import easyocr
import numpy as np
import cv2
import re

reader = easyocr.Reader(['en'], gpu=False)

def enhance_image(img):
    # Convert to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Apply sharpening kernel
    kernel = np.array([[0, -1, 0], [-1, 5,-1], [0, -1, 0]])
    sharp = cv2.filter2D(gray, -1, kernel)

    # Contrast Limited Adaptive Histogram Equalization (CLAHE)
    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
    contrast = clahe.apply(sharp)

    # Denoising
    denoised = cv2.fastNlMeansDenoising(contrast, h=30)

    # Adaptive threshold for very dim images
    thresh = cv2.adaptiveThreshold(denoised, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
                                   cv2.THRESH_BINARY, 11, 2)

    return thresh

def extract_weight_from_image(pil_img):
    try:
        img = np.array(pil_img)

        # Resize if too large or too small
        max_dim = 1000
        height, width = img.shape[:2]
        if max(height, width) > max_dim:
            scale = max_dim / max(height, width)
            img = cv2.resize(img, None, fx=scale, fy=scale, interpolation=cv2.INTER_AREA)
        elif max(height, width) < 400:
            scale = 2.5  # Upscale very small images
            img = cv2.resize(img, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)

        # Enhance image for OCR
        preprocessed = enhance_image(img)

        results = reader.readtext(preprocessed)

        best_weight = None
        best_conf = 0.0

        for item in results:
            if len(item) != 2 or not isinstance(item[1], tuple):
                continue
            text, conf = item[1]
            cleaned = text.lower().strip()

            cleaned = cleaned.replace(",", ".")
            cleaned = cleaned.replace("o", "0").replace("O", "0")
            cleaned = cleaned.replace("s", "5").replace("S", "5")
            cleaned = cleaned.replace("g", "9").replace("G", "6")
            cleaned = cleaned.replace("kg", "").replace("kgs", "")
            cleaned = re.sub(r"[^\d\.]", "", cleaned)

            if re.fullmatch(r"\d{2,4}(\.\d{1,3})?", cleaned):
                if conf > best_conf:
                    best_weight = cleaned
                    best_conf = conf

        if not best_weight:
            for item in results:
                if len(item) != 2 or not isinstance(item[1], tuple):
                    continue
                text, conf = item[1]
                fallback = re.sub(r"[^\d\.]", "", text)
                if fallback and fallback.replace(".", "").isdigit():
                    best_weight = fallback
                    best_conf = conf
                    break

        if not best_weight:
            return "Not detected", 0.0

        if "." in best_weight:
            int_part, dec_part = best_weight.split(".")
            int_part = int_part.lstrip("0") or "0"
            best_weight = f"{int_part}.{dec_part}"
        else:
            best_weight = best_weight.lstrip("0") or "0"

        return best_weight, round(best_conf * 100, 2)

    except Exception as e:
        return f"Error: {str(e)}", 0.0