Add main space requirements file

app.py

@@ -1,11 +1,11 @@
-import torch
 import numpy as np
 import cv2
-from PIL import Image
+import torch
 import albumentations as albu
 from pylab import imshow
 import matplotlib.pyplot as plt
 from diffusers import StableDiffusionInpaintPipeline
+from PIL import Image
 from iglovikov_helper_functions.utils.image_utils import load_rgb, pad, unpad
 from iglovikov_helper_functions.dl.pytorch.utils import tensor_from_rgb_image
 from cloths_segmentation.pre_trained_models import create_model
@@ -18,13 +18,9 @@ try:
 except Exception as e:
     raise RuntimeError(f"Error loading segmentation model: {e}")
 
-# Load Inpainting Model
+# Load Inpainting Model (Without CUDA)
 try:
-    pipe = StableDiffusionInpaintPipeline.from_pretrained(
-        "runwayml/stable-diffusion-inpainting",
-        torch_dtype=torch.float16
-    )
-    pipe.to("cuda")  
+    pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
 except Exception as e:
     raise RuntimeError(f"Error loading inpainting model: {e}")
 
@@ -55,7 +51,6 @@ def resize_and_upscale(image, new_width, new_height):
     resized_img = cv2.resize(np.array(image), (new_width, new_height), interpolation=cv2.INTER_CUBIC)
     return Image.fromarray(resized_img)
 
-
 def image_segmentation_and_inpainting(image, prompt="Chinese Red and Golder Armor"):
     try:
         pil_image = Image.fromarray(image.astype('uint8'))  
@@ -69,7 +64,7 @@ def image_segmentation_and_inpainting(image, prompt="Chinese Red and Golder Armo
         plt.imsave(mask_path, mask, cmap='gray')
 
         output_image = perform_inpainting(temp_image_path, mask_path, prompt)
-        output_image = resize_and_upscale(output_image, 1280, 720) 
+        output_image = resize_and_upscale(output_image, 1280, 720)  # You can adjust the size
         return output_image
     except Exception as e:
         raise gr.Error(f"Error processing image: {e}")