from run import process
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

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 🔞 There is a queue system. According to the logic of first come, first served, only one picture will be made at a time. Must be able to at least see the outline of a human body ⛔"

examples = [
    ['input.png', 'Test'],
    ['input.jpg', 'Test'],
]

css = """
body {
    background-color: rgb(17, 24, 39);
    color: white;
}
.gradio-container {
    background-color: rgb(17, 24, 39) !important;
    border: none !important;
}
footer {display: none !important;}
"""

with gr.Blocks(css=css) as demo:
    with gr.Row():
        with gr.Column(scale=2):  # Adjust scale for proper sizing
            image_input = gr.Image(type="numpy", label="Upload Image")
            process_button = gr.Button("Process Image")
            status = gr.Markdown(value="")
        with gr.Column(scale=1):  # Adjust scale for proper sizing
            gr.Markdown("### Examples")
            example_1 = gr.Examples(examples=[examples[0]], inputs=image_input, examples_per_page=1)
            example_2 = gr.Examples(examples=[examples[1]], inputs=image_input, examples_per_page=1)

    def update_status(img):
        status.update(value="Processing... Please wait.")
        processed_img = inference(img)
        status.update(value="Processing complete!")
        return processed_img

    process_button.click(update_status, inputs=image_input, outputs=image_input)

demo.launch()