import numpy as np
import cv2
import re
import logging
from datetime import datetime
import os
from PIL import Image
from transformers import TrOCRProcessor, VisionEncoderDecoderModel

# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# Initialize TrOCR with error handling
try:
    processor = TrOCRProcessor.from_pretrained("microsoft/trocr-small-printed")
    model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-small-printed")
    logging.info("TrOCR model and processor loaded successfully")
except Exception as e:
    logging.error(f"Failed to load TrOCR model: {str(e)}")
    processor = None
    model = None

# Directory for debug images
DEBUG_DIR = "debug_images"
os.makedirs(DEBUG_DIR, exist_ok=True)

def save_debug_image(img, filename_suffix, prefix=""):
    """Save image to debug directory with timestamp."""
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
    filename = os.path.join(DEBUG_DIR, f"{prefix}{timestamp}_{filename_suffix}.png")
    if isinstance(img, Image.Image):
        img.save(filename)
    elif len(img.shape) == 3:
        cv2.imwrite(filename, cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    else:
        cv2.imwrite(filename, img)
    logging.info(f"Saved debug image: {filename}")

def estimate_brightness(img):
    """Estimate image brightness."""
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    return np.mean(gray)

def preprocess_image(img):
    """Preprocess image for OCR with enhanced contrast and noise reduction."""
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # Dynamic contrast adjustment based on brightness
    brightness = estimate_brightness(img)
    clahe_clip = 4.0 if brightness < 100 else 2.0
    clahe = cv2.createCLAHE(clipLimit=clahe_clip, tileGridSize=(8, 8))
    enhanced = clahe.apply(gray)
    save_debug_image(enhanced, "01_preprocess_clahe")
    # Gaussian blur to reduce noise
    blurred = cv2.GaussianBlur(enhanced, (3, 3), 0)
    save_debug_image(blurred, "02_preprocess_blur")
    # Adaptive thresholding for digit segmentation
    block_size = max(11, min(31, int(img.shape[0] / 20) * 2 + 1))
    thresh = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
                                   cv2.THRESH_BINARY_INV, block_size, 2)
    # Morphological operations to clean up digits
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
    thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)
    thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=1)
    save_debug_image(thresh, "03_preprocess_morph")
    return thresh, enhanced

def correct_rotation(img):
    """Correct image rotation using edge detection."""
    try:
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        edges = cv2.Canny(gray, 50, 150)
        lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=50, minLineLength=30, maxLineGap=10)
        if lines is not None:
            angles = [np.arctan2(line[0][3] - line[0][1], line[0][2] - line[0][0]) * 180 / np.pi for line in lines]
            angle = np.median(angles)
            if abs(angle) > 1.0:
                h, w = img.shape[:2]
                center = (w // 2, h // 2)
                M = cv2.getRotationMatrix2D(center, angle, 1.0)
                img = cv2.warpAffine(img, M, (w, h))
                save_debug_image(img, "00_rotated_image")
                logging.info(f"Applied rotation: {angle:.2f} degrees")
        return img
    except Exception as e:
        logging.error(f"Rotation correction failed: {str(e)}")
        return img

def detect_roi(img):
    """Detect region of interest (display) with refined contour filtering."""
    try:
        save_debug_image(img, "04_original")
        thresh, enhanced = preprocess_image(img)
        brightness_map = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        
        if contours:
            img_area = img.shape[0] * img.shape[1]
            valid_contours = []
            for c in contours:
                area = cv2.contourArea(c)
                x, y, w, h = cv2.boundingRect(c)
                roi_brightness = np.mean(brightness_map[y:y+h, x:x+w])
                aspect_ratio = w / h
                # Relaxed constraints for digital displays
                if (200 < area < (img_area * 0.8) and 
                    0.5 <= aspect_ratio <= 15.0 and w > 50 and h > 20 and roi_brightness > 30):
                    valid_contours.append((c, area * roi_brightness))
                    logging.debug(f"Contour: Area={area}, Aspect={aspect_ratio:.2f}, Brightness={roi_brightness:.2f}")
            
            if valid_contours:
                contour, _ = max(valid_contours, key=lambda x: x[1])
                x, y, w, h = cv2.boundingRect(contour)
                padding = max(15, min(50, int(min(w, h) * 0.3)))
                x, y = max(0, x - padding), max(0, y - padding)
                w, h = min(w + 2 * padding, img.shape[1] - x), min(h + 2 * padding, img.shape[0] - y)
                roi_img = img[y:y+h, x:x+w]
                save_debug_image(roi_img, "05_detected_roi")
                logging.info(f"Detected ROI: ({x}, {y}, {w}, {h})")
                return roi_img, (x, y, w, h)
        
        logging.info("No ROI found, using full image.")
        save_debug_image(img, "05_no_roi_fallback")
        return img, None
    except Exception as e:
        logging.error(f"ROI detection failed: {str(e)}")
        save_debug_image(img, "05_roi_error_fallback")
        return img, None

def perform_ocr(img):
    """Perform OCR using TrOCR for digital displays."""
    if processor is None or model is None:
        logging.error("TrOCR model not loaded, cannot perform OCR.")
        return None, 0.0
    try:
        # Convert to PIL for TrOCR
        pil_img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
        save_debug_image(pil_img, "06_ocr_input")
        # Process image with TrOCR
        pixel_values = processor(pil_img, return_tensors="pt").pixel_values
        generated_ids = model.generate(pixel_values, max_length=10)
        text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
        logging.info(f"TrOCR raw output: {text}")

        # Clean and validate text
        text = re.sub(r"[^\d\.]", "", text)
        if text.count('.') > 1:
            text = text.replace('.', '', text.count('.') - 1)
        text = text.strip('.')
        if text and re.fullmatch(r"^\d*\.?\d*$", text):
            text = text.lstrip('0') or '0'
            confidence = 95.0 if len(text.replace('.', '')) >= 2 else 85.0
            logging.info(f"Validated text: {text}, Confidence: {confidence:.2f}%")
            return text, confidence
        logging.info(f"Text '{text}' failed validation.")
        return None, 0.0
    except Exception as e:
        logging.error(f"OCR failed: {str(e)}")
        return None, 0.0

def extract_weight_from_image(pil_img):
    """Extract weight from a digital scale image."""
    try:
        img = np.array(pil_img)
        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
        save_debug_image(img, "00_input_image")
        img = correct_rotation(img)
        brightness = estimate_brightness(img)
        conf_threshold = 0.7 if brightness > 100 else 0.5

        roi_img, roi_bbox = detect_roi(img)
        if roi_bbox:
            conf_threshold *= 1.1 if (roi_bbox[2] * roi_bbox[3]) > (img.shape[0] * img.shape[1] * 0.3) else 1.0

        result, confidence = perform_ocr(roi_img)
        if result and confidence >= conf_threshold * 100:
            try:
                weight = float(result)
                if 0.01 <= weight <= 1000:  # Narrowed range for typical scale weights
                    logging.info(f"Detected weight: {result} kg, Confidence: {confidence:.2f}%")
                    return result, confidence
                logging.warning(f"Weight {result} out of range.")
            except ValueError:
                logging.warning(f"Invalid weight format: {result}")

        logging.info("Primary OCR failed, using full image fallback.")
        result, confidence = perform_ocr(img)
        if result and confidence >= conf_threshold * 0.9 * 100:
            try:
                weight = float(result)
                if 0.01 <= weight <= 1000:
                    logging.info(f"Full image weight: {result} kg, Confidence: {confidence:.2f}%")
                    return result, confidence
                logging.warning(f"Full image weight {result} out of range.")
            except ValueError:
                logging.warning(f"Invalid full image weight format: {result}")

        logging.info("No valid weight detected.")
        return "Not detected", 0.0
    except Exception as e:
        logging.error(f"Weight extraction failed: {str(e)}")
        return "Not detected", 0.0