Create app.py

app.py

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+import os
+from PIL import Image, ImageOps, ImageChops
+import io
+import fitz  # PyMuPDF
+from docx import Document
+from rembg import remove
+import gradio as gr
+from hezar.models import Model
+from ultralytics import YOLO
+import json
+
+# ایجاد دایرکتوری‌های لازم
+os.makedirs("static", exist_ok=True)
+os.makedirs("output_images", exist_ok=True)
+
+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)
+    return images
+
+def convert_docx_to_jpeg(docx_bytes):
+    document = Document(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(BytesIO(image_stream))
+            jpeg_image = BytesIO()
+            image.convert('RGB').save(jpeg_image, format="JPEG")
+            jpeg_image.seek(0)
+            images.append(Image.open(jpeg_image))
+    return images
+
+def remove_background_from_image(image):
+    return remove(image)
+
+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)
+        image = image.convert('RGB')
+        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:
+        return "File format not supported."
+
+    input_folder = 'output_images'
+    for i, img in enumerate(images):
+        img.save(os.path.join(input_folder, f'image_{i}.jpg'))
+
+    return images
+
+def run_detection_and_ocr():
+    # Load models
+    ocr_model = Model.load('hezarai/crnn-fa-printed-96-long')
+    yolo_model = YOLO("/content/drive/MyDrive/train3/weights/best.pt")
+    
+    input_folder = 'output_images'
+    yolo_model.predict(input_folder, save=True, imgsz=320, conf=0.5, save_crop=True)
+    
+    output_folder = '/content/runs/detect/predict'
+    results = []
+    
+    for filename in os.listdir(input_folder):
+        if filename.endswith('.JPEG') or filename.endswith('.jpg'):
+            image_path = os.path.join(input_folder, filename)
+            crop_folder = os.path.join(output_folder, 'crops')
+            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)
+                        text_prediction = predict_text(ocr_model, 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
+            })
+
+    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)
+    
+    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):
+            return ' '.join([item['text'] for item in output])
+        return str(output)
+    except FileNotFoundError:
+        return "N/A"
+
+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:
+        return json.load(f)
+
+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__":
+    iface.launch()