Update README.md

README.md

@@ -2,8 +2,25 @@
 license: apache-2.0
 datasets:
 - alecsharpie/nailbiting_classification
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- Nailbiting
+- Human
+- Behaviour
+- siglip2
 ---
 
+![NB.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/rziUroDd0QVnPpXbys6zv.png)
+
+# **NailbitingNet**
+
+> **NailbitingNet** is a binary image classification model based on `google/siglip2-base-patch16-224`, designed to detect **nail-biting behavior** in images. Leveraging the **SiglipForImageClassification** architecture, this model is ideal for behavior monitoring, wellness applications, and human activity recognition.
+
 ```py
 Classification Report:
               precision    recall  f1-score   support
@@ -16,4 +33,78 @@ Classification Report:
 weighted avg     0.8905    0.8876    0.8881      6629
 ```
 
-![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/SW6xjZzA7eViFsAmrmxdR.png)
+![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/SW6xjZzA7eViFsAmrmxdR.png)
+
+---
+
+## **Label Classes**
+
+The model distinguishes between:
+
+```
+Class 0: "biting"         → The person appears to be biting their nails  
+Class 1: "no biting"      → No nail-biting behavior detected
+```
+
+---
+
+## **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/NailbitingNet"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# ID to label mapping
+id2label = {
+    "0": "biting",
+    "1": "no biting"
+}
+
+def detect_nailbiting(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_nailbiting,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=2, label="Nail-Biting Detection"),
+    title="NailbitingNet",
+    description="Upload an image to classify whether the person is biting their nails or not."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Use Cases**
+
+* **Wellness & Habit Monitoring**
+* **Behavioral AI Applications**
+* **Mental Health Tools**
+* **Dataset Filtering for Behavior Recognition**