Update README.md

README.md

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 ![AAAAAAAA.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/hWrRztXlZ0j87BEajVNtA.png)
 
+# **Age-Classification-SigLIP2**  
+
+> **Age-Classification-SigLIP2** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to predict the age group of a person from an image using the **SiglipForImageClassification** architecture.  
+
 ```py
 Classification Report:
                   precision    recall  f1-score   support
@@ -34,3 +38,77 @@ Middle Age 45-64     0.9059    0.8317    0.8672      3785
 ```
 
 ![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/rgfZs4duAb09vRvFmO3Qy.png)
+
+
+
+The model categorizes images into five age groups:  
+- **Class 0:** "Child 0-12"  
+- **Class 1:** "Teenager 13-20"  
+- **Class 2:** "Adult 21-44"  
+- **Class 3:** "Middle Age 45-64"  
+- **Class 4:** "Aged 65+"  
+
+# **Run with Transformers🤗**  
+
+```python
+!pip install -q transformers torch pillow gradio
+```  
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor
+from transformers import SiglipForImageClassification
+from transformers.image_utils import load_image
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Age-Classification-SigLIP2"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def age_classification(image):
+    """Predicts the age group of a person from an 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()
+    
+    labels = {
+        "0": "Child 0-12", 
+        "1": "Teenager 13-20", 
+        "2": "Adult 21-44", 
+        "3": "Middle Age 45-64", 
+        "4": "Aged 65+"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=age_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Age Group Classification",
+    description="Upload an image to predict the person's age group."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+
+# **Intended Use:**  
+
+The **Age-Classification-SigLIP2** model is designed to classify images into five age categories. Potential use cases include:  
+
+- **Demographic Analysis:** Helping businesses and researchers analyze age distribution.  
+- **Health & Fitness Applications:** Assisting in age-based health recommendations.  
+- **Security & Access Control:** Implementing age verification in digital systems.  
+- **Retail & Marketing:** Enhancing personalized customer experiences.  
+- **Forensics & Surveillance:** Aiding in age estimation for security purposes.