Update app.py

app.py

@@ -1,5 +1,14 @@
+try:
+    import torch
+    import torchvision
+except ImportError:
+    import subprocess
+    import sys
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "torch", "torchvision"])
+    import torch
+    import torchvision
+
 import gradio as gr
-import torch
 import numpy as np
 from PIL import Image
 import torchvision.transforms as transforms
@@ -83,21 +92,26 @@ def apply_gaussian_blur(image, sigma):
     
     return Image.fromarray(blurred.astype(np.uint8))
 
-# Initialize depth estimation pipeline
-pipe = pipeline(
-    task="depth-estimation",
-    model="depth-anything/Depth-Anything-V2-Small-hf",
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-    device=0 if torch.cuda.is_available() else -1
-)
+# Initialize depth estimation pipeline (moved inside the processing function to avoid CUDA issues)
+def get_depth_pipeline():
+    return pipeline(
+        task="depth-estimation",
+        model="depth-anything/Depth-Anything-V2-Small-hf",
+        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+        device=0 if torch.cuda.is_available() else -1
+    )
 
 def process_image(image, blur_type, gaussian_sigma, lens_min_sigma, lens_max_sigma):
     """Main processing function for Gradio interface"""
+    if image is None:
+        return None
+        
     processed_image = preprocess_image(image)
     
     if blur_type == "Gaussian Blur":
         result = apply_gaussian_blur(processed_image, gaussian_sigma)
     else:  # Lens Blur
+        pipe = get_depth_pipeline()
         depth_map = estimate_depth(processed_image, pipe)
         result = apply_depth_aware_blur(processed_image, depth_map, lens_max_sigma, lens_min_sigma)