add application file

app.py

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+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")
+
+
+def inference(input_image):
+    preprocess = transforms.Compose([
+        transforms.Resize(260),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        #transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    input_tensor = preprocess(input_image)
+    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
+
+    # move the input and model to GPU for speed if available
+    if torch.cuda.is_available():
+        input_batch = input_batch.to('cuda')
+        model.to('cuda')
+    else:
+        model.to('cpu')
+
+    with torch.no_grad():
+        output = model(input_batch)
+    # The output has unnormalized scores. To get probabilities, you can run a softmax on it.
+    probabilities = torch.nn.functional.softmax(output[0], dim=0)
+
+    # Read the categories
+    with open("artist_classes.txt", "r") as f:
+        categories = [s.strip() for s in f.readlines()]
+        
+    categories = {
+        0:"vanGogh",
+        1:"Monet",
+        2:"Leonardo da Vinci",
+        3:"Rembrandt",
+        4:"Pablo Picasso",
+        5:"Salvador Dali"
+    }
+    
+    # Show top categories per image
+    top5_prob, top5_catid = torch.topk(probabilities, 6)
+    result = {}
+    for i in range(top5_prob.size(0)):
+        result[categories[top5_catid[i].item()]] = top5_prob[i].item()
+    return result
+
+inputs = gr.Image(type='pil')
+outputs = gr.Label(type="confidences",num_top_classes=5)
+
+title = "Artist Classifier"
+description = "Gradio demo for MOBILENET V2, Efficient networks optimized for speed and memory, with residual blocks. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1801.04381'>MobileNetV2: Inverted Residuals and Linear Bottlenecks</a> | <a href='https://github.com/pytorch/vision/blob/master/torchvision/models/mobilenet.py'>Github Repo</a></p>"
+
+#examples = [
+#            ['dog.jpg']
+#]
+#gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch()
+gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, analytics_enabled=False).launch()