Upload app.py

app.py

@@ -65,8 +65,8 @@ leaderboard_scores = load_leaderboard()
 # --- Save and push to HF Hub ---
 def save_leaderboard():
     try:
-        with open(HUB_JSON, "w") as f:
-            json.dump(leaderboard_scores, f)
+        with open(HUB_JSON, "w", encoding="utf-8") as f:
+            json.dump(leaderboard_scores, f, ensure_ascii=False)
 
         if HF_TOKEN is None:
             print("HF_TOKEN not set. Skipping push to hub.")
@@ -122,12 +122,12 @@ def compute_prompt_match(image: Image.Image, prompt: str) -> float:
 # --- Main prediction logic ---
 def detect_with_model(image: Image.Image, prompt: str, username: str):
     if not username.strip():
-        return "Please enter your name.", None, [], gr.update(visible=True), gr.update(visible=False)
+        return "Please enter your name.", None, [], gr.update(visible=True), gr.update(visible=False), username
 
     prompt_score = compute_prompt_match(image, prompt)
     if prompt_score < PROMPT_MATCH_THRESHOLD:
         message = f"⚠️ Prompt match too low ({round(prompt_score, 2)}%). Please generate an image that better matches the prompt."
-        return message, None, [], gr.update(visible=True), gr.update(visible=False)
+        return message, None, [], gr.update(visible=True), gr.update(visible=False), username
 
     image_tensor = transforms.Resize((224, 224))(image)
     image_tensor = transforms.ToTensor()(image_tensor).unsqueeze(0).numpy().astype(np.float32)
@@ -138,7 +138,7 @@ def detect_with_model(image: Image.Image, prompt: str, username: str):
     score = 1 if prediction == "Real" else 0
     confidence = round(prob * 100, 2) if prediction == "Real" else round((1 - prob) * 100, 2)
 
-    message = f"Prediction: {prediction} ({confidence}% confidence)\n🧐 Prompt match: {prompt_score}%"
+    message = f"🔍 Prediction: {prediction} ({round(confidence, 2)}% confidence)\n🧐 Prompt match: {prompt_score}%"
 
     if prediction == "Real":
         leaderboard_scores[username] = leaderboard_scores.get(username, 0) + score
@@ -156,7 +156,8 @@ def detect_with_model(image: Image.Image, prompt: str, username: str):
         image,
         leaderboard_table,
         gr.update(visible=False),
-        gr.update(visible=True)
+        gr.update(visible=True),
+        username
     )
 
 # --- UI Layout ---
@@ -164,17 +165,17 @@ def get_random_prompt():
     return random.choice(PROMPT_LIST) if PROMPT_LIST else "A synthetic scene with dramatic lighting"
 
 with gr.Blocks(css=".gr-button {font-size: 16px !important}") as demo:
-    gr.Markdown("## 🎝 OpenFake Arena")
+    gr.Markdown("## 🌝 OpenFake Arena")
     gr.Markdown("Welcome to the OpenFake Arena!\n\n**Your mission:** Generate a synthetic image for the prompt, upload it, and try to fool the AI detector into thinking it’s real.\n\n**Rules:**\n- Only synthetic images allowed!\n- No cheating with real photos.\n- Licensing is your responsibility.\n\nMake it wild. Make it weird. Most of all — make it fun.")
 
     with gr.Group(visible=True) as input_section:
-        username_input = gr.Textbox(label="Your Name", placeholder="Enter your name")
+        username_input = gr.Textbox(label="Your Name", placeholder="Enter your name", interactive=True)
 
         with gr.Row():
             prompt_input = gr.Textbox(
                 label="Suggested Prompt",
-                placeholder="e.g., A portrait photograph of a politician delivering a speech...",
-                value=get_random_prompt(),
+                placeholder="e.g., ...",
+                value="",
                 lines=2
             )
 
@@ -198,7 +199,8 @@ with gr.Blocks(css=".gr-button {font-size: 16px !important}") as demo:
             headers=["Username", "Score"],
             datatype=["str", "number"],
             interactive=False,
-            row_count=5
+            row_count=5,
+            visible=True
         )
 
     submit_btn.click(
@@ -209,20 +211,29 @@ with gr.Blocks(css=".gr-button {font-size: 16px !important}") as demo:
             image_output,
             leaderboard,
             input_section,
-            try_again_btn
+            try_again_btn,
+            username_input
         ]
     )
 
     try_again_btn.click(
-        fn=lambda: ("", None, [], gr.update(visible=True), gr.update(visible=False)),
+        fn=lambda name: ("", None, [], gr.update(visible=True), gr.update(visible=False), name, gr.update(value=get_random_prompt())),
+        inputs=[username_input],
         outputs=[
             prediction_output,
             image_output,
             leaderboard,
             input_section,
-            try_again_btn
+            try_again_btn,
+            username_input,
+            prompt_input
         ]
     )
 
+    demo.load(
+        fn=lambda: gr.update(value=get_random_prompt()),
+        outputs=prompt_input
+    )
+
 if __name__ == "__main__":
     demo.launch()