utils/image_processor.py

import os
import cv2
import numpy as np
from PIL import Image
import torch
from transformers import TrOCRProcessor, VisionEncoderDecoderModel

# ✅ Load TrOCR model and processor once
try:
    processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
    model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
    model.eval()
    print("✅ TrOCR model loaded.")
    trocr_available = True
except Exception as e:
    print(f"❌ Failed to load TrOCR: {e}")
    trocr_available = False

def preprocess_image(image):
    """
    Preprocess image for OCR: convert to grayscale and enhance contrast.
    """
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    denoised = cv2.fastNlMeansDenoising(gray, h=10)
    processed = cv2.adaptiveThreshold(
        denoised, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
        cv2.THRESH_BINARY, 11, 2
    )
    return processed

def extract_text_from_image(image_path):
    """
    Extract handwritten text from an image using TrOCR
    """
    try:
        print(f"📂 Reading image from: {image_path}")  # ✅ log file path

        image = Image.open(image_path).convert("RGB")
        pixel_values = processor(images=image, return_tensors="pt").pixel_values

        generated_ids = trocr_model.generate(pixel_values)
        text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()

        print(f"📝 Extracted Text from {os.path.basename(image_path)}: {text}")  # ✅ PRINT EXTRACTED TEXT

        return text or "Text extraction failed."
    
    except Exception as e:
        print(f"❌ OCR failed on {image_path}: {str(e)}")
        return "Text extraction failed."