change app name

app.py

@@ -0,0 +1,54 @@
+import numpy as np
+import gradio as gr
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from model import SRCNNModel, pred_SRCNN
+from PIL import Image
+
+
+title = "Super Resolution with CNN"
+description = """
+
+Your low resolution image will be reconstructed to high resolution with a scale of 2 with a convolutional neural network!
+
+CNN output on the left, bicubic interpolation output on the right.
+
+
+"""
+
+article = "Check out the origianl [paper](https://arxiv.org/abs/1501.00092) proposed by Dong *et al*."
+
+# load model
+print("Loading  SRCNN model...")
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+model = SRCNNModel().to(device)
+model.load_state_dict(torch.load('SRCNNmodel_trained.pt'))
+model.eval()
+print("SRCNN model loaded!")
+
+def image_grid(imgs, rows, cols):
+    '''
+    imgs:list of PILImage
+    '''
+    assert len(imgs) == rows*cols
+
+    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
+
+def sepia(image_path):
+    # gradio open image as np array
+    image = Image.fromarray(image_path,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 grid
+
+demo = gr.Interface(fn = sepia, inputs=gr.Image(shape=(200, 200)), outputs="image",title=title,description = description,article = article,examples=['LR_image.png','barbara.png'])
+
+demo.launch(share=True)