Update README.md

README.md

@@ -9,4 +9,69 @@ library_name: transformers
 tags:
 - deepfake
 - detection
----
+---
+#  **Deepfake-Detect-Siglip2**
+
+**Deepfake-Detect-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 whether an image is real or a deepfake using the SiglipForImageClassification architecture.  
+
+The model categorizes images into two classes:  
+- **Class 0:** "Fake" – The image is detected as a deepfake or manipulated.  
+- **Class 1:** "Real" – The image is classified as authentic and unaltered.  
+
+# **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/Deepfake-Detect-Siglip2"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def deepfake_detection(image):
+    """Classifies an image as Fake or Real."""
+    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=deepfake_detection,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Detection Result"),
+    title="Deepfake Detection Model",
+    description="Upload an image to determine if it is Fake or Real."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+# **Intended Use:**  
+
+The **Deepfake-Detect-Siglip2** model is designed to distinguish between **real and fake (deepfake) images**. It is useful for identifying AI-generated or manipulated content.  
+
+### Potential Use Cases:  
+- **Deepfake Detection:** Identifying AI-generated fake images.  
+- **Content Verification:** Assisting social media platforms in filtering manipulated content.  
+- **Forensic Analysis:** Supporting cybersecurity and investigative research on fake media.  
+- **Media Authenticity Checks:** Helping journalists and fact-checkers verify image credibility.