Create app.py

app.py

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+import torch
+from torchvision import models, transforms, datasets
+
+from PIL import Image
+
+import gradio as gr
+
+
+model_ft = models.resnet18(pretrained = True)
+num_ftrs = model_ft.fc.in_features
+model_ft.fc = nn.Linear(num_ftrs, 2)
+
+state_dict = torch.load("up500Model.pt", map_location = "cpu")
+
+model_ft.load_state_dict(state_dict)
+model_ft.eval()
+
+img_transforms = transforms.Compose(
+    [
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ]
+)
+
+labels = ["fiat500", "VW Up!"]
+def predict(img):
+    inp = img.fromarray(inp.astype("unit8"), "RGB")
+    inp = img_transforms(inp).unsqueeze(0)
+
+    # We don't want to compute gradients
+    with torch.no_grad():
+        preds = torch.np.functional.softmax(model_ft(inp)[0])
+
+    return {labels[i]: preds[i] for i in range(2)}
+
+interface = gr.Interface(
+    predict, 
+    inputs = "image", 
+    output = "label",
+    title = "Car classification"
+)
+interface.launch()