Update app.py

app.py

@@ -1,65 +1,65 @@
-import gradio as gr
-from PIL import Image
-import torch
-import numpy as np
-import sys
-import os
-import RRDBNet_arch as arch
-# Add the `scripts/` folder to the system path
-sys.path.append(os.path.join(os.path.dirname(__file__), 'scripts'))
-
-
-
-
-# Path to the pretrained model
-model_path = os.path.join(os.path.dirname(__file__), 'models', 'RRDB_ESRGAN_x4.pth')
-
-# Load ESRGAN model
-device = torch.device('cpu') #'cuda' if using GPU
-model = arch.RRDBNet(3, 3, 64, 23, gc=32)
-model.load_state_dict(torch.load(model_path, map_location=device), strict=True)
-model.eval()
-model = model.to(device)
-
-
-def upscale_image(image):
-    img = np.array(image) / 255.0
-    img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()
-    img_LR = img.unsqueeze(0).to(device)
-
-    with torch.no_grad():
-        output = model(img_LR).data.squeeze().float().cpu().clamp_(0, 1).numpy()
-    output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))
-    output = (output * 255.0).round().astype(np.uint8)
-    return Image.fromarray(output)
-
-# Gradio interface 
-def gradio_interface(image):
-    try:
-        
-        if image is None:
-            raise ValueError("No image uploaded. Please upload an image to upscale.")
-
-       
-        upscaled_image = upscale_image(image)
-        original_size = image.size
-        upscaled_size = upscaled_image.size
-
-        return image, upscaled_image, f"Original Size: {original_size[0]}x{original_size[1]}", f"Upscaled Size: {upscaled_size[0]}x{upscaled_size[1]}"
-
-    except Exception as e:
-        
-        return None, None, "Error", str(e)
-
-
-gr_interface = gr.Interface(
-    fn=gradio_interface,
-    inputs=gr.Image(type="pil"),
-    outputs=[gr.Image(), gr.Image(), gr.Text(), gr.Text()],
-    title="ESRGAN Image Upscaler",
-    description="Upload an image to upscale it using ESRGAN."
-)
-
-if __name__ == '__main__':
-   
-    gr_interface.launch()
+import gradio as gr
+from PIL import Image
+import torch
+import numpy as np
+import sys
+import os
+
+# Add the `scripts/` folder to the system path
+sys.path.append(os.path.join(os.path.dirname(__file__), 'scripts'))
+import RRDBNet_arch as arch
+
+
+
+# Path to the pretrained model
+model_path = os.path.join(os.path.dirname(__file__), 'models', 'RRDB_ESRGAN_x4.pth')
+
+# Load ESRGAN model
+device = torch.device('cpu') #'cuda' if using GPU
+model = arch.RRDBNet(3, 3, 64, 23, gc=32)
+model.load_state_dict(torch.load(model_path, map_location=device), strict=True)
+model.eval()
+model = model.to(device)
+
+
+def upscale_image(image):
+    img = np.array(image) / 255.0
+    img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()
+    img_LR = img.unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        output = model(img_LR).data.squeeze().float().cpu().clamp_(0, 1).numpy()
+    output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))
+    output = (output * 255.0).round().astype(np.uint8)
+    return Image.fromarray(output)
+
+# Gradio interface 
+def gradio_interface(image):
+    try:
+        
+        if image is None:
+            raise ValueError("No image uploaded. Please upload an image to upscale.")
+
+       
+        upscaled_image = upscale_image(image)
+        original_size = image.size
+        upscaled_size = upscaled_image.size
+
+        return image, upscaled_image, f"Original Size: {original_size[0]}x{original_size[1]}", f"Upscaled Size: {upscaled_size[0]}x{upscaled_size[1]}"
+
+    except Exception as e:
+        
+        return None, None, "Error", str(e)
+
+
+gr_interface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Image(), gr.Image(), gr.Text(), gr.Text()],
+    title="ESRGAN Image Upscaler",
+    description="Upload an image to upscale it using ESRGAN."
+)
+
+if __name__ == '__main__':
+   
+    gr_interface.launch()