Delete app copy.py

app copy.py

@@ -1,247 +0,0 @@
-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()