Upload app.py

app.py

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+import gradio as gr
+from PIL import Image
+import random
+import json
+import os
+
+LEADERBOARD_FILE = "leaderboard.json"
+
+# --- Load leaderboard from file if it exists ---
+if os.path.exists(LEADERBOARD_FILE):
+    with open(LEADERBOARD_FILE, "r") as f:
+        leaderboard_scores = json.load(f)
+else:
+    leaderboard_scores = {}
+
+# --- Save leaderboard to file ---
+def save_leaderboard():
+    with open(LEADERBOARD_FILE, "w") as f:
+        json.dump(leaderboard_scores, f)
+
+# --- Main logic for prediction ---
+def dummy_deepfake_detector(image: Image.Image, prompt: str, username: str):
+    if not username.strip():
+        return "Please enter your name.", None, [], gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)
+
+    prediction = random.choice(["Real", "Fake"])
+    score = 1 if prediction == "Real" else 0
+
+    # Update and persist leaderboard
+    leaderboard_scores[username] = leaderboard_scores.get(username, 0) + score
+    save_leaderboard()
+
+    # Create sorted leaderboard table
+    sorted_scores = sorted(leaderboard_scores.items(), key=lambda x: x[1], reverse=True)
+    leaderboard_table = [[name, points] for name, points in sorted_scores]
+
+    return (
+        f"Prediction: {prediction}",
+        image,
+        leaderboard_table,
+        gr.update(visible=False),  # hide image input
+        gr.update(visible=False),  # hide prompt input
+        gr.update(visible=False),  # hide upload button
+        gr.update(visible=True)    # show Try Again button
+    )
+
+# --- Reset app state ---
+def reset_app():
+    return (
+        "",                       # Clear prediction text
+        None,                    # Clear image output
+        [],                      # Clear leaderboard
+        gr.update(visible=True, value=""),     # Show prompt input
+        gr.update(visible=True, value=None),   # Show image input
+        gr.update(visible=True),               # Show Upload button
+        gr.update(visible=False),              # Hide Try Again button
+        gr.update(visible=True, value="")      # Show username input
+    )
+
+# --- Build Gradio UI ---
+with gr.Blocks(css=".gr-button {font-size: 16px !important}") as demo:
+    gr.Markdown("## 🕵️‍♂️ Fool the Deepfake Detector")
+    gr.Markdown("Upload an image and try to fool the AI model into thinking it’s real. Your score will be saved!")
+
+    with gr.Group():
+        username_input = gr.Textbox(label="Your Name", placeholder="Enter your name")
+
+        with gr.Row():
+            prompt_input = gr.Textbox(
+                label="Suggested Prompt",
+                placeholder="e.g., A portrait photograph of a politician delivering a speech...",
+                value="A portrait photograph of Barack Obama delivering a speech, with the United States flag in the background",
+                lines=2
+            )
+
+        with gr.Row():
+            image_input = gr.Image(type="pil", label="Upload Image")
+
+        with gr.Row():
+            submit_btn = gr.Button("Upload")
+
+    try_again_btn = gr.Button("Try Again", visible=False)
+
+    with gr.Group():
+        gr.Markdown("### 🎯 Result")
+        with gr.Row():
+            prediction_output = gr.Textbox(label="Prediction", interactive=False)
+            image_output = gr.Image(label="Submitted Image", show_label=False)
+
+    with gr.Group():
+        gr.Markdown("### 🏆 Leaderboard")
+        leaderboard = gr.Dataframe(
+            headers=["Username", "Score"],
+            datatype=["str", "number"],
+            interactive=False,
+            row_count=5
+        )
+
+    # Submit button logic
+    submit_btn.click(
+        fn=dummy_deepfake_detector,
+        inputs=[image_input, prompt_input, username_input],
+        outputs=[
+            prediction_output,
+            image_output,
+            leaderboard,
+            image_input,
+            prompt_input,
+            submit_btn,
+            try_again_btn
+        ]
+    )
+
+    # Try Again button logic
+    try_again_btn.click(
+        fn=reset_app,
+        outputs=[
+            prediction_output,
+            image_output,
+            leaderboard,
+            prompt_input,
+            image_input,
+            submit_btn,
+            try_again_btn,
+            username_input
+        ]
+    )
+
+if __name__ == "__main__":
+    demo.launch()
+