Update app.py

app.py

@@ -127,13 +127,13 @@ def inference(image, upscale, large_input_flag, color_fix):
 	print(f'input size: {img.shape}')
 
 	# img2tensor
-	img = img.astype(np.float32) / 255.
-	img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()
-	img = img.unsqueeze(0).to(device)
+	y = y.astype(np.float32) / 255.
+	y = torch.from_numpy(np.transpose(y[:, :, [2, 1, 0]], (2, 0, 1))).float()
+	y = y.unsqueeze(0).to(device)
 
 	# inference
 	if large_input_flag:
-		patches, idx, size = img2patch(img, scale=upscale)
+		patches, idx, size = img2patch(y, scale=upscale)
 		with torch.no_grad():
 			n = len(patches)
 			outs = []
@@ -153,24 +153,26 @@ def inference(image, upscale, large_input_flag, color_fix):
 		output = patch2img(output, idx, size, scale=upscale)
 	else:
 		with torch.no_grad():
-			output = model(img)
+			output = model(y)
 
 	# color fix
 	if color_fix:
-		img = F.interpolate(img, scale_factor=upscale, mode='bilinear')
-		output = wavelet_reconstruction(output, img)
+		y = F.interpolate(y, scale_factor=upscale, mode='bilinear')
+		output = wavelet_reconstruction(output, y)
 	# tensor2img
 	output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
 	if output.ndim == 3:
 		output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))
 	output = (output * 255.0).round().astype(np.uint8)
+	
+    return (img, Image.fromarray(output))
 
-	# save restored img
-	save_path = f'results/out.png'
-	cv2.imwrite(save_path, output)
+	# # save restored img
+	# save_path = f'results/out.png'
+	# cv2.imwrite(save_path, output)
 
-	output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
-	return output, save_path
+	# output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
+	# return output, save_path
     
 
 
@@ -223,10 +225,11 @@ demo = gr.Interface(
     fn=inference,
     inputs=[
         gr.Image(value="real_testdata/004.png", type="pil", label="Input"),
-        gr.Number(minimum=2, maximum=4, default=2, label="Upscaling factor (up to 4)"),
+        gr.Number(minimum=2, maximum=4, default_value=2, label="Upscaling factor (up to 4)"),
 		gr.Checkbox(value=False, label="Memory-efficient inference"),
         gr.Checkbox(value=False, label="Color correction"),
     ],
+	
     outputs=ImageSlider(label="Super-Resolved Image", 
                         type="pil",
                         show_download_button=True,