Update app.py

app.py

@@ -1,12 +1,12 @@
 import gradio as gr
 from PIL import Image
 import torch
-import torch.nn.functional as F
-import torchvision
-import torchvision.transforms as T
+#import torch.nn.functional as F
+#import torchvision
+#import torchvision.transforms as T
 from diffusers import StableDiffusionInpaintPipeline
 import numpy as np
-import cv2
+#import cv2
 import os
 import shutil
 from gradio_client import Client, handle_file
@@ -27,11 +27,11 @@ inpaint_pipeline = load_inpainting_model()
 
 # Function to resize image (simpler interpolation method for speed)
 def resize_to_match(input_image, output_image):
-    torch_img = pil_to_torch(input_image)
-    torch_img_scaled = F.interpolate(torch_img.unsqueeze(0),mode='trilinear').squeeze(0)
-    output_image = torchvision.transforms.functional.to_pil_image(torch_img_scaled, mode=None)
-    return output_image
-    #return output_image.resize(input_image.size, Image.BICUBIC)  # Use BILINEAR for faster resizing
+    #torch_img = pil_to_torch(input_image)
+    #torch_img_scaled = F.interpolate(torch_img.unsqueeze(0),mode='trilinear').squeeze(0)
+    #output_image = torchvision.transforms.functional.to_pil_image(torch_img_scaled, mode=None)
+    #return output_image
+    return output_image.resize(input_image.size, Image.BICUBIC)  # Use BILINEAR for faster resizing
 
 # Function to generate the mask using Florence SAM Masking API (Replicate)
 def generate_mask(image_path, text_prompt="clothing"):