app.py

import os
import io
import fitz  # PyMuPDF
from PIL import Image, ImageOps, ImageChops
from docx import Document
from rembg import remove
import gradio as gr
from hezar.models import Model
from ultralytics import YOLO
import json
import logging
import shutil

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

# ایجاد دایرکتوری‌های لازم
os.makedirs("static", exist_ok=True)
os.makedirs("output_images", exist_ok=True)

def remove_readonly(func, path, excinfo):
    os.chmod(path, stat.S_IWRITE)
    func(path)

current_dir = os.path.dirname(os.path.abspath(__file__))
ultralytics_path = os.path.join(current_dir, 'runs')

if os.path.exists(ultralytics_path):
    shutil.rmtree(ultralytics_path, onerror=remove_readonly)

def trim_whitespace(image):
    gray_image = ImageOps.grayscale(image)
    inverted_image = ImageChops.invert(gray_image)
    bbox = inverted_image.getbbox()
    trimmed_image = image.crop(bbox)
    return trimmed_image

def convert_pdf_to_images(pdf_path, zoom=2):
    pdf_document = fitz.open(pdf_path)
    images = []
    for page_num in range(len(pdf_document)):
        page = pdf_document.load_page(page_num)
        matrix = fitz.Matrix(zoom, zoom)
        pix = page.get_pixmap(matrix=matrix)
        image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
        trimmed_image = trim_whitespace(image)
        images.append(trimmed_image)
    logging.info(f"Converted PDF {pdf_path} to images.")
    return images

def convert_docx_to_jpeg(docx_bytes):
    document = Document(io.BytesIO(docx_bytes))
    images = []
    for rel in document.part.rels.values():
        if "image" in rel.target_ref:
            image_stream = rel.target_part.blob
            image = Image.open(io.BytesIO(image_stream))
            jpeg_image = io.BytesIO()
            image.convert('RGB').save(jpeg_image, format="JPEG")
            jpeg_image.seek(0)
            images.append(Image.open(jpeg_image))
    logging.info("Converted DOCX to images.")
    return images

def remove_background_from_image(image):
    result = remove(image)
    logging.info("Removed background from image.")
    return result

def process_file(input_file):
    file_extension = os.path.splitext(input_file.name)[1].lower()
    images = []

    if file_extension in ['.png', '.jpeg', '.jpg', '.bmp', '.gif']:
        image = Image.open(input_file)
        output_image = remove_background_from_image(image)
        images.append(output_image)
    elif file_extension == '.pdf':
        images = convert_pdf_to_images(input_file.name)
        images = [remove_background_from_image(image) for image in images]
    elif file_extension in ['.docx', '.doc']:
        images = convert_docx_to_jpeg(input_file.name)
        images = [remove_background_from_image(image) for image in images]
    else:
        logging.error("File format not supported.")
        return "File format not supported."

    input_folder = 'output_images'
    for i, img in enumerate(images):
        if img.mode == 'RGBA':
            img = img.convert('RGB')
        img.save(os.path.join(input_folder, f'image_{i}.jpg'))
    logging.info("Processed file and saved images.")
    return images

def run_detection_and_ocr():
    # Load models
    ocr_model = Model.load('hezarai/crnn-fa-printed-96-long')
    yolo_model_check = YOLO("best_300_D_check.pt")
    yolo_model_numbers = YOLO("P_D_T.pt")
    
    input_folder = 'output_images'
    yolo_model_check.predict(input_folder, save=True, conf=0.5, save_crop=True)
    logging.info("Ran YOLO detection for check model.")

    output_folder = 'runs/detect/predict'
    crop_folder = os.path.join(output_folder, 'crops')
    
    results = []
    
    for filename in os.listdir(input_folder):
        if filename.endswith('.JPEG') or filename.endswith('.jpg'):
            image_path = os.path.join(input_folder, filename)
            if os.path.exists(crop_folder):
                crops = []
                for crop_label in os.listdir(crop_folder):
                    crop_label_folder = os.path.join(crop_folder, crop_label)
                    if os.path.isdir(crop_label_folder):
                        for crop_filename in os.listdir(crop_label_folder):
                            crop_image_path = os.path.join(crop_label_folder, crop_filename)
                            if crop_label in ['mablagh_H', 'owner', 'vajh']:
                                text_prediction = predict_text(ocr_model, crop_image_path)
                            else:
                                text_prediction = process_numbers(yolo_model_numbers, crop_image_path)
                            crops.append({
                                'crop_image_path': crop_image_path,
                                'text_prediction': text_prediction,
                                'class_label': crop_label
                            })
                results.append({
                    'image': filename,
                    'crops': crops
                })
    logging.info("Processed detection and OCR.")
    output_json_path = 'output.json'
    with open(output_json_path, 'w', encoding='utf-8') as f:
        json.dump(results, f, ensure_ascii=False, indent=4)
    logging.info("Saved results to JSON.")
    return output_json_path

def predict_text(model, image_path):
    try:
        image = Image.open(image_path)
        image = image.resize((320, 320))
        output = model.predict(image)
        if isinstance(output, list):
            result = ' '.join([item['text'] for item in output])
            logging.info(f"Predicted text for {image_path}.")
            return result
        return str(output)
    except FileNotFoundError:
        logging.error(f"File not found: {image_path}.")
        return "N/A"

def process_numbers(model, image_path):
    label_map = {
        '-': '/',
        '0': '0',
        '1': '1',
        '2': '2',
        '3': '3',
        '4': '4',
        '4q': '4',
        '5': '5',
        '6': '6',
        '6q': '6',
        '7': '7',
        '8': '8',
        '9': '9'
    }
    results = model(image_path, conf=0.5, save_crop=False)
    detected_objects = []
    for result in results[0].boxes:
        class_id = int(result.cls[0].cpu().numpy())
        label = model.names[class_id]
        mapped_label = label_map.get(label, '')
        detected_objects.append({'bbox': result.xyxy[0].cpu().numpy().tolist(), 'label': mapped_label})
    sorted_objects = sorted(detected_objects, key=lambda x: x['bbox'][0])
    logging.info(f"Processed numbers for {image_path}.")
    return ''.join([obj['label'] for obj in sorted_objects])

def gradio_interface(input_file):
    process_file(input_file)
    json_output = run_detection_and_ocr()
    with open(json_output, 'r', encoding='utf-8') as f:
        data = json.load(f)
    logging.info("Generated JSON output for Gradio interface.")
    return data

iface = gr.Interface(
    fn=gradio_interface,
    inputs=gr.File(label="Upload Word, PDF, or Image"),
    outputs=gr.JSON(label="JSON Output"),
    title="Document to JSON Converter with Background Removal"
)

if __name__ == "__main__":
    logging.info("Starting Gradio interface.")
    iface.launch()