formatting

app.py

@@ -26,10 +26,12 @@ article = """
 
 # load model
 print("Loading  SRCNN model...")
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 model = SRCNNModel().to(device)
-model.load_state_dict(torch.load('SRCNNmodel_trained.pt',map_location=torch.device(device) ))
+model.load_state_dict(
+    torch.load("SRCNNmodel_trained.pt", map_location=torch.device(device))
+)
 model.eval()
 print("SRCNN model loaded!")
 
@@ -42,18 +44,37 @@ print("SRCNN model loaded!")
 #     w, h = imgs[0].size
 #     grid = Image.new('RGB', size=(cols*w, rows*h))
 #     grid_w, grid_h = grid.size
-    
+
 #     for i, img in enumerate(imgs):
 #         grid.paste(img, box=(i%cols*w, i//cols*h))
 #     return grid
+examples = [
+    ["LR_image.png"],
+    ["barbara.png"],
+]
+
 
 def sepia(image):
     # gradio open image as np array
-    image = Image.fromarray(image,mode='RGB')
-    out_final,image_bicubic,image = pred_SRCNN(model=model,image=image,device=device)
+    image = Image.fromarray(image, mode="RGB")
+    out_final, image_bicubic, image = pred_SRCNN(
+        model=model, image=image, device=device
+    )
     # grid = image_grid([out_final,image_bicubic],1,2)
-    return out_final,image_bicubic
+    return out_final, image_bicubic
+
 
-demo = gr.Interface(fn = sepia, inputs=gr.inputs.Image(label="Upload image"), outputs=[gr.outputs.Image(label="Conv net"), gr.outputs.Image(label="Bicubic interpoloation")],title=title,description = description,article = article,examples=[['LR_image.png'],['barbara.png']])
+demo = gr.Interface(
+    fn=sepia,
+    inputs=gr.inputs.Image(label="Upload image"),
+    outputs=[
+        gr.outputs.Image(label="Convolutional neural network"),
+        gr.outputs.Image(label="Bicubic interpoloation"),
+    ],
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+)
 
-demo.launch()
+demo.launch()