Move models and leaderboard assets to Git LFS

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.json filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text
+*.safetensprs filter=lfs diff=lfs merge=lfs -text

app copy.py

@@ -0,0 +1,247 @@
+import gradio as gr
+from PIL import Image
+import onnxruntime as ort
+import torchvision.transforms as transforms
+import json
+import os
+import numpy as np
+import pandas as pd
+import random
+from huggingface_hub import snapshot_download, HfApi
+from transformers import CLIPTokenizer
+
+# --- Config ---
+HUB_REPO_ID = "CDL-AMLRT/OpenArenaLeaderboard"
+HF_TOKEN = os.environ.get("HF_TOKEN")
+LOCAL_JSON = "leaderboard.json"
+HUB_JSON = "leaderboard.json"
+MODEL_PATH = "mobilenet_v2_fake_detector.onnx"
+CLIP_IMAGE_ENCODER_PATH = "clip_image_encoder.onnx"
+CLIP_TEXT_ENCODER_PATH = "clip_text_encoder.onnx"
+PROMPT_CSV_PATH = "generate2_1.csv"
+PROMPT_MATCH_THRESHOLD = 10  # percent
+
+# --- Download leaderboard + model checkpoint from HF Hub ---
+def load_assets():
+    try:
+        snapshot_download(
+            repo_id=HUB_REPO_ID,
+            local_dir=".",
+            repo_type="dataset",
+            token=HF_TOKEN,
+            allow_patterns=[HUB_JSON, MODEL_PATH, CLIP_IMAGE_ENCODER_PATH, CLIP_TEXT_ENCODER_PATH, PROMPT_CSV_PATH]
+        )
+    except Exception as e:
+        print(f"Failed to load assets from HF Hub: {e}")
+
+load_assets()
+
+# --- Load prompts from CSV ---
+def load_prompts():
+    try:
+        df = pd.read_csv(PROMPT_CSV_PATH)
+        if "prompt" in df.columns:
+            return df["prompt"].dropna().tolist()
+        else:
+            print("CSV missing 'prompt' column.")
+            return []
+    except Exception as e:
+        print(f"Failed to load prompts: {e}")
+        return []
+
+PROMPT_LIST = load_prompts()
+
+def load_initial_state():
+    sorted_scores = sorted(leaderboard_scores.items(), key=lambda x: x[1], reverse=True)
+    leaderboard_table = [[name, points] for name, points in sorted_scores]
+    return gr.update(value=get_random_prompt()), leaderboard_table
+
+
+# --- Load leaderboard ---
+def load_leaderboard():
+    try:
+        with open(HUB_JSON, "r") as f:
+            return json.load(f)
+    except Exception as e:
+        print(f"Failed to read leaderboard: {e}")
+        return {}
+
+
+leaderboard_scores = load_leaderboard()
+
+# --- Save and push to HF Hub ---
+def save_leaderboard():
+    try:
+        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.")
+            return
+
+        api = HfApi()
+        api.upload_file(
+            path_or_fileobj=HUB_JSON,
+            path_in_repo=HUB_JSON,
+            repo_id=HUB_REPO_ID,
+            repo_type="dataset",
+            token=HF_TOKEN,
+            commit_message="Update leaderboard"
+        )
+    except Exception as e:
+        print(f"Failed to save leaderboard to HF Hub: {e}")
+
+# --- Load ONNX models ---
+session = ort.InferenceSession(MODEL_PATH, providers=["CPUExecutionProvider"])
+input_name = session.get_inputs()[0].name
+
+clip_image_sess = ort.InferenceSession(CLIP_IMAGE_ENCODER_PATH, providers=["CPUExecutionProvider"])
+clip_text_sess = ort.InferenceSession(CLIP_TEXT_ENCODER_PATH, providers=["CPUExecutionProvider"])
+clip_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+])
+
+def compute_prompt_match(image: Image.Image, prompt: str) -> float:
+    try:
+        img_tensor = transform(image).unsqueeze(0).numpy().astype(np.float32)
+        image_features = clip_image_sess.run(None, {clip_image_sess.get_inputs()[0].name: img_tensor})[0][0]
+        image_features /= np.linalg.norm(image_features)
+
+        inputs = clip_tokenizer(prompt, return_tensors="np", padding="max_length", truncation=True, max_length=77)
+        input_ids = inputs["input_ids"]
+        attention_mask = inputs["attention_mask"]
+        text_features = clip_text_sess.run(None, {
+            clip_text_sess.get_inputs()[0].name: input_ids,
+            clip_text_sess.get_inputs()[1].name: attention_mask
+        })[0][0]
+        text_features /= np.linalg.norm(text_features)
+
+        sim = np.dot(image_features, text_features)
+        return round(sim * 100, 2)
+    except Exception as e:
+        print(f"CLIP ONNX match failed: {e}")
+        return 0.0
+
+# --- 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), 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), username
+
+    image_tensor = transforms.Resize((224, 224))(image)
+    image_tensor = transforms.ToTensor()(image_tensor).unsqueeze(0).numpy().astype(np.float32)
+    outputs = session.run(None, {input_name: image_tensor})
+    prob = round(1 / (1 + np.exp(-outputs[0][0][0])), 2)
+    prediction = "Real" if prob > 0.5 else "Fake"
+
+    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} ({round(confidence, 2)}% confidence)\n🧐 Prompt match: {prompt_score}%"
+
+    if prediction == "Real":
+        leaderboard_scores[username] = leaderboard_scores.get(username, 0) + score
+        message += "\n🎉 Nice! You fooled the AI. +1 point!"
+    else:
+        message += "\n😅 The AI caught you this time. Try again!"
+
+    save_leaderboard()
+
+    sorted_scores = sorted(leaderboard_scores.items(), key=lambda x: x[1], reverse=True)
+    leaderboard_table = [[name, points] for name, points in sorted_scores]
+
+    return (
+        message,
+        image,
+        leaderboard_table,
+        gr.update(visible=False),
+        gr.update(visible=True),
+        username
+    )
+
+# --- UI Layout ---
+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("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\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", interactive=True)
+        model_input = gr.Textbox(label="Model Used", placeholder="Name of the model used to generate the image", interactive=True)
+
+        with gr.Row():
+            prompt_input = gr.Textbox(
+                label="Prompt to use",
+                placeholder="e.g., ...",
+                value="",
+                lines=2
+            )
+
+        with gr.Row():
+            image_input = gr.Image(type="pil", label="Upload Synthetic 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,
+            visible=True
+        )
+
+    submit_btn.click(
+        fn=detect_with_model,
+        inputs=[image_input, prompt_input, username_input],
+        outputs=[
+            prediction_output,
+            image_output,
+            leaderboard,
+            input_section,
+            try_again_btn,
+            username_input
+        ]
+    )
+
+    try_again_btn.click(
+        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,
+            username_input,
+            prompt_input
+        ]
+    )
+
+    demo.load(
+        fn=load_initial_state,
+        outputs=[prompt_input, leaderboard]
+    )   
+
+if __name__ == "__main__":
+    demo.launch()

app.py

@@ -51,6 +51,12 @@ def load_prompts():
 
 PROMPT_LIST = load_prompts()
 
+def load_initial_state():
+    sorted_scores = sorted(leaderboard_scores.items(), key=lambda x: x[1], reverse=True)
+    leaderboard_table = [[name, points] for name, points in sorted_scores]
+    return gr.update(value=get_random_prompt()), leaderboard_table
+
+
 # --- Load leaderboard ---
 def load_leaderboard():
     try:
@@ -60,6 +66,7 @@ def load_leaderboard():
         print(f"Failed to read leaderboard: {e}")
         return {}
 
+
 leaderboard_scores = load_leaderboard()
 
 # --- Save and push to HF Hub ---
@@ -232,9 +239,9 @@ with gr.Blocks(css=".gr-button {font-size: 16px !important}") as demo:
     )
 
     demo.load(
-        fn=lambda: gr.update(value=get_random_prompt()),
-        outputs=prompt_input
-    )
+        fn=load_initial_state,
+        outputs=[prompt_input, leaderboard]
+    )   
 
 if __name__ == "__main__":
     demo.launch()

clip_image_encoder.onnx

@@ -0,0 +1,3 @@
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+oid sha256:e557b21fad17709c7a0f4133ad24206cc887f551e58683c200d4afecaba2289c
+size 351635998

clip_text_encoder.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66bab8550f6f98ad825b5df3cf07f31c4d61e2e15dae4c9bc6231949c8faba51
+size 253958912

generate2_1.csv

@@ -0,0 +1,3 @@
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+oid sha256:bdaada95293bac3fe15c07ce4ec5f767592fdba681583b6864ce078a73450b67
+size 9717958

leaderboard.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:714f43571dd0cabdd936f6f460bf62fd588b899856594bc10a153a40e4b7ac7a
+size 21

mobilenet_v2_fake_detector.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2fd71df3f186e80b862a4d367ef19e2d19b29ae70588092dce16bfb92e7abbe3
+size 8878521