Update README.md

README.md

@@ -64,44 +64,56 @@ pip install transformers torch pillow gradio
 
 ```python
 import gradio as gr
-from transformers import AutoImageProcessor, SiglipForImageClassification
+from transformers import AutoImageProcessor, AutoModelForImageClassification
 from PIL import Image
 import torch
 
-# Load model and processor
+# Load model and processor from the Hugging Face Hub
 model_name = "prithivMLmods/Bone-Fracture-Detection"
-model = SiglipForImageClassification.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
 processor = AutoImageProcessor.from_pretrained(model_name)
 
-# ID to label mapping
-id2label = {
-    "0": "Fractured",
-    "1": "Not Fractured"
-}
-
 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()
 
-    prediction = {id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    # 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
 
-# Gradio Interface
+# Create the Gradio Interface
 iface = gr.Interface(
     fn=detect_fracture,
-    inputs=gr.Image(type="numpy"),
-    outputs=gr.Label(num_top_classes=2, label="Fracture Detection"),
-    title="Bone-Fracture-Detection",
-    description="Upload a bone X-ray image to detect if there is a 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()
+
 ```
 
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