from run import process
import time
import os
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
    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):
    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 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 = "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 = [
    [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')],
]

with gr.Blocks(theme="Nymbo/Alyx_Theme") as demo:
    width = 240
    height = 340

    with gr.Row(equal_height=False):
        with gr.Column(min_width=240):
            image_input = gr.Image(type="numpy", label="", height=height)
            gr.Examples(examples=examples, inputs=image_input, examples_per_page=10, elem_id="example_img")
            process_button = gr.Button("Nude!", size="sm")

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

    image_input.change(fn=lambda x: x, inputs=[image_input], outputs=[gr.State([])])
    process_button.click(update_status, inputs=image_input, outputs=image_input)

demo.queue(max_size=10)
demo.launch()