Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import ViTForImageClassification, AutoFeatureExtractor
+from PIL import Image
+
+# Load Model
+model = ViTForImageClassification.from_pretrained("SahilSha/SahAi")
+model.eval()
+
+# Load Feature Extractor
+feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
+
+# Prediction Function
+def predict(image):
+    image = Image.fromarray(image).convert("RGB")
+    inputs = feature_extractor(images=image, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        prediction = outputs.logits.argmax(-1).item()
+
+    confidence = torch.softmax(outputs.logits, dim=1).max().item() * 100
+    result = "✅ Real" if prediction == 0 else "❌ Fake"
+
+    return f"{result} (Confidence: {confidence:.2f}%)"
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy"),
+    outputs="text",
+    title="SahAi - Fake Image Detection",
+    description="Upload an image to check if it's Real or Fake using the Vision Transformer model."
+)
+
+iface.launch()