getenv('TELEGRAM_BOT_TOKEN')
    chat_id = os.getenv('TELEGRAM_CHAT_ID')
    
    return {
        "configured": bool(bot_token and chat_id),
        "bot_token_set": bool(bot_token),
        "chat_id_set": bool(chat_id),
        "connection": "ok" if (bot_token and chat_id) else "missing_config",
        "bot_username": "unknown"  # Would need to fetch from Telegram API
    }

@app.post("/telegram/set-webhook")
async def set_telegram_webhook(webhook_url: str = Query(...)):
    """Set Telegram webhook URL"""
    bot_token = os.getenv('TELEGRAM_BOT_TOKEN')
    if not bot_token:
        return {"success": False, "message": "Bot token not configured"}
    
    url = f"https://api.telegram.org/bot{bot_token}/setWebhook"
    data = {"url": webhook_url}
    
    try:
        response = requests.post(url, data=data)
        result = response.json()
        return {
            "success": result.get("ok", False),
            "message": result.get("description", "Unknown error"),
            "webhook_url": webhook_url,
            "telegram_response": result
        }
    except Exception as e:
        return {"success": False, "message": f"Error setting webhook: {str(e)}"}

@app.post("/telegram/webhook")
async def telegram_webhook(request: Request):
    """Handle incoming Telegram messages"""
    try:
        data = await request.json()
        logger.info(f"Received Telegram webhook: {data}")
        
        # Extract message info
        message = data.get('message', {})
        chat_id = message.get('chat', {}).get('id')
        user_id = message.get('from', {}).get('id')
        text = message.get('text', '')
        photo = message.get('photo', [])
        
        # Handle text commands
        if text:
            if text.startswith('/start'):
                # Log the command but don't send response due to DNS issues
                logger.info(f"User {user_id} sent /start command")
                # Just return success - user can access mini app directly
                pass
            
            elif text.startswith('/help'):
                logger.info(f"User {user_id} requested help")
                # Just return success
                pass
            
            elif text.startswith('/status'):
                logger.info(f"User {user_id} checked status")
                # Just return success
                pass
        
        # Handle photo uploads
        elif photo:
            # Get the largest photo
            largest_photo = max(photo, key=lambda x: x.get('file_size', 0))
            file_id = largest_photo.get('file_id')
            
            # Download photo
            photo_path = await download_telegram_photo(file_id)
            if photo_path:
                try:
                    # Process with OCR
                    result = process_image(photo_path, mode="fast")
                    
                    if result and result.get('zones'):
                        zones = result['zones']
                        logger.info(f"Processed photo for user {user_id}: Found {len(zones)} zones")
                        
                        # Log the results instead of sending message
                        for i, zone in enumerate(zones[:5]):
                            logger.info(f"Zone {i+1}: {zone.get('text', '')} ({int(zone.get('confidence', 0)*100)}%)")
                        
                        if len(zones) > 5:
                            logger.info(f"... and {len(zones)-5} more zones")
                    else:
                        logger.info(f"No text detected in photo from user {user_id}")
                    
                except Exception as ocr_error:
                    logger.error(f"OCR processing error for user {user_id}: {ocr_error}")
                finally:
                    # Clean up
                    if os.path.exists(photo_path):
                        os.unlink(photo_path)
        
        return {"status": "ok"}
        
    except Exception as e:
        logger.error(f"Error handling Telegram webhook: {e}")
        return {"status": "error", "message": str(e)}

def send_telegram_message_with_keyboard(message, chat_id, keyboard):
    """Send message with inline keyboard"""
    bot_token = os.getenv('TELEGRAM_BOT_TOKEN')
    if not bot_token:
        return False
    
    # Try multiple Telegram API endpoints for better connectivity
    api_urls = [
        "https://api.telegram.org",
        "https://api.telegram.org:443"
    ]
    
    for api_url in api_urls:
        try:
            url = f"{api_url}/bot{bot_token}/sendMessage"
            data = {
                'chat_id': chat_id,
                'text': message,
                'parse_mode': 'Markdown',
                'reply_markup': json.dumps(keyboard)
            }
            
            # Add timeout and retry logic
            session = requests.Session()
            session.timeout = 10
            
            response = session.post(url, data=data, timeout=10)
            if response.status_code == 200:
                logger.info(f"Successfully sent Telegram message with keyboard via {api_url}")
                return True
                
        except Exception as e:
            logger.warning(f"Failed to send keyboard message via {api_url}: {e}")
            continue
    
    logger.error("Failed to send Telegram message with keyboard via all endpoints")
    return False

async def download_telegram_photo(file_id):
    """Download photo from Telegram"""
    bot_token = os.getenv('TELEGRAM_BOT_TOKEN')
    if not bot_token:
        return None
    
    try:
        # Get file info
        url = f"https://api.telegram.org/bot{bot_token}/getFile"
        response = requests.get(url, params={'file_id': file_id})
        file_info = response.json()
        
        if not file_info.get('ok'):
            return None
        
        file_path = file_info['result']['file_path']
        
        # Download file
        download_url = f"https://api.telegram.org/file/bot{bot_token}/{file_path}"
        response = requests.get(download_url)
        
        # Save to temporary file
        with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as tmp_file:
            tmp_file.write(response.content)
            return tmp_file.name
            
    except Exception as e:
        logger.error(f"Failed to download Telegram photo: {e}")
        return None

@app.get("/camera")
async def camera_mini_app():
    """Serve the camera mini app"""
    return FileResponse("static/camera.html")

@app.post("/blueprint/quick-validate")
async def quick_validate_blueprint(file: UploadFile = File(...)):
    """
    Validation rapide d'un plan - retourne juste les statistiques
    Utile pour un aperçu rapide avant traitement complet
    """
    with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as temp_file:
        content = await file.read()
        temp_file.write(content)
        temp_path = temp_file.name
    
    try:
        # OCR rapide
        result = process_image(temp_path, mode="fast")
        zones = result.get('zones', [])
        
        # Statistiques rapides
        stats = {
            'total_zones': len(zones),
            'dimension_count': sum(1 for z in zones if z.get('is_dimension')),
            'with_tolerance': sum(1 for z in zones if z.get('tolerance_info')),
            'avg_confidence': sum(z.get('confidence', 0) for z in zones) / len(zones) if zones else 0,
            'low_confidence_zones': sum(1 for z in zones if z.get('confidence', 0) < 0.7),
            'recommendation': ''
        }
        
        # Recommandation
        if stats['avg_confidence'] < 0.7:
            stats['recommendation'] = "Qualité d'image faible - essayez le mode 'quality_control'"
        elif stats['dimension_count'] < 5:
            stats['recommendation'] = "Peu de dimensions détectées - vérifiez l'image"
        else:
            stats['recommendation'] = "Qualité acceptable - traitement complet recommandé"
        
        os.unlink(temp_path)
        
        return JSONResponse(content=stats)
        
    except Exception as e:
        if os.path.exists(temp_path):
            os.unlink(temp_path)
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/blueprint/stats")
async def get_blueprint_stats():
    """Obtenir des statistiques sur le système OCR"""
    return {
        "system_status": "operational",
        "blueprint_processor_available": True,
        "features": [
            "Enhanced dimension detection",
            "Tolerance parsing",
            "Quality analysis",
            "Smart zone merging",
            "Advanced text cleaning",
            "Thread specification support"
        ],
        "supported_modes": ["fast", "accurate", "hardcore"],
        "advanced_processing": {
            "smart_merging": merge_overlapping_zones,
            "duplicate_removal": remove_duplicate_zones,
            "text_cleaning": True,
            "orientation_detection": True
        }
    }

@app.post("/export/pdf")
async def export_pdf_report(
    image: UploadFile = File(...),
    zones: str = Body(...),
    title: str = Body("OCR Measurement Report"),
    part_number: str = Body("Part 1")
):
    """Export PDF report with image, bubbles, and tolerance grid"""
    try:
        # Check if PDF service is available
        if not PDF_AVAILABLE or not pdf_service:
            raise HTTPException(
                status_code=503, 
                detail="PDF export service not available. Please ensure reportlab is installed."
            )
        
        # Validate image
        if not image.content_type or not image.content_type.startswith('image/'):
            raise HTTPException(status_code=400, detail="Invalid image file")
        
        # Parse zones data
        import json
        try:
            zones_data = json.loads(zones)
            logger.info(f"PDF Export: Parsed {len(zones_data)} zones")
            
            # Debug: Check for None zones
            none_zones = [i for i, zone in enumerate(zones_data) if zone is None]
            if none_zones:
                logger.error(f"PDF Export: Found None zones at indices: {none_zones}")
                # Filter out None zones
                zones_data = [zone for zone in zones_data if zone is not None]
                logger.info(f"PDF Export: Filtered to {len(zones_data)} valid zones")
                
        except json.JSONDecodeError:
            raise HTTPException(status_code=400, detail="Invalid zones data format")
        
        # Read image data
        image_data = await image.read()
        
        # Generate PDF
        if not pdf_service:
            raise HTTPException(
                status_code=503, 
                detail="PDF service not initialized"
            )
        
        pdf_bytes = pdf_service.generate_pdf_report(
            image_data=image_data,
            zones=zones_data,
            title=title,
            part_number=part_number
        )
        
        # Return PDF as response
        return Response(
            content=pdf_bytes,
            media_type="application/pdf",
            headers={
                "Content-Disposition": f"attachment; filename={part_number}_measurement_report.pdf"
            }
        )
        
    except Exception as e:
        logger.error(f"PDF export error: {e}")
        raise HTTPException(status_code=500, detail=f"PDF generation failed: {str(e)}")

def create_measurement_sheet(ws, part_number, language, zones=None):
    """Create a measurement sheet for the specified language"""
    
    # Language-specific translations
    translations = {
        "en": {
            "title": f"Measurement Grid - {part_number}",
            "date": "Date: _______________",
            "operator": "Operator: _______________",
            "annotations": "Annotations",
            "part": "Part",
            "headers": ['#', 'Value Name', 'Min Tol', 'Max Tol', 'Mid Value', '1', '2', '3', '4', '5', '6']
        },
        "fr": {
            "title": f"Grille de Mesure - {part_number}",
            "date": "Date: _______________",
            "operator": "Opérateur: _______________",
            "annotations": "Annotations",
            "part": "Pièce",
            "headers": ['#', 'Nom Valeur', 'Min Tol', 'Max Tol', 'Valeur Mil', '1', '2', '3', '4', '5', '6']
        }
    }
    
    trans = translations[language]
    
    # Set column widths - new layout with more parts
    ws.column_dimensions['A'].width = 4   # #
    ws.column_dimensions['B'].width = 15  # Value name (more space)
    ws.column_dimensions['C'].width = 8   # Min tolerance
    ws.column_dimensions['D'].width = 8   # Max tolerance
    ws.column_dimensions['E'].width = 10  # Mid value to check
    # Parts columns - more space for handwriting
    ws.column_dimensions['F'].width = 15  # Part 1
    ws.column_dimensions['G'].width = 15  # Part 2
    ws.column_dimensions['H'].width = 15  # Part 3
    ws.column_dimensions['I'].width = 15  # Part 4
    ws.column_dimensions['J'].width = 15  # Part 5
    ws.column_dimensions['K'].width = 15  # Part 6
    
    # Define styles
    header_fill = PatternFill(start_color="0088FF", end_color="0088FF", fill_type="solid")
    header_font = Font(bold=True, color="FFFFFF", size=12)
    border = Border(
        left=Side(style='thin'),
        right=Side(style='thin'),
        top=Side(style='thin'),
        bottom=Side(style='thin')
    )
    
    # Add title
    ws['A1'] = trans["title"]
    ws['A1'].font = Font(bold=True, size=14)
    ws.merge_cells('A1:K1')
    
    # Add date and operator fields for template
    ws['A2'] = trans["date"]
    ws['A2'].font = Font(size=11)
    ws.merge_cells('A2:C2')
    
    ws['D2'] = trans["operator"]
    ws['D2'].font = Font(size=11)
    ws.merge_cells('D2:F2')
    
    # Add grid headers - Annotations and Parts
    ws['A3'] = trans["annotations"]
    ws['A3'].font = Font(bold=True, size=10)
    ws.merge_cells('A3:E3')
    
    ws['F3'] = "Parts"
    ws['F3'].font = Font(bold=True, size=10)
    ws.merge_cells('F3:K3')
    
    # Add compact headers
    headers = trans["headers"]
    for col, header in enumerate(headers, start=1):
        cell = ws.cell(row=4, column=col, value=header)
        cell.fill = header_fill
        cell.font = Font(bold=True, color="FFFFFF", size=9)  # Smaller font
        cell.alignment = Alignment(horizontal='center', vertical='center')
        cell.border = border
    
    # Add data rows - populate with extracted features if available
    row = 5
    if zones and len(zones) > 0:
        # Populate with ALL extracted zones (no limit)
        for idx, zone in enumerate(zones, start=1):  # Export ALL zones
            if zone is None:
                continue
            
            # Extract zone data safely
            text = zone.get('text', '') if isinstance(zone, dict) else ''
            tolerance_info = zone.get('tolerance_info', {}) if isinstance(zone, dict) else {}
            
            # Ensure tolerance_info is a dict
            if not isinstance(tolerance_info, dict):
                tolerance_info = {}
            
            # Extract tolerance data
            min_tol = tolerance_info.get('min_tolerance', '')
            max_tol = tolerance_info.get('max_tolerance', '')
            middle_value = tolerance_info.get('middle_value', '')
            
            # Write row with extracted data
            ws.cell(row=row, column=1, value=idx).border = border  # #
            ws.cell(row=row, column=2, value=text).border = border  # Value Name
            ws.cell(row=row, column=3, value=min_tol).border = border  # Min Tol
            ws.cell(row=row, column=4, value=max_tol).border = border  # Max Tol
            ws.cell(row=row, column=5, value=middle_value).border = border  # Mid Value
            # Parts columns - empty for operators to fill
            ws.cell(row=row, column=6, value='').border = border   # Part 1
            ws.cell(row=row, column=7, value='').border = border   # Part 2
            ws.cell(row=row, column=8, value='').border = border   # Part 3
            ws.cell(row=row, column=9, value='').border = border   # Part 4
            ws.cell(row=row, column=10, value='').border = border  # Part 5
            ws.cell(row=row, column=11, value='').border = border  # Part 6
            
            row += 1
    else:
        # Create empty template rows if no zones provided
        for idx in range(1, 21):
            ws.cell(row=row, column=1, value=idx).border = border  # #
            ws.cell(row=row, column=2, value='').border = border   # Value Name
            ws.cell(row=row, column=3, value='').border = border   # Min Tol
            ws.cell(row=row, column=4, value='').border = border   # Max Tol
            ws.cell(row=row, column=5, value='').border = border   # Mid Value
            # Parts columns - empty for operators to fill
            ws.cell(row=row, column=6, value='').border = border   # Part 1
            ws.cell(row=row, column=7, value='').border = border   # Part 2
            ws.cell(row=row, column=8, value='').border = border   # Part 3
            ws.cell(row=row, column=9, value='').border = border   # Part 4
            ws.cell(row=row, column=10, value='').border = border  # Part 5
            ws.cell(row=row, column=11, value='').border = border  # Part 6
            
            row += 1

@app.post("/export/excel")
async def export_excel_report(request: Request):
    """Export Excel report with tolerance data"""
    try:
        # Check if openpyxl is available
        if not OPENPYXL_AVAILABLE:
            raise HTTPException(
                status_code=503, 
                detail="Excel export service not available. Please ensure openpyxl is installed."
            )
        
        # Parse request data
        import json
        data = await request.json()
        zones = data.get('zones', [])
        title = data.get('title', 'OCR Measurement Report')
        part_number = data.get('part_number', 'Part 1')
        
        logger.info(f"Excel Export: Processing {len(zones)} zones for {part_number}")
        
        # Create workbook with two sheets
        wb = Workbook()
        
        # English sheet
        ws_en = wb.active
        ws_en.title = "Measurements_EN"
        
        # French sheet
        ws_fr = wb.create_sheet("Measurements_FR")
        
        # Process both sheets
        for ws, lang in [(ws_en, "en"), (ws_fr, "fr")]:
            create_measurement_sheet(ws, part_number, lang, zones)
        
        # Save to bytes
        from io import BytesIO
        excel_buffer = BytesIO()
        wb.save(excel_buffer)
        excel_bytes = excel_buffer.getvalue()
        
        logger.info(f"Excel Export: Generated dual-language template with {len(excel_bytes)} bytes")
        
        # Return Excel as response
        return Response(
            content=excel_bytes,
            media_type="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
            headers={
                "Content-Disposition": f"attachment; filename={part_number}_measurement_template_bilingual.xlsx"
            }
        )
        
    except Exception as e:
        logger.error(f"Excel export error: {e}")
        raise HTTPException(status_code=500, detail=f"Excel generation failed: {str(e)}")

@app.post("/training/save-zone")
async def save_training_zone(request: Request):
    """
    Save validated zone as training data for custom YOLOv11 model
    Collects: thumbnail, text, bbox, category, rotation
    ALSO saves full image with bbox for proper detection training
    """
    try:
        data = await request.json()
        
        # Extract zone data
        zone_id = data.get('zone_id')
        image_data = data.get('image')  # Base64 cropped image
        full_image_data = data.get('full_image')  # Base64 full image (NEW!)
        text = data.get('text', '')  # Allow empty text
        bbox = data.get('bbox')
        category = data.get('category', 'measure')  # Default to measure
        rotation = data.get('rotation', 0)
        confidence = data.get('confidence', 0)
        tolerance_info = data.get('tolerance_info')  # Include tolerance data
        
        # Validate required fields with better error messages
        missing_fields = []
        if not zone_id:
            missing_fields.append('zone_id')
        if not image_data:
            missing_fields.append('image')
        if not bbox or not isinstance(bbox, dict):
            missing_fields.append('bbox')
        
        if missing_fields:
            logger.error(f"Missing required fields for zone {zone_id}: {missing_fields}")
            raise HTTPException(status_code=400, detail=f"Missing required fields: {', '.join(missing_fields)}")
        
        # Skip zones with no text (likely false detections)
        if not text or text.strip() == '' or text == '[No Text]':
            logger.warning(f"Skipping zone {zone_id} - no valid text detected")
            return {"status": "skipped", "reason": "no_text", "zone_id": zone_id}
        
        # Create training data directory structure
        training_dir = os.path.join(base_dir, 'training_data')
        images_dir = os.path.join(training_dir, 'images', category)
        labels_dir = os.path.join(training_dir, 'labels', category)
        metadata_dir = os.path.join(training_dir, 'metadata')
        
        # NEW: Full images for detection training
        full_images_dir = os.path.join(training_dir, 'full_images')
        full_labels_dir = os.path.join(training_dir, 'full_labels')
        
        os.makedirs(images_dir, exist_ok=True)
        os.makedirs(labels_dir, exist_ok=True)
        os.makedirs(metadata_dir, exist_ok=True)
        os.makedirs(full_images_dir, exist_ok=True)
        os.makedirs(full_labels_dir, exist_ok=True)
        
        # Generate unique filename
        timestamp = int(time.time() * 1000)
        filename = f"{category}_{zone_id}_{timestamp}"
        
        # Save cropped image (for classification)
        import base64
        if image_data.startswith('data:image'):
            image_data = image_data.split(',')[1]
        
        image_bytes = base64.b64decode(image_data)
        image_path = os.path.join(images_dir, f"{filename}.jpg")
        
        with open(image_path, 'wb') as f:
            f.write(image_bytes)
        
        # Category mapping for YOLO
        category_mapping = {
            'measure': 0,
            'diameter': 1,
            'radius': 2,
            'tolerance': 3,
            'thread': 4,
            'material': 5,
            'gdt': 6,
            'note': 7,
            'surface_roughness': 8,
            'title_block': 9
        }
        
        class_id = category_mapping.get(category.lower(), 0)  # Default to measure
        
        # Save full image with YOLO label (NEW for detection training)
        if full_image_data:
            try:
                # Decode and save full image
                if full_image_data.startswith('data:image'):
                    full_image_data = full_image_data.split(',')[1]
                
                full_image_bytes = base64.b64decode(full_image_data)
                full_image_path = os.path.join(full_images_dir, f"{filename}.jpg")
                
                with open(full_image_path, 'wb') as f:
                    f.write(full_image_bytes)
                
                # Get image dimensions to normalize bbox
                from PIL import Image
                import io
                img = Image.open(io.BytesIO(full_image_bytes))
                img_width, img_height = img.size
                
                # Calculate normalized YOLO format bbox
                x1, y1 = bbox['x1'], bbox['y1']
                x2, y2 = bbox['x2'], bbox['y2']
                
                center_x = (x1 + x2) / 2 / img_width
                center_y = (y1 + y2) / 2 / img_height
                width = (x2 - x1) / img_width
                height = (y2 - y1) / img_height
                
                # Clamp to [0, 1]
                center_x = max(0, min(1, center_x))
                center_y = max(0, min(1, center_y))
                width = max(0, min(1, width))
                height = max(0, min(1, height))
                
                # Save YOLO format label
                label_path = os.path.join(full_labels_dir, f"{filename}.txt")
                with open(label_path, 'w') as f:
                    f.write(f"{class_id} {center_x:.6f} {center_y:.6f} {width:.6f} {height:.6f}\n")
                
                logger.info(f"💾 Saved full image + YOLO label for detection training")
                
            except Exception as e:
                logger.warning(f"Could not save full image for detection training: {e}")
        
        # Save metadata JSON (for reference and Donut training)
        metadata = {
            'zone_id': zone_id,
            'text': text,
            'bbox': bbox,
            'category': category,
            'rotation': rotation,
            'confidence': confidence,
            'ocr_confidence': confidence,
            'timestamp': timestamp,
            'image_path': image_path,
            'tolerance_info': tolerance_info,  # Include tolerance data for training
            'user_edited': False  # Flag to prevent auto-correction of user-edited values
        }
        
        metadata_path = os.path.join(metadata_dir, f"{filename}.json")
        import json
        with open(metadata_path, 'w', encoding='utf-8') as f:
            json.dump(metadata, f, indent=2, ensure_ascii=False)
        
        logger.info(f"💾 Saved training data: {filename} (category: {category})")
        
        return {
            "success": True,
            "message": f"Training data saved: {category}/{filename}",
            "image_path": image_path,
            "metadata_path": metadata_path
        }
        
    except Exception as e:
        logger.error(f"Error saving training data: {e}")
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/corrections/submit")
async def submit_corrections(request: Request):
    """Submit corrections for training data"""
    try:
        data = await request.json()
        
        # Send to Telegram channel
        success = send_correction_to_telegram(data)
        
        return {
            "status": "success" if success else "error",
            "message": "Corrections submitted successfully" if success else "Failed to submit corrections",
            "total_zones": len(data.get('corrected_zones', []))
        }
        
    except Exception as e:
        logger.error(f"Error submitting corrections: {e}")
        return {"status": "error", "message": str(e)}


# ============================================================
# TRAINING DATA MANAGEMENT ENDPOINTS
# ============================================================

@app.get("/training-data/list")
async def list_training_data(
    category: str = Query(None, description="Filter by category")
):
    """List all training data samples with metadata - no pagination, frontend handles it"""
    try:
        metadata_dir = os.path.join(base_dir, 'training_data', 'metadata')
        
        if not os.path.exists(metadata_dir):
            return {"samples": [], "total": 0}
        
        # Get all JSON files
        json_files = [f for f in os.listdir(metadata_dir) if f.endswith('.json')]
        
        samples = []
        for json_file in json_files:
            try:
                json_path = os.path.join(metadata_dir, json_file)
                with open(json_path, 'r', encoding='utf-8') as f:
                    metadata = json.load(f)
                
                # Filter by category if specified
                if category and metadata.get('category') != category:
                    continue
                
                # Add file reference
                metadata['metadata_file'] = json_file
                metadata['id'] = os.path.splitext(json_file)[0]
                
                samples.append(metadata)
            except Exception as e:
                logger.error(f"Error reading {json_file}: {e}")
                continue
        
        # Sort by timestamp (newest first)
        samples.sort(key=lambda x: x.get('timestamp', 0), reverse=True)
        
        return {
            "samples": samples,
            "total": len(samples)
        }
        
    except Exception as e:
        logger.error(f"Error listing training data: {e}")
        raise HTTPException(status_code=500, detail=str(e))


@app.get("/training-data/{sample_id}")
async def get_training_sample(sample_id: str):
    """Get a single training sample with full details"""
    try:
        metadata_dir = os.path.join(base_dir, 'training_data', 'metadata')
        json_path = os.path.join(metadata_dir, f"{sample_id}.json")
        
        if not os.path.exists(json_path):
            raise HTTPException(status_code=404, detail="Sample not found")
        
        with open(json_path, 'r', encoding='utf-8') as f:
            metadata = json.load(f)
        
        metadata['id'] = sample_id
        metadata['metadata_file'] = f"{sample_id}.json"
        
        # Get image data
        category = metadata.get('category', 'measure')
        images_dir = os.path.join(base_dir, 'training_data', 'images', category)
        
        # Find the image file
        image_filename = None
        for ext in ['.jpg', '.png', '.jpeg']:
            possible_path = os.path.join(images_dir, f"{sample_id}{ext}")
            if os.path.exists(possible_path):
                image_filename = f"{sample_id}{ext}"
                # Read and encode image
                import base64
                with open(possible_path, 'rb') as img_file:
                    img_data = base64.b64encode(img_file.read()).decode('utf-8')
                    metadata['cropped_image_data'] = f"data:image/jpeg;base64,{img_data}"
                break
        
        return metadata
        
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error getting training sample: {e}")
        raise HTTPException(status_code=500, detail=str(e))


@app.put("/training-data/{sample_id}")
async def update_training_sample(sample_id: str, request: Request):
    """Update a training sample (category, text, bbox, etc.)"""
    try:
        data = await request.json()
        
        metadata_dir = os.path.join(base_dir, 'training_data', 'metadata')
        json_path = os.path.join(metadata_dir, f"{sample_id}.json")
        
        if not os.path.exists(json_path):
            raise HTTPException(status_code=404, detail="Sample not found")
        
        # Load existing metadata
        with open(json_path, 'r', encoding='utf-8') as f:
            metadata = json.load(f)
        
        old_category = metadata.get('category', 'measure')
        new_category = data.get('category', old_category)
        
        # Update metadata fields
        for key in ['text', 'category', 'bbox', 'confidence', 'rotation', 'tolerance_info']:
            if key in data:
                metadata[key] = data[key]
        
        # Mark as user-edited if text or category was changed
        if 'text' in data or 'category' in data:
            metadata['user_edited'] = True
        
        # Save updated metadata
        with open(json_path, 'w', encoding='utf-8') as f:
            json.dump(metadata, f, indent=2, ensure_ascii=False)
        
        # If category changed, move the image file
        if old_category != new_category:
            old_images_dir = os.path.join(base_dir, 'training_data', 'images', old_category)
            new_images_dir = os.path.join(base_dir, 'training_data', 'images', new_category)
            os.makedirs(new_images_dir, exist_ok=True)
            
            # Find and move the image
            for ext in ['.jpg', '.png', '.jpeg']:
                old_path = os.path.join(old_images_dir, f"{sample_id}{ext}")
                if os.path.exists(old_path):
                    new_path = os.path.join(new_images_dir, f"{sample_id}{ext}")
                    shutil.move(old_path, new_path)
                    logger.info(f"Moved image from {old_category} to {new_category}")
                    break
        
        return {
            "success": True,
            "message": "Sample updated successfully",
            "sample_id": sample_id
        }
        
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error updating training sample: {e}")
        raise HTTPException(status_code=500, detail=str(e))


@app.delete("/training-data/{sample_id}")
async def delete_training_sample(sample_id: str):
    """Delete a training sample (metadata + images)"""
    try:
        metadata_dir = os.path.join(base_dir, 'training_data', 'metadata')
        json_path = os.path.join(metadata_dir, f"{sample_id}.json")
        
        if not os.path.exists(json_path):
            raise HTTPException(status_code=404, detail="Sample not found")
        
        # Load metadata to get category
        with open(json_path, 'r', encoding='utf-8') as f:
            metadata = json.load(f)
        
        category = metadata.get('category', 'measure')
        
        # Delete metadata file
        os.remove(json_path)
        
        # Delete cropped image
        images_dir = os.path.join(base_dir, 'training_data', 'images', category)
        for ext in ['.jpg', '.png', '.jpeg']:
            img_path = os.path.join(images_dir, f"{sample_id}{ext}")
            if os.path.exists(img_path):
                os.remove(img_path)
                break
        
        # Delete full image and label if they exist
        full_images_dir = os.path.join(base_dir, 'training_data', 'full_images')
        full_labels_dir = os.path.join(base_dir, 'training_data', 'full_labels')
        
        for ext in ['.jpg', '.png', '.jpeg']:
            full_img_path = os.path.join(full_images_dir, f"{sample_id}{ext}")
            if os.path.exists(full_img_path):
                os.remove(full_img_path)
                break
        
        label_path = os.path.join(full_labels_dir, f"{sample_id}.txt")
        if os.path.exists(label_path):
            os.remove(label_path)
        
        logger.info(f"Deleted training sample: {sample_id}")
        
        return {
            "success": True,
            "message": "Sample deleted successfully",
            "sample_id": sample_id
        }
        
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error deleting training sample: {e}")
        raise HTTPException(status_code=500, detail=str(e))


@app.get("/training-data/stats")
async def get_training_stats():
    """Get statistics about training data"""
    try:
        metadata_dir = os.path.join(base_dir, 'training_data', 'metadata')
        
        if not os.path.exists(metadata_dir):
            return {"total": 0, "by_category": {}}
        
        json_files = [f for f in os.listdir(metadata_dir) if f.endswith('.json')]
        
        stats = {
            "total": 0,
            "by_category": {},
            "avg_confidence": 0,
            "with_tolerance": 0
        }
        
        total_confidence = 0
        
        for json_file in json_files:
            try:
                json_path = os.path.join(metadata_dir, json_file)
                with open(json_path, 'r', encoding='utf-8') as f:
                    metadata = json.load(f)
                
                stats["total"] += 1
                
                category = metadata.get('category', 'unknown')
                stats["by_category"][category] = stats["by_category"].get(category, 0) + 1
                
                confidence = metadata.get('confidence', 0)
                total_confidence += confidence
                
                if metadata.get('tolerance_info'):
                    stats["with_tolerance"] += 1
                    
            except Exception as e:
                logger.error(f"Error reading {json_file}: {e}")
                continue
        
        if stats["total"] > 0:
            stats["avg_confidence"] = total_confidence / stats["total"]
        
        return stats
        
    except Exception as e:
        logger.error(f"Error getting training stats: {e}")
        raise HTTPException(status_code=500, detail=str(e))


@app.post("/training-data/{sample_id}/validate")
async def validate_training_sample(sample_id: str):
    """Re-run OCR on a training sample to validate/correct the text - uses SAME method as main UI"""
    try:
        if ocr is None:
            raise HTTPException(status_code=503, detail="OCR service not initialized")
        
        metadata_dir = os.path.join(base_dir, 'training_data', 'metadata')
        json_path = os.path.join(metadata_dir, f"{sample_id}.json")
        
        if not os.path.exists(json_path):
            raise HTTPException(status_code=404, detail="Sample not found")
        
        # Load metadata
        with open(json_path, 'r', encoding='utf-8') as f:
            metadata = json.load(f)
        
        category = metadata.get('category', 'measure')
        bbox = metadata.get('bbox')
        user_edited = metadata.get('user_edited', False)
        
        if not bbox:
            raise HTTPException(status_code=400, detail="No bbox information in metadata")
        
        # Skip auto-correction for user-edited samples
        if user_edited:
            return {
                "success": True,
                "original_text": metadata.get('text', ''),
                "new_text": metadata.get('text', ''),
                "original_confidence": metadata.get('confidence', 0),
                "new_confidence": metadata.get('confidence', 0),
                "original_category": category,
                "suggested_category": category,
                "tolerance_info": metadata.get('tolerance_info', {}),
                "text_changed": False,
                "category_changed": False,
                "user_edited": True,
                "message": "Sample marked as user-edited, skipping auto-correction"
            }
        
        # Find the cropped image file
        images_dir = os.path.join(base_dir, 'training_data', 'images', category)
        image_path = None
        for ext in ['.jpg', '.png', '.jpeg']:
            possible_path = os.path.join(images_dir, f"{sample_id}{ext}")
            if os.path.exists(possible_path):
                image_path = possible_path
                break
        
        if not image_path:
            raise HTTPException(status_code=404, detail="Image file not found")
        
        # Load the cropped image
        import cv2
        img = cv2.imread(image_path)
        if img is None:
            raise HTTPException(status_code=500, detail="Failed to load image")
        
        # Log image info for debugging
        logger.info(f"Validation: Processing image {image_path}, size: {img.shape}")
        
        # Use the EXACT SAME method as main UI: process_image()
        try:
            # Process using the exact same function as main UI
            result = process_image(image_path, mode="accurate", rotation=0)
            logger.info(f"Validation: Processing {sample_id} with process_image(), found {len(result.get('zones', []))} zones")
            
            # Extract text from zones (same as UI)
            new_text = ""
            new_confidence = 0
            tolerance_info = {}
            
            zones = result.get('zones', [])
            if zones and len(zones) > 0:
                # Combine text from all zones
                for zone in zones:
                    if zone and zone.get('text'):
                        zone_text = zone.get('text', '')
                        zone_conf = zone.get('confidence', 0)
                        
                        new_text += zone_text + " "
                        new_confidence = max(new_confidence, zone_conf)
                
                # Get tolerance info from first zone if available
                if zones[0]:
                    tolerance_info = zones[0].get('tolerance_info', {})
            
            new_text = new_text.strip()
            
            # Detect category based on text (same as UI)
            detected_category = detect_zone_category(new_text)
        
        except Exception as e:
            logger.error(f"Error running OCR validation: {e}")
            # Set defaults if OCR fails
            new_text = ""
            new_confidence = 0
            tolerance_info = {}
            detected_category = "note"
        
        return {
            "success": True,
            "original_text": metadata.get('text', ''),
            "new_text": new_text,
            "original_confidence": metadata.get('confidence', 0),
            "new_confidence": new_confidence,
            "original_category": category,
            "suggested_category": detected_category,
            "tolerance_info": tolerance_info,
            "text_changed": new_text != metadata.get('text', ''),
            "category_changed": detected_category != category
        }
        
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error validating training sample: {e}")
        raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
    import uvicorn
    # Get port from environment variable (for Hugging Face Spaces) or default to 8000
    port = int(os.getenv("PORT", 8000))
    uvicorn.run(app, host="0.0.0.0", port=port)