fast food app launch

app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+
+
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+class_names = [
+    'Baked Potato',
+    'Burger',
+    'Crispy Chicken',
+    'Donut',
+    'Fries',
+    'Hot Dog',
+    'Pizza',
+    'Sandwich',
+    'Taco',
+    'Taquito'
+ ]
+
+model = models.resnet18(pretrained=True)
+num_features = model.fc.in_features
+model.fc = nn.Linear(num_features, len(class_names))
+
+# model = model.to(device)
+# load model from ./models
+model.load_state_dict(torch.load("models/model_resNet18_1.pth"))
+model.eval()
+
+def predict(inp):
+    inp = transforms.ToTensor()(inp).unsqueeze(0)
+    with torch.no_grad():
+        prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
+        confidences = {class_names[i]: float(prediction[i]) for i in range(len(class_names))}
+    return confidences
+
+app = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=10)    
+)
+
+# display the app
+app.launch()

requirements.txt

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+torch=1.13.0