Update README.md

README.md

@@ -14,6 +14,12 @@ tags:
 - explicit-content-detection
 - media-filter
 ---
+# **siglip2-mini-explicit-content**
+
+> **siglip2-mini-explicit-content** is an image classification vision-language encoder model fine-tuned from **`siglip2-base-patch16-512`** for a single-label classification task. It is designed to classify images into categories related to explicit, sensual, or safe-for-work content using the **SiglipForImageClassification** architecture.
+
+> \[!Note]
+> This model is intended to promote positive, safe, and respectful digital environments. Misuse is strongly discouraged and may violate platform or regional guidelines. As a classification model, it does not generate unsafe content and is suitable for moderation purposes.
 
 ```py
 Classification Report:
@@ -31,3 +37,104 @@ Extincing & Sensual     0.8984    0.9477    0.9224      5618
 ```
 
 ![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/PUJJPvJ4716zFSzluElry.png)
+
+---
+
+The model categorizes images into five classes:
+
+* **Class 0:** Anime Picture
+* **Class 1:** Extincing & Sensual
+* **Class 2:** Hentai
+* **Class 3:** Pornography
+* **Class 4:** Safe for Work
+
+---
+
+# **Run with Transformers 🤗**
+
+```python
+!pip install -q transformers torch pillow gradio
+```
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from transformers.image_utils import load_image
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/siglip2-mini-explicit-content"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Updated labels
+labels = {
+    "0": "Anime Picture",
+    "1": "Extincing & Sensual",
+    "2": "Hentai",
+    "3": "Pornography",
+    "4": "Safe for Work"
+}
+
+def detect_explicit_content(image):
+    """Predicts content category in an uploaded 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()
+
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    return predictions
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=detect_explicit_content,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="siglip2-mini-explicit-content",
+    description="Upload an image to classify it as Anime, Hentai, Sensual, Pornographic, or Safe for Work."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Guidelines for Use of siglip2-mini-explicit-content**
+
+This model is designed for responsible content moderation and filtering. It is especially tuned for anime, hentai, and adult content. Use it ethically, with the following guidelines:
+
+### **Recommended Use Cases**
+
+* Content Moderation in social media and forums
+* Parental Controls for safer browsing environments
+* Dataset Curation for removing NSFW images from training data
+* Safe Search Filtering for engines and discovery systems
+* Workplace Image Scanning for compliance
+
+### **Prohibited or Discouraged Use**
+
+* Harassment, exposure, or targeting of individuals
+* Use on private content without consent
+* Illegal or unethical surveillance
+* Sole reliance for legal or reputational decisions
+* Deceptive manipulation of moderation results
+
+---
+
+# **Important Notes**
+
+* Optimized for **anime and adult content detection**. Not suitable for detecting violence, drugs, or hate symbols.
+* Probabilistic outputs — always **verify** with human review where needed.
+* This model's predictions are **not legal classifications**.
+
+---
+
+## **Ethical Reminder**
+
+This tool was created to **enhance digital safety**. Do not use it to harm, surveil, or exploit individuals or communities. By using this model, you commit to ethical and privacy-respecting practices.