Delete app.py

app.py

@@ -1,69 +0,0 @@
-from __future__ import print_function
-import torch
-import torchvision
-import torch.nn as nn
-import gradio as gr
-import os
-import time
-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)
-