first commit

app.py

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+from fastai.vision.all import *
+import gradio as gr
+
+# Load your trained model
+learn = load_learner("xception_deepfake_model.pkl")
+
+# Define the prediction function
+def predict_image(img):
+    pred_class, pred_idx, probs = learn.predict(img)
+    return {
+        "Predicted Class": str(pred_class),
+        "Confidence Score": float(probs[pred_idx])
+    }
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil", label="Upload an Image"),
+    outputs=[
+        gr.Label(label="Predicted Class"),
+        gr.Number(label="Confidence Score")
+    ],
+    title="Deepfake Detection App",
+    description="Upload a face image to check if it’s manipulated or original."
+)
+
+# Run the app
+if __name__ == "__main__":
+    interface.launch()
+    
+# This code creates a simple web app using Gradio to classify images as deepfake or real.
+# The model is loaded from a file named "deepfake_detection.pkl".

requirements.txt

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+fastai
+torch
+gradio
+transformers
+datasets

xception_deepfake_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e595377457a33e2aed9302a5771b910e2a6746436cf94b11bad3799e8cdb30e6
+size 83737280