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app.py

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+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from PIL import Image
+import gradio as gr
+
+# Define the same preprocessing as during training
+transform = transforms.Compose([
+    transforms.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+])
+
+# Define model architecture (same as training)
+def load_model():
+    model = models.resnet50(weights=None)  # Don't load pretrained again
+    in_features = model.fc.in_features
+    model.fc = nn.Sequential(
+        nn.Linear(in_features, 512),
+        nn.ReLU(),
+        nn.Dropout(0.4),
+        nn.Linear(512, 2)  # 2 classes: Fractured, Non-Fractured
+    )
+    model.load_state_dict(torch.load("fract_model.pth", map_location=torch.device('cpu')))
+    model.eval()
+    return model
+
+model = load_model()
+class_names = ["Fractured", "Non-Fractured"]
+
+# Prediction function
+def predict(image):
+    image = transform(image).unsqueeze(0)  # Add batch dimension
+    with torch.no_grad():
+        outputs = model(image)
+        _, predicted = torch.max(outputs, 1)
+        class_idx = predicted.item()
+        confidence = torch.softmax(outputs, dim=1)[0][class_idx].item()
+        return {class_names[class_idx]: float(confidence)}
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=2),
+    title="Bone Fracture Detection",
+    description="Upload an X-ray image to detect if it's Fractured or Non-Fractured."
+)
+
+if __name__ == "__main__":
+    interface.launch()

reqruiements.txt

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+torch>=2.0
+torchvision>=0.15
+gradio>=4.0
+pillow>=9.0