Update app.py

app.py

@@ -1,27 +1,12 @@
-import torch
-from PIL import Image
-from torchvision import datasets, models, transforms
-import gradio as gr
-import os
-import torch.nn as nn
-
-
-os.system("wget https://github.com/liuxiaoyuyuyu/vanGogh-and-Other-Artist/blob/main/artist_classes.txt")
-#os.system("wget https://github.com/liuxiaoyuyuyu/vanGogh-and-Other-Artist/blob/main/model_weights_mobilenet_v2_valp1trainp2.pth")
-
-#model = torch.hub.load('pytorch/vision:v0.9.0', 'mobilenet_v2', pretrained=False)
-#checkpoint = 'https://github.com/liuxiaoyuyuyu/vanGogh-and-Other-Artist/blob/main/model_weights_mobilenet_v2_valp1trainp2.pth'
-#model.load_state_dict(torch.hub.load_state_dict_from_url(checkpoint, progress=False))
-model = models.vgg16()
-num_ftrs = model.classifier[6].in_features
-model.classifier[6] = nn.Linear(num_ftrs, 6)
-device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-#model = model.to(device)
-model.load_state_dict(torch.load('VGG16_weights_May28.pth',map_location=device))
-
-#torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
-
-
+#Imports
+import tensorflow as tf
+from tensorflow import keras
+import matplotlib.pyplot as plt
+import tensorflow_hub as hub
+
+#Load Magenta Arbitrary Image Stylization network
+hub_module = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/1')
+"""
 def inference(input_image):
     preprocess = transforms.Compose([
         transforms.Resize(260),
@@ -62,7 +47,7 @@ def inference(input_image):
     result = {}
     for i in range(top5_prob.size(0)):
         result[categories[top5_catid[i].item()]] = top5_prob[i].item()
-    return result
+    return result"""
 
 inputs = gr.Image(type='pil')
 outputs = gr.Label(type="confidences",num_top_classes=5)