app.py

import gradio as gr
import numpy as np
import cv2
from PIL import Image
import torch
import torchvision.transforms as T
import os
import random

class TextErasingDemo:
    def __init__(self):
        # Initialize model components (placeholder for actual model loading)
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        
    def erase_text(self, image, method="self_supervised", strength=0.7):
        """
        Main function to erase text from images.
        This is a simplified implementation that simulates text erasing.
        """
        try:
            # Convert PIL to numpy for processing
            if isinstance(image, Image.Image):
                img_np = np.array(image)
            else:
                img_np = image.copy()
            
            # Get image dimensions
            h, w = img_np.shape[:2]
            
            # Simulate text detection and erasing
            if method == "self_supervised":
                # Create a mask for text regions (simulated)
                mask = np.zeros((h, w), dtype=np.uint8)
                
                # Generate random rectangular regions as "text"
                num_regions = random.randint(3, 8)
                for _ in range(num_regions):
                    # Random text region
                    x1 = random.randint(0, w-50)
                    y1 = random.randint(0, h-20)
                    x2 = x1 + random.randint(30, 100)
                    y2 = y1 + random.randint(15, 30)
                    
                    # Apply Gaussian blur to simulate text removal
                    region = img_np[y1:y2, x1:x2]
                    if region.size > 0:
                        # Apply inpainting or blurring
                        kernel_size = int(5 * strength) + 1
                        kernel_size = kernel_size if kernel_size % 2 == 1 else kernel_size + 1
                        blurred_region = cv2.GaussianBlur(region, (kernel_size, kernel_size), 0)
                        
                        # Blend the blurred region back
                        alpha = 0.8 * strength
                        img_np[y1:y2, x1:x2] = cv2.addWeighted(
                            region, 1-alpha, blurred_region, alpha, 0
                        )
                
                # Create a more realistic mask with text-like shapes
                for i in range(h)):
                    for j in range(w)):
                        # Simple pattern to simulate text
                        if (i // 20 + j // 20) % 2 == 0:
                            mask[i,j] = 255
                
                # Apply inpainting using the mask
                result = cv2.inpaint(img_np, mask, 3, cv2.INPAINT_TELEA)
            else:
                # For other methods, use a different approach
                # Apply median filtering for text removal
                result = cv2.medianBlur(img_np, int(5 * strength) + 1)
            
            # Ensure we have a valid image
            if result is None or result.size == 0:
                result = img_np
                
            return result
            
        except Exception as e:
            print(f"Error in text erasing: {e}")
            return image

def main():
    demo = TextErasingDemo()
    
    def process_image(input_image, method, strength):
        """
        Process the image with text erasing
        """
        try:
            result = demo.erase_text(input_image, method, strength)
            return result
        except Exception as e:
            raise gr.Error(f"Failed to process image: {str(e)}")
    
    with gr.Blocks(
        title="Self-supervised Text Erasing",
        footer_links=[{"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"]
    ) as app:
        gr.Markdown("# 🎨 Self-supervised Text Erasing")
        gr.Markdown("Upload an image containing text and see it get erased!")
        
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(
                    label="Input Image",
                    type="pil",
                    sources=["upload", "webcam"],
                    interactive=True
                )
                
                method_selector = gr.Dropdown(
                    choices=["self_supervised", "traditional", "neural_network"],
                    label="Erasing Method",
                    value="self_supervised"
                )
                
                strength_slider = gr.Slider(
                    label="Erasing Strength",
                    minimum=0.1,
                    maximum=1.0,
                    value=0.7,
                    step=0.1
                )
                
            with gr.Column():
                output_image = gr.Image(
                    label="Output Image (Text Erased)"
                )
        
        process_btn = gr.Button("Erase Text �", variant="primary")
        
        # Example images
        example_images = [
            ["https://raw.githubusercontent.com/alimama-creative/Self-supervised-Text-Erasing/main/assets/example1.jpg"],
            ["https://raw.githubusercontent.com/alimama-creative/Self-supervised-Text-Erasing/main/assets/example2.jpg"],
            ["https://raw.githubusercontent.com/alimama-creative/Self-supervised-Text-Erasing/main/assets/example3.jpg"]
        ]
        
        gr.Examples(
            examples=example_images,
            inputs=input_image,
            outputs=output_image,
            fn=process_image,
            cache_examples=True
        )
        
        # Event listener with Gradio 6 syntax
        process_btn.click(
            fn=process_image,
            inputs=[input_image, method_selector, strength_slider],
            outputs=output_image,
            api_visibility="public"
        )
        
        # Additional information
        with gr.Accordion("About this Demo"):
            gr.Markdown("""
            ## Self-supervised Text Erasing
            
            This demo showcases text erasing capabilities using self-supervised learning approaches.
            
            **Features:**
            - Multiple text erasing methods
            - Adjustable erasing strength
            - Real-time processing
            
            **How to use:**
            1. Upload an image with text or use your webcam
            2. Select the erasing method
            3. Adjust the erasing strength
            4. Click 'Erase Text' to process the image
            
            **Note:** This is a simulation of the actual text erasing process.
            """)
    
    app.launch(
        share=False,
        server_name="0.0.0.0",
        server_port=7860
    )

if __name__ == "__main__":
    main()