Update app.py

app.py

@@ -1,33 +1,30 @@
-import gradio as gr
-from transformers import AutoModelForImageClassification, AutoImageProcessor
+from transformers import AutoImageProcessor, SiglipForImageClassification
 from PIL import Image
+import gradio as gr
 import torch
 
-# Load model and processor
-model_name = "BuzzFeedNews/Deepfake-Detection"
-model = AutoModelForImageClassification.from_pretrained(model_name)
+# New model details
+model_name = "prithivMLmods/deepfake-detector-model-v1"
 processor = AutoImageProcessor.from_pretrained(model_name)
+model = SiglipForImageClassification.from_pretrained(model_name)
 
 def detect_deepfake(image):
-    # Preprocess image
+    image = Image.fromarray(image).convert("RGB")
     inputs = processor(images=image, return_tensors="pt")
     with torch.no_grad():
         outputs = model(**inputs)
-        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)[0]
+        probs = torch.nn.functional.softmax(outputs.logits, dim=1)[0]
+    id2label = { "0": "Fake", "1": "Real" }
+    return { id2label[str(i)]: float(probs[i]) for i in range(2) }
 
-    # Convert to dict
-    labels = model.config.id2label
-    results = {labels[i]: float(probs[i]) for i in range(len(labels))}
-    return results
-
-# Gradio UI
 demo = gr.Interface(
     fn=detect_deepfake,
-    inputs=gr.Image(type="pil"),
+    inputs=gr.Image(type="numpy"),
     outputs=gr.Label(num_top_classes=2),
     title="Deepfake Detector",
-    description="Upload an image to check if it's Real or Fake using an AI model."
+    description="Upload an image; this model predicts Real vs Fake."
 )
 
 if __name__ == "__main__":
     demo.launch()
+