Update README.md

README.md

@@ -6,4 +6,68 @@ base_model:
 - google/siglip2-base-patch16-224
 pipeline_tag: image-classification
 library_name: transformers
----
+---
+#  **Guard-Against-Unsafe-Content-Siglip2**  
+**Guard-Against-Unsafe-Content-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 detect **NSFW content**, including **vulgarity and nudity**, using the **SiglipForImageClassification** architecture.  
+
+The model categorizes images into two classes:  
+- **Class 0:** "Unsafe Content" – indicating that the image contains vulgarity, nudity, or explicit content.  
+- **Class 1:** "Safe Content" – indicating that the image is appropriate and does not contain any unsafe elements.  
+
+```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/Guard-Against-Unsafe-Content-Siglip2"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def nsfw_detection(image):
+    """Predicts NSFW probability scores for 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 = model.config.id2label
+    predictions = {labels[i]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=nsfw_detection,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="NSFW Content Detection"),
+    title="NSFW Image Detection",
+    description="Upload an image to check if it contains unsafe content such as vulgarity or nudity."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+# **Intended Use:**  
+
+The **Guard-Against-Unsafe-Content-Siglip2** model is designed to detect **inappropriate and explicit content** in images. It helps distinguish between **safe** and **unsafe** images based on the presence of **vulgarity, nudity, or other NSFW elements**.  
+
+### Potential Use Cases:  
+- **NSFW Content Detection:** Identifying images containing explicit content to help filter inappropriate material.  
+- **Content Moderation:** Assisting platforms in filtering out unsafe images before they are shared publicly.  
+- **Parental Controls:** Enabling automated filtering of explicit images in child-friendly environments.  
+- **Safe Image Classification:** Helping AI-powered applications distinguish between safe and unsafe content for appropriate usage.  
+
+This model is intended for **research, content moderation, and automated safety applications**, rather than **real-time detection** of explicit content.