Upload app.py

app.py

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+from __future__ import print_function
+from torchvision import nn
+import gradio as gr
+import os
+import time
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import matplotlib.pyplot as plt
+import torchvision.transforms as transforms
+import copy
+import torchvision.models as models
+import torchvision.transforms.functional as TF
+from PIL import Image
+import numpy as np
+
+
+#Defining the predict function
+def style_transfer(cont_img,styl_img):
+  
+  #Start the timer
+  start_time = time.time()
+  
+  #transform the input image
+  style_img = image_transform(styl_img)
+  content_img =image_transform(cont_img)
+  
+  #getting input image
+  input_img = content_img.clone()
+
+  #running the style transfer
+  output = run_style_transfer(cnn, cnn_normalization_mean, cnn_normalization_std,
+                            content_img, style_img, input_img)
+  # output_img = output.detach().cpu().squeeze(0)
+  # output_img = TF.to_pil_image(output_img)
+  end_time=time.time()
+  
+  pred_time =round(end_time- start_time, 5)
+  
+  return output
+
+##Gradio App
+import gradio as gr
+title= 'Style Transfer'
+description='A model to transfer the style of one image to another'
+article = 'Created at Pytorch Model Deployment'
+
+#example_images
+example_images = [["/content/content.jpg" ,"/content/style.jpg"]]
+
+#Create the gradio demo
+demo = gr.Interface(
+    fn=style_transfer,
+    inputs=[
+        gr.inputs.Image(label="content Image"),
+        gr.inputs.Image(label="style_image")
+    ],
+    examples=example_images,
+    outputs="image",
+    allow_flagging=False,
+    title=title,
+    description=description,
+    article=article
+)
+
+# Launch the Gradio interface
+demo.launch(debug=True)
+