Create app.py

app.py

@@ -0,0 +1,50 @@
+import torch
+from torchvision import models, transforms
+from PIL import Image
+import gradio as gr
+
+# Updated class names with 'plaque' in front of 'calculus' and 'gingivitis'
+class_names = [
+    "plaque_calculus",
+    "caries",
+    "plaque_gingivitis",
+    "hypodontia",
+    "mouth_ulcer",
+    "tooth_discoloration"
+]
+
+model = models.resnet50(weights=None)
+model.fc = torch.nn.Linear(model.fc.in_features, len(class_names))
+
+model.load_state_dict(torch.load('tooth_model.pth', map_location=torch.device('cpu')))
+model.eval()
+
+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):
+    processed_image = preprocess(image).unsqueeze(0)
+
+    with torch.no_grad():
+        outputs = model(processed_image)
+        probabilities = torch.nn.functional.softmax(outputs, dim=1)
+        top_probs, top_indices = torch.topk(probabilities, 3)
+        top_classes = [class_names[idx] for idx in top_indices[0]]
+
+    # Create a result dictionary with class names and probabilities
+    result = {top_classes[i]: top_probs[0][i].item() for i in range(3)}
+
+    return result
+
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs="label",
+    title="Medical Image Classification",
+    description="Upload an image to predict its class with probabilities of top 3 predictions."
+)
+
+iface.launch()