app.py

import time
import subprocess
import os
import argparse
import cv2
import sys
from PIL import Image
import torch
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
    bgra = cv2.cvtColor(img, cv2.COLOR_RGBA2BGRA)
    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 update_status(img):
    return inference(img), gr.update(value="Processing complete!")

def init_interface(request: gr.Request):
    query_params = request.query_params
    bg_color = query_params.get('bg_color', 'rgb(17, 24, 39)')
    image_height = query_params.get('image_height', '90%')
    css = f"""
    body {{
        background-color: {bg_color};
        color: white;
        overflow: hidden;
    }}
    .gradio-container {{
        background-color: {bg_color} !important;
        border: none !important;
    }}
    .image-container {{
        height: {image_height} !important;
        display: flex;
        align-items: center;
        justify-content: center;
    }}
    .image-container img {{
        width: auto !important;
        height: 100% !important;
    }}
    footer {{
        display: none !important;
    }}
    """

    with gr.Blocks(css=css) as demo:
        with gr.Column():
            with gr.Row(elem_id="image-container"):
                image_input = gr.Image(type="numpy", label="Upload Image")
            process_button = gr.Button("Process Image")

        process_button.click(update_status, inputs=image_input, outputs=[image_input])
    
    return demo

with gr.Blocks() as outer_demo:
    outer_demo.load(init_interface)

outer_demo.launch()