Update app.py

app.py

@@ -2,15 +2,14 @@ import gradio as gr
 from PIL import Image
 import numpy as np
 import os,sys
-import uuid
 import torch
 import cv2
+import base64
 
 import io
 import multiprocessing
 import random
 import time
-import imghdr
 from loguru import logger
 
 from share_btn import community_icon_html, loading_icon_html, share_js
@@ -168,8 +167,14 @@ def model_process(image, mask, img_enhancer):
         log_info = f"image_enhancer_: {(time.time() - start) * 1000}ms, {res_np_img.shape} "
         logger.info(log_info)
          
-    return  image # image
+    return  image
     
+def resize_image(pil_image, new_width=400):
+    width, height = pil_image.size
+    new_height = int(height*(new_width/width))
+    pil_image = pil_image.resize((new_width, new_height))
+    return pil_image
+
 model = ModelManager(
         name='lama',
         device=device,
@@ -192,7 +197,7 @@ def predict(input, img_enhancer):
         image = np.array(image_pil)
         mask = np.array(mask_pil.convert("L"))
     output = model_process(image, mask, img_enhancer)
-    return output, [image_pil, output], gr.update(visible=True)
+    return output, [resize_image(image_pil, new_width=400), resize_image(output, new_width=400)], gr.update(visible=True)
 
 css = '''    
     .container {max-width: 1150px; margin: auto;padding-top: 1.5rem}
@@ -411,4 +416,4 @@ with image_blocks as demo:
     
         begin_button.click(fn=None, inputs=[], outputs=[group_1, group_2], _js=start_cleaner)
 
-image_blocks.launch(server_name='0.0.0.0')
+image_blocks.launch(server_name='0.0.0.0')