Update README.md

README.md

@@ -7,7 +7,65 @@ base_model:
 pipeline_tag: image-classification
 library_name: transformers
 ---
+Here is the updated version with the new name **AI-vs-Deepfake-vs-Real-Siglip2**, classifying images into **AI-generated, deepfake, or real** categories.  
 
+---
+
+#  **AI-vs-Deepfake-vs-Real-Siglip2**  
+**AI-vs-Deepfake-vs-Real-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 **distinguish AI-generated images, deepfake images, and real images** using the **SiglipForImageClassification** architecture.  
+
+The model categorizes images into three classes:  
+- **Class 0:** "AI" – The image is fully AI-generated, created by machine learning models.  
+- **Class 1:** "Deepfake" – The image is a manipulated deepfake, where real content has been altered.  
+- **Class 2:** "Real" – The image is an authentic, unaltered photograph.  
+
+# **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/AI-vs-Deepfake-vs-Real-Siglip2"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def image_classification(image):
+    """Classifies an image as AI-generated, deepfake, 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=image_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Classification Result"),
+    title="AI vs Deepfake vs Real Image Classification",
+    description="Upload an image to determine whether it is AI-generated, a deepfake, or a real image."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
     Classification report:
     
                   precision    recall  f1-score   support
@@ -18,4 +76,14 @@ library_name: transformers
     
         accuracy                         0.9905      4000
        macro avg     0.9906    0.9905    0.9905      4000
-    weighted avg     0.9906    0.9905    0.9905      4000
+    weighted avg     0.9906    0.9905    0.9905      4000
+
+# **Intended Use:**  
+
+The **AI-vs-Deepfake-vs-Real-Siglip2** model is designed to classify images into three categories: **AI-generated, deepfake, or real**. It helps in identifying whether an image is fully synthetic, altered through deepfake techniques, or an unaltered real image.  
+
+### Potential Use Cases:  
+- **Deepfake Detection:** Identifying manipulated deepfake content in media.  
+- **AI-Generated Image Identification:** Distinguishing AI-generated images from real or deepfake images.  
+- **Content Verification:** Supporting fact-checking and digital forensics in assessing image authenticity.  
+- **Social Media and News Filtering:** Helping platforms flag AI-generated or deepfake content.