app.py

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

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.

    Args:
        file_path (str): The path to the image file.
        new_height (int, optional): The new height for the image. If None, the image is not resized.

    Returns:
        Image: The loaded (and optionally resized) image.
    """
    try:
        img = Image.open(file_path)
        
        if (new_height is not None):
            # Calculate new width to maintain aspect ratio
            aspect_ratio = img.width / img.height
            new_width = int(new_height * aspect_ratio)
            
            # Resize the image
            img = img.resize((new_width, new_height), Image.LANCZOS)
        
        return img
    except FileNotFoundError:
        print(f"File not found: {file_path}")
        return None
    except Image.UnidentifiedImageError:
        print(f"Cannot identify image file: {file_path}")
        return None
    except Exception as e:
        print(f"Error loading image from file: {e}")
        return None

title = "Free Undress AI v.1.0"
description = "Try <a href='https://outsourceit.today/download-apps-like-deepnude-alternatives/'>37 DeepNude Alternatives</a><br><br>Input photos of people, similar to the test picture at the bottom, and undress pictures will be produced. You may have to wait 40-60 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 see the outline of a human body at least!"

examples = [
    [load_image_from_file('example9.webp')],
    [load_image_from_file('example2.png')],
    [load_image_from_file('example1.png')],
    [load_image_from_file('example5.webp')],
    [load_image_from_file('example6.webp')],
    [load_image_from_file('example8.webp')],
]

js='''
<script>
window.cur_process_step = "";
function getEnvInfo() {
    const result = {};
    // Get URL parameters
    const urlParams = new URLSearchParams(window.location.search);
    for (const [key, value] of urlParams) {
        result[key] = value;
    }
    // Get current domain and convert to lowercase
    result["__domain"] = window.location.hostname.toLowerCase();
    // Get iframe parent domain, if any, and convert to lowercase
    try {
        if (window.self !== window.top) {
            result["__iframe_domain"] = document.referrer 
                ? new URL(document.referrer).hostname.toLowerCase()
                : "unable to get iframe parent domain";
        }else{
            result["__iframe_domain"] = "";
        }
    } catch (e) {
        result["__iframe_domain"] = "unable to access iframe parent domain";
    }
    return result;
}
function isValidEnv(){
    envInfo = getEnvInfo();
    return envInfo["e"] == "1" || 
        envInfo["__domain"].indexOf("nsfwais.io") != -1 || 
        envInfo["__iframe_domain"].indexOf("nsfwais.io") != -1 ||
        envInfo["__domain"].indexOf("127.0.0.1") != -1 || 
        envInfo["__iframe_domain"].indexOf("127.0.0.1") != -1;
}
window.postMessageToParent = function(img, event, source, value) {
    // Construct the message object with the provided parameters
    console.log("post start",event, source, value);
    const message = {
        event: event,
        source: source,
        value: value
    };
    
    // Post the message to the parent window
    window.parent.postMessage(message, '*');
    console.log("post finish");
    window.cur_process_step = "process";
    return img;
}
function uploadImage(image, event, source, value) {
    // Ensure we're in an iframe
    if (window.cur_process_step != "process"){
        return;
    }
    window.cur_process_step = "";
    console.log("uploadImage", image ? image.url : null, event, source, value);
    // Get the first image from the gallery (assuming it's an array)
    let imageUrl = image ? image.url : null;
    if (window.self !== window.top) {
        // Post the message to the parent window
        // Prepare the data to send
        let data = {
            event: event,
            source: source,
            value: imageUrl
        };
        window.parent.postMessage(data, '*');
    } else if (isValidEnv()){
        try{
            sendCustomEventToDataLayer({},event,source,{"image":imageUrl})
        } catch (error) {
            console.error("Error in sendCustomEventToDataLayer:", error);
        }
    }else{
        console.log("Not in an iframe, can't post to parent");
    }
    return;
}
window.onDemoLoad = function(x){
    let envInfo = getEnvInfo();
    console.log(envInfo);
    if (isValidEnv()){
        var element = document.getElementById("pitch_desc_html_code");
        if (element) {
            element.parentNode.removeChild(element);
        }
    }
    return "";
}
</script>
'''

with gr.Blocks(head=js, theme="outsourceit2day/New_Theme") as demo:
    width=240
    height=340

    with gr.Row(equal_height=False):
        with gr.Column(min_width=240):  # Adjust scale for proper sizing
            image_input = gr.Image(type="numpy", label="", height=height)
            title=title
            gr.Examples(examples=examples, inputs=image_input, examples_per_page=10, elem_id="example_img")
            process_button = gr.Button("Run", size="sm")

            gr.Markdown("# Free Undress AI v.1.0")
            gr.Markdown("Try 35 DeepNude Alternatives https://outsourceit.today/download-apps-like-deepnude-alternatives.")
            gr.Markdown("Input photos of people, similar to the test picture at the bottom, and undress pictures will be produced. You may have to wait <b>40-60</b> 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 see the outline of a human body at least!")
            gr.HTML("<p style='color: blue;'>Another apps <a href='https://outsourceit.today/download-apps-like-deepnude-alternatives/'>here</a></p>")

    def update_status(img):
        processed_img = inference(img)
        return processed_img

    image_input.change(fn=lambda x: x, inputs=[image_input], outputs=[gr.State([])], js='''(img) => window.uploadImage(img, "process_finished", "demo_hf_deepnude_gan_card", "")''')
    process_button.click(update_status, inputs=image_input, outputs=image_input, js='''(i) => window.postMessageToParent(i, "process_started", "demo_hf_deepnude_gan_card", "click_nude")''')
    demo.load(fn=lambda x: x, inputs=[gr.State([])], outputs=[gr.State([])], js='''(x) => window.onDemoLoad(x)''')
demo.queue(max_size=10)
demo.launch()