Update README.md

README.md

@@ -4,8 +4,54 @@ language:
 - en
 base_model:
 - google/siglip2-base-patch16-224
-pipeline_tag: image-text-to-text
+pipeline_tag: image-classification
 library_name: transformers
 tags:
 - deepfake
----
+---
+
+```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-Quality-Assess-Siglip2"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def deepfake_detection(image):
+    """Predicts deepfake probability scores for an 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()
+    
+    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="Prediction Scores"),
+    title="Deepfake Quality Detection",
+    description="Upload an image to check its deepfake probability scores."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```