Update app.py

app.py

@@ -1,16 +1,17 @@
+from run import process
 import time
+import subprocess
+import os
+import argparse
 import cv2
-import numpy as np
-import gradio as gr
+import sys
 from PIL import Image
+import torch
+import gradio as gr
 
+TESTdevice = "cpu"
 index = 1
 
-def process(dress):
-    # Dummy processing function for demonstration
-    # Replace with your actual processing logic
-    return cv2.GaussianBlur(dress, (15, 15), 0)
-
 def mainTest(inputpath, outpath):
     watermark = deep_nude_process(inputpath)
     watermark1 = cv2.cvtColor(watermark, cv2.COLOR_BGRA2RGBA)
@@ -27,17 +28,16 @@ def deep_nude_process(inputpath):
 
 def inference(img):
     global index
-    img = np.array(img)  # Convert PIL image to NumPy array
     bgra = cv2.cvtColor(img, cv2.COLOR_RGBA2BGRA)
-    inputpath = f"input_{index}.jpg"
+    inputpath = "input_" + str(index) + ".jpg"
     cv2.imwrite(inputpath, bgra)
 
-    outputpath = f"out_{index}.jpg"
+    outputpath = "out_" + str(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 Image.fromarray(output)
+    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 ⛔"
@@ -47,15 +47,16 @@ examples = [
     ['input.jpg', 'Test'],
 ]
 
-web = gr.Interface(
-    fn=inference,
-    inputs=gr.Image(type="pil", label="Input Image"),
-    outputs=gr.Image(type="pil", label="Processed Image"),
-    title=title,
-    description=description,
-    examples=examples,
-    allow_flagging="never"
-)
+def update_image(img):
+    return gr.update(value=inference(img))
+
+web = gr.Interface(fn=update_image,
+                   inputs=gr.Image(type="numpy", label="Upload Image"),
+                   outputs=gr.Image(type="numpy", label="Processed Image"),
+                   title=title,
+                   description=description,
+                   examples=examples,
+                   )
 
 if __name__ == '__main__':
     web.launch()