app.py

from run import process
import time
import cv2
from PIL import Image
import gradio as gr

TESTdevice = "cpu"
index = 1

def mainTest(inputpath, outpath):
    watermark = deep_nude_process(inputpath)
    watermark1 = cv2.cvtColor(watermark, cv2.COLOR_BGRA2RGBA)
    return watermark1

def deep_nude_process(inputpath):
    dress = cv2.imread(inputpath)
    h = dress.shape[0]
    w = dress.shape[1]
    dress = cv2.resize(dress, (512, 512), interpolation=cv2.INTER_CUBIC)
    watermark = process(dress)
    watermark = cv2.resize(watermark, (w, h), interpolation=cv2.INTER_CUBIC)
    return watermark

def inference(img):
    global index
    if img is None:
        return None
    
    bgra = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
    inputpath = f"input_{index}.jpg"
    cv2.imwrite(inputpath, bgra)

    outputpath = f"out_{index}.jpg"
    index += 1
    print(time.strftime("START!!!!!!!!! %Y-%m-%d %H:%M:%S", time.localtime()))
    output = mainTest(inputpath, outputpath)
    print(time.strftime("Finish!!!!!!!!! %Y-%m-%d %H:%M:%S", time.localtime()))
    return output

def load_image_from_file(file_path, new_height=None):
    """
    Load an image from a file and optionally resize it while maintaining the aspect ratio.
    """
    try:
        img = Image.open(file_path)
        
        if new_height is not None:
            aspect_ratio = img.width / img.height
            new_width = int(new_height * aspect_ratio)
            img = img.resize((new_width, new_height), Image.LANCZOS)
        
        return img
    except FileNotFoundError:
        print(f"File not found: {file_path}")
        return None
    except Exception as e:
        print(f"Error loading image from file: {e}")
        return None

title = "Undress AI"
description = "⛔ Input photos of people, similar to the test picture at the bottom, and undress pictures will be produced. You may have to wait 30 seconds for a picture. 🔞 Do not upload personal photos 🔞"

# Prepare examples
examples = [
    ["example9.webp"],
    ["example2.png"],
    ["example1.png"],
    ["example5.webp"],
    ["example6.webp"],
    ["example8.webp"],
]

# Custom CSS
css = """
#example_img {
    max-height: 400px;
}
"""

# Create Gradio interface
with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
    gr.Markdown(f"# {title}")
    gr.Markdown(description)
    
    with gr.Row():
        with gr.Column():
            image_input = gr.Image(type="numpy", label="Input Image", height=340)
            process_button = gr.Button("Run", variant="primary")
            
            gr.Examples(
                examples=examples,
                inputs=image_input,
                label="Example Images",
                elem_id="example_img"
            )
        
        with gr.Column():
            image_output = gr.Image(type="numpy", label="Output Image", height=340)
    
    # Connect the button to the inference function
    process_button.click(
        fn=inference,
        inputs=image_input,
        outputs=image_output
    )

# Launch the app
if __name__ == "__main__":
    demo.queue(max_size=10)
    demo.launch()