Create app.py

app.py

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+import gradio as gr
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor from the Hugging Face Hub
+model_name = "prithivMLmods/Bone-Fracture-Detection"
+model = AutoModelForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def detect_fracture(image):
+    """
+    Takes a NumPy image array, processes it, and returns the model's prediction.
+    """
+    # Convert NumPy array to a PIL Image
+    image = Image.fromarray(image).convert("RGB")
+    
+    # Process the image and prepare it as input for the model
+    inputs = processor(images=image, return_tensors="pt")
+
+    # Perform inference without calculating gradients
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        
+        # Apply softmax to get probabilities and convert to a list
+        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
+
+    # Create a dictionary of labels and their corresponding probabilities
+    # This now correctly uses the labels from the model's configuration
+    prediction = {model.config.id2label[i]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return prediction
+
+# Create the Gradio Interface
+iface = gr.Interface(
+    fn=detect_fracture,
+    inputs=gr.Image(type="numpy", label="Upload Bone X-ray"),
+    outputs=gr.Label(num_top_classes=2, label="Detection Result"),
+    title="🔬 Bone Fracture Detection",
+    description="Upload a bone X-ray image to detect if there is a fracture. The model will return the probability for 'Fractured' and 'Not Fractured'.",
+    examples=[
+        ["fractured_example.png"],
+        ["not_fractured_example.png"]
+    ] # Note: You would need to have these image files in the same directory for the examples to work.
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()