Add application file

app.py

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+import gradio as gr
+import torch
+from torchvision import models
+from PIL import Image
+from torch import nn
+
+
+num_classes = 3
+device = "cuda" if torch.cuda.is_available() else "cpu"
+class_names = ["pizza", "steak", "sushi"]
+class_dict = {"pizza": 0, "steak": 1, "sushi": 2}
+
+
+def classify_image(image_path):
+    # Load the pre-trained model
+    model = models.mobilenet_v3_large(
+        weights=models.MobileNet_V3_Large_Weights.IMAGENET1K_V1
+    )
+    model.classifier[-1] = nn.Linear(model.classifier[-1].in_features, num_classes)
+    model.load_state_dict(
+        torch.load(
+            "MobileNetV3-Food-Classification.pth",
+            weights_only=True,
+            map_location=device,
+        )
+    )
+    model.to(device)
+    model.eval()
+
+    # Get the proper transforms directly from the weights
+    weights = models.MobileNet_V3_Large_Weights.IMAGENET1K_V1
+    preprocess = weights.transforms()
+
+    # Load and transform the image
+    image = Image.open(image_path)
+    input_tensor = preprocess(image)
+
+    # Add batch dimension
+    input_batch = input_tensor.unsqueeze(0)
+
+    # Move to GPU if available
+    input_batch = input_batch.to(device)
+
+    # Perform inference
+    with torch.no_grad():
+        output = model(input_batch)
+
+    # Get predictions
+    probabilities = torch.nn.functional.softmax(output[0], dim=0)
+
+    # Get the top prediction
+    top_prob, top_catid = torch.topk(probabilities, 1)
+    top_category = class_names[top_catid.item()]  # type: ignore
+    top_probability = top_prob.item()
+
+    # Format the output string nicely
+    return f"Prediction: {top_category.title()} ({top_probability:.1%} confident)"
+
+
+# Update the interface with label and better output type
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(
+        type="filepath", label="Upload a food image (pizza, steak, or sushi)"
+    ),
+    outputs=gr.Text(label="Classification Result"),
+    title="Food Classifier",
+    description="This model classifies images of pizza, steak, and sushi.",
+)
+demo.launch()