Create app.py

app.py

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+import torch
+from torchvision import models, transforms
+from PIL import Image
+import gradio as gr
+
+class_names = [
+    "plaque_calculus",
+    "caries",
+    "plaque_gingivitis",
+    "hypodontia",
+    "mouth_ulcer",
+    "tooth_discoloration"
+]
+
+# Load the model and update the final fully connected layer
+model = models.resnet50(weights=None)
+model.fc = torch.nn.Linear(model.fc.in_features, len(class_names))
+
+# Load the model weights from tooth_model.pth
+model.load_state_dict(torch.load('tooth_model.pth', map_location=torch.device('cpu')))
+model.eval()
+
+# Preprocessing steps for input images
+preprocess = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+def predict_image(image):
+    # Preprocess the image and add a batch dimension
+    processed_image = preprocess(image).unsqueeze(0)
+
+    with torch.no_grad():
+        outputs = model(processed_image)
+        _, top_indices = torch.topk(outputs, 2)  # Get top 2 predictions
+        top_classes = [class_names[idx] for idx in top_indices[0]]
+
+    return ", ".join(top_classes)
+
+# Set up the Gradio interface
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",  # Output will be text listing 
+    title="Medical Image Classification",
+    description="Upload an image to predict its class."
+)
+
+# Launch the interface
+iface.launch()