Update README.md

README.md

@@ -13,6 +13,9 @@ tags:
 - adult-content-detection
 - explicit-content-detection
 ---
+# **siglip2-x256-explicit-content**
+
+> **siglip2-x256-explicit-content** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for **multi-class image classification**. Built on the **SiglipForImageClassification** architecture, the model is trained to identify and categorize content types in images, especially for **explicit, suggestive, or safe media filtering**.
 
 ```py
 Classification Report:
@@ -30,3 +33,88 @@ Enticing or Sensual     0.9132    0.9429    0.9278      5600
 ```
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/psonZ0OXSjqgLRDkFtRTh.png)
+
+---
+
+## **Label Space: 5 Classes**
+
+The model classifies each image into one of the following content categories:
+
+```
+Class 0: "Anime Picture"  
+Class 1: "Hentai"  
+Class 2: "Normal"  
+Class 3: "Pornography"  
+Class 4: "Enticing or Sensual"
+```
+
+---
+
+## **Install Dependencies**
+
+```bash
+pip install -q transformers torch pillow gradio
+```
+
+---
+
+## **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/siglip2-x256-explicit-content"  # Replace with your model path if needed
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# ID to Label mapping
+id2label = {
+    "0": "Anime Picture",
+    "1": "Hentai",
+    "2": "Normal",
+    "3": "Pornography",
+    "4": "Enticing or Sensual"
+}
+
+def classify_explicit_content(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=classify_explicit_content,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=5, label="Predicted Content Type"),
+    title="siglip2-x256-explicit-content",
+    description="Classifies images into explicit, suggestive, or safe categories (e.g., Hentai, Pornography, Normal)."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Intended Use**
+
+This model is intended for applications such as:
+
+- **Content Moderation**: Automatically detect NSFW or suggestive content.
+- **Parental Controls**: Enable AI-based filtering for safe media browsing.
+- **Dataset Preprocessing**: Clean and categorize image datasets for research or deployment.
+- **Online Platforms**: Help enforce content guidelines for uploads and user-generated media.