application file

app.py

@@ -0,0 +1,37 @@
+import torch  # for model
+import numpy as np
+import torch.nn as nn
+import torch.optim as optim
+from PIL import Image  #for importing images
+import torchvision.models as models  #to load vgg 19 model
+import torchvision.transforms as transforms  #to transform the images
+from torchvision.utils import save_image #to save the generated images
+from tqdm import tqdm # progress bar
+import matplotlib.pyplot as plt
+import gradio as gr
+
+from styleTransfer import style_transfer
+from dataTransform import tensor_to_image
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+print(device)
+
+def gradio_style_transfer(steps, content_image, style_image):
+    generated_tensor = style_transfer(content_image, style_image, total_steps= steps)
+    generated_image = tensor_to_image(generated_tensor)
+    return generated_image
+
+
+interface = gr.Interface(
+    fn=gradio_style_transfer,
+    inputs=[
+        gr.Slider(minimum=100, maximum=1000, step=100, label="Number of Steps", value=500),
+        gr.Image(type='filepath', label='Content Image'),
+        gr.Image(type='filepath', label='Style Image')
+    ],
+    outputs=gr.Image(type='pil', label='Generated Image'),
+    title="Neural Style Transfer",
+    description="Upload a content image and a style image, and the app will generate a stylized image.",
+)
+
+interface.launch(debug = True)