app.py

import gradio as gr
import time
from PIL import Image
from paddleocr import PaddleOCR
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
import pytesseract
import numpy as np

# Initialize models
paddle_ocr = PaddleOCR(lang='fa', use_textline_orientation=True)
trocr_processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-printed")
trocr_model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-printed")

def run_paddleocr(image):
    """Run PaddleOCR on image"""
    image_path = "temp.jpg"
    image.save(image_path)
    result = paddle_ocr.ocr(image_path, cls=True)
    text = ' '.join([line[1][0] for line in result[0]]) if result else ''
    return text

def run_trocr(image):
    """Run TrOCR on image"""
    pixel_values = trocr_processor(image, return_tensors="pt").pixel_values
    generated_ids = trocr_model.generate(pixel_values)
    return trocr_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

def run_tesseract(image):
    """Run Tesseract OCR on image"""
    return pytesseract.image_to_string(image, lang='fas')

def compare_models(image):
    """Compare all three OCR models"""
    # Convert to RGB if needed
    if isinstance(image, np.ndarray):
        image = Image.fromarray(image)
    image = image.convert("RGB")
    
    results = {}
    
    # Run PaddleOCR
    start = time.time()
    results['PaddleOCR'] = run_paddleocr(image)
    paddle_time = time.time() - start
    
    # Run TrOCR
    start = time.time()
    results['TrOCR'] = run_trocr(image)
    trocr_time = time.time() - start
    
    # Run Tesseract
    start = time.time()
    results['Tesseract'] = run_tesseract(image)
    tesseract_time = time.time() - start
    
    # Create comparison table
    comparison = f"""
    <table>
        <tr>
            <th>مدل</th>
            <th>متن استخراج شده</th>
            <th>زمان پردازش (ثانیه)</th>
        </tr>
        <tr>
            <td>PaddleOCR</td>
            <td>{results['PaddleOCR']}</td>
            <td>{paddle_time:.2f}</td>
        </tr>
        <tr>
            <td>TrOCR</td>
            <td>{results['TrOCR']}</td>
            <td>{trocr_time:.2f}</td>
        </tr>
        <tr>
            <td>Tesseract</td>
            <td>{results['Tesseract']}</td>
            <td>{tesseract_time:.2f}</td>
        </tr>
    </table>
    """
    
    return comparison, results['PaddleOCR'], results['TrOCR'], results['Tesseract']

# Create Gradio interface
with gr.Blocks(title="مقایسه مدل‌های OCR فارسی") as demo:
    gr.Markdown("""
    ## مقایسه عملکرد مدل‌های OCR برای زبان فارسی
    این برنامه سه مدل مختلف OCR را روی تصاویر فارسی مقایسه می‌کند:
    1. PaddleOCR
    2. TrOCR (مایکروسافت)
    3. Tesseract OCR
    """)
    
    with gr.Row():
        with gr.Column():
            image_input = gr.Image(label="تصویر ورودی", type="pil")
            submit_btn = gr.Button("مقایسه مدل‌ها")
        
        with gr.Column():
            comparison_output = gr.HTML(label="نتایج مقایسه")
            paddle_output = gr.Textbox(label="PaddleOCR")
            trocr_output = gr.Textbox(label="TrOCR")
            tesseract_output = gr.Textbox(label="Tesseract")
    
    submit_btn.click(
        fn=compare_models,
        inputs=image_input,
        outputs=[comparison_output, paddle_output, trocr_output, tesseract_output]
    )

if __name__ == "__main__":
    demo.launch()