Update app.py

app.py

@@ -1,13 +1,7 @@
-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
+import cv2
+import time
+from urllib.parse import parse_qs, urlparse
 
 TESTdevice = "cpu"
 index = 1
@@ -39,39 +33,46 @@ def inference(img):
     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 ⛔"
+def get_css(bg_color, width, height):
+    return f"""
+    body {{
+        background-color: {bg_color};
+        color: white;
+        overflow: hidden; /* Prevent scrolling */
+    }}
+    .gradio-container {{
+        background-color: {bg_color} !important;
+        border: none !important;
+        width: {width} !important;  /* Set the width */
+        height: {height} !important; /* Set the height */
+        max-width: 100%; /* Ensure it does not exceed the container's width */
+        max-height: 100%; /* Ensure it does not exceed the container's height */
+        overflow: scroll; /* Prevent internal scrolling */
+    }}
+    footer {{display: none !important;}} /* Hide footer */
+    """
+
+def update_status(img):
+    return inference(img), gr.update(value="Processing complete!")
 
-examples = [
-    ['input.png', 'Test'],
-    ['input.jpg', 'Test'],
-]
+def create_interface(request):
+    query_params = parse_qs(urlparse(request.url).query)
+    bg_color = query_params.get('bg_color', ['rgb(17, 24, 39)'])[0]
+    width = int(query_params.get('width', ['auto'])[0])
+    height = int(query_params.get('height', ['100%'])[0])
 
-css = """
-body {
-    background-color: rgb(17, 24, 39);
-    color: white;
-    overflow: hidden; /* Prevent scrolling */
-}
-.gradio-container {
-    background-color: rgb(17, 24, 39) !important;
-    border: none !important;
-    max-width: 100%; /* Ensure it does not exceed the container's width */
-    max-height: 100%; /* Ensure it does not exceed the container's height */
-    overflow: hidden; /* Prevent internal scrolling */
-}
-footer {display: none !important;} /* Hide footer */
-"""
+    css = get_css(bg_color, width, height)
+    
+    with gr.Blocks(css=css) as demo:
+        with gr.Column():
+            image_input = gr.Image(type="numpy", label="Upload Image", height=height, width=width)
+            process_button = gr.Button("Process Image")
+            status = gr.Markdown(value="")
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column():
-        image_input = gr.Image(type="numpy", label="Upload Image", height=512, width=512)
-        process_button = gr.Button("Process Image")
-        status = gr.Markdown(value="")
+        process_button.click(update_status, inputs=image_input, outputs=[image_input, status])
 
-    def update_status(img):
-        return inference(img), gr.update(value="Processing complete!")
+    return demo
 
-    process_button.click(update_status, inputs=image_input, outputs=[image_input, status])
+demo = gr.Interface(create_interface, inputs=[], outputs=[])
 
 demo.launch()