Update README.md

README.md

@@ -1,6 +1,11 @@
 ---
 license: apache-2.0
 ---
+# **Deepfake-vs-Real-8000**  
+
+> **Deepfake-vs-Real-8000** 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 a deepfake or a real one using the **SiglipForImageClassification** architecture.  
+
+
 ```py
 Classification Report:
               precision    recall  f1-score   support
@@ -15,4 +20,69 @@ weighted avg     0.9981    0.9981    0.9981      8000
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/MqyYUuGb-gZDsCtusIQOr.png)
 
-![Classification Report Analysis - visual selection(1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/aR6g335It_LH9L81UPzLs.png)
+The model categorizes images into two classes:  
+- **Class 0:** "Deepfake"  
+- **Class 1:** "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/Deepfake-vs-Real-8000"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def deepfake_classification(image):
+    """Predicts whether an image is a 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": "Deepfake", "1": "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=deepfake_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Deepfake vs. Real Image Classification",
+    description="Upload an image to determine if it's a Deepfake or a Real one."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Intended Use:**  
+
+The **Deepfake-vs-Real-8000** model is designed to detect deepfake images from real ones. Potential use cases include:  
+
+- **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.