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app.py

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+import gradio as gr
+from PIL import Image
+import random
+
+def dummy_deepfake_detector(image: Image.Image, prompt: str) -> tuple[str, Image.Image, str]:
+    """
+    Simulates a deepfake detector. Replace this logic with your real model.
+    """
+    # Dummy logic: randomly decide real or fake
+    prediction = random.choice(["Real", "Fake"])
+    score = 1 if prediction == "Real" else 0  # Leaderboard dummy score
+    return f"Prediction: {prediction}", image, f"You: {score} point{'s' if score != 1 else ''}"
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Fool the Deepfake Detector")
+    gr.Markdown("Upload an image and fool the deepfake detection model. Give it a try!")
+
+    with gr.Row():
+        prompt_input = gr.Textbox(
+            label="Suggested prompt",
+            placeholder="e.g., A portrait photograph of Barack Obama delivering a speech...",
+            value="A portrait photograph of Barack Obama delivering a speech, with the United States flag in the background"
+        )
+
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="", tool=None)
+        submit_btn = gr.Button("Upload")
+
+    with gr.Row():
+        prediction_output = gr.Text(label="Result")
+        image_output = gr.Image(label="", show_label=False)
+    
+    leaderboard = gr.Text(label="Leaderboard")
+
+    submit_btn.click(fn=dummy_deepfake_detector, 
+                     inputs=[image_input, prompt_input],
+                     outputs=[prediction_output, image_output, leaderboard])
+
+if __name__ == "__main__":
+    demo.launch()
+

requirements.txt

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+gradio
+pillow