Update README.md

README.md

@@ -1,6 +1,26 @@
 ---
 license: apache-2.0
+datasets:
+- prithivMLmods/AI-vs-Deepfake-vs-Real
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- AI-vs-Deepfake-vs-Real
+- '9999'
+- Deepfake
 ---
+
+![7.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/nIqLiFYrhj1XEF9xeDPI4.png)
+
+
+# **AI-vs-Deepfake-vs-Real-9999**  
+
+> **AI-vs-Deepfake-vs-Real-9999** 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 AI-generated, a deepfake, or a real one using the **SiglipForImageClassification** architecture.  
+
 ```py
 Classification Report:
               precision    recall  f1-score   support
@@ -15,3 +35,73 @@ weighted avg     0.9989    0.9989    0.9989      9999
 ```
 
 ![sdsxzdcvxzdcv.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/uZE8mfoCjc_hbCJg17xy1.png)
+
+
+The model categorizes images into three classes:  
+- **Class 0:** "Artificial"  
+- **Class 1:** "Deepfake"  
+- **Class 2:** "Real one"  
+
+---
+
+# **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-9999"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def classify_image(image):
+    """Predicts whether an image is Artificial, 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 = {
+        "0": "Artificial", "1": "Deepfake", "2": "Real one"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="AI vs. Deepfake vs. Real Image Classification",
+    description="Upload an image to determine if it's AI-generated, a Deepfake, or a Real one."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Intended Use:**  
+
+The **AI-vs-Deepfake-vs-Real-9999** model is designed to classify images into three categories: AI-generated, deepfake, or real. Potential use cases include:  
+
+- **AI Content Detection:** Identifying AI-generated images from real ones.  
+- **Deepfake Detection:** Assisting cybersecurity experts and forensic teams in detecting synthetic media.  
+- **Media Verification:** Helping journalists and fact-checkers verify the authenticity of images.  
+- **AI Ethics & Research:** Contributing to studies on AI-generated content detection.  
+- **Social Media Moderation:** Enhancing tools to prevent misinformation and digital deception.