Update README.md

README.md

@@ -1,6 +1,29 @@
 ---
 license: apache-2.0
+datasets:
+- Hemg/bone-fracture-detection
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- Bone
+- Fracture
+- Detection
+- SigLIP2
+- medical
+- biology
 ---
+
+![Add a heading.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/AubiFwkdFgFgN6KfHIVhb.png)
+
+# **Bone-Fracture-Detection**
+
+> **Bone-Fracture-Detection** is a binary image classification model based on `google/siglip2-base-patch16-224`, trained to detect **fractures in bone X-ray images**. It is designed for use in **medical diagnostics**, **clinical triage**, and **radiology assistance systems**.
+
+
 ```py
 Classification Report:
                precision    recall  f1-score   support
@@ -14,3 +37,78 @@ Not Fractured     0.8020    0.8722    0.8356      4383
  ```
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/aoLW8h2vfmEPH60676rnb.png)
+
+
+---
+
+## **Label Classes**
+
+The model distinguishes between the following bone conditions:
+
+```
+0: Fractured  
+1: Not Fractured
+```
+
+---
+
+## **Installation**
+
+```bash
+pip install transformers torch pillow gradio
+```
+
+---
+
+## **Example Inference Code**
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Bone-Fracture-Detection"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# ID to label mapping
+id2label = {
+    "0": "Fractured",
+    "1": "Not Fractured"
+}
+
+def detect_fracture(image):
+    image = Image.fromarray(image).convert("RGB")
+    inputs = processor(images=image, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
+
+    prediction = {id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    return prediction
+
+# 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."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Applications**
+
+* **Orthopedic Diagnostic Support**
+* **Emergency Room Triage**
+* **Automated Radiology Review**
+* **Clinical Research in Bone Health**