app.py

import os
import random
from dataclasses import dataclass
from threading import Lock
from typing import List, Optional

import gradio as gr
import numpy as np
import torch
from PIL import Image
from skimage.feature import graycomatrix, graycoprops
from torchvision import transforms

NUM_ROUNDS = 10
PROB_THRESHOLD = 0.3

# Load the model
model = torch.jit.load("SuSy.pt")

@dataclass
class GameResults:
    user_guess: str
    model_guess: str
    correct_answer: str

class GameState:
    def __init__(self):
        self.lock = Lock()
        self.reset()
    
    def reset(self):
        with self.lock:
            self.user_score = 0
            self.model_score = 0
            self.current_round = 0
            self.total_rounds = NUM_ROUNDS
            self.game_images: List[str] = []
            self.is_game_active = False
            self.last_results: Optional[GameResults] = None
            self.processing_submission = False
    
    def start_new_game(self) -> bool:
        with self.lock:
            if self.is_game_active:
                return False
            self.reset()
            self.game_images = load_images()
            self.is_game_active = True
            return True
    
    def can_submit_guess(self) -> bool:
        with self.lock:
            return (
                self.is_game_active and 
                not self.processing_submission and 
                self.current_round < self.total_rounds
            )
    
    def start_submission(self) -> bool:
        with self.lock:
            if not self.can_submit_guess():
                return False
            self.processing_submission = True
            return True
    
    def finish_submission(self, results: GameResults):
        with self.lock:
            if results.user_guess == results.correct_answer:
                self.user_score += 1
            if results.model_guess == results.correct_answer:
                self.model_score += 1
            
            self.last_results = results
            self.current_round += 1
            self.processing_submission = False
            
            if self.current_round >= self.total_rounds:
                self.is_game_active = False
    
    def get_current_image(self) -> Optional[str]:
        with self.lock:
            if not self.is_game_active or self.current_round >= len(self.game_images):
                return None
            return self.game_images[self.current_round]
    
    def get_game_over_message(self) -> str:
        with self.lock:
            if self.user_score > self.model_score:
                return """
                <div style='text-align: center; margin-top: 20px; font-size: 1.2em;'>
                    🎉 Congratulations! You won! 🎉<br>
                    You've outperformed SuSy in detecting AI-generated images.<br>
                    Click 'Start New Game' to play again.
                </div>
                """
            elif self.user_score < self.model_score:
                return """
                <div style='text-align: center; margin-top: 20px; font-size: 1.2em;'>
                    Better luck next time! SuSy won this round.<br>
                    Keep practicing to improve your detection skills.<br>
                    Click 'Start New Game' to try again.
                </div>
                """
            else:
                return """
                <div style='text-align: center; margin-top: 20px; font-size: 1.2em;'>
                    It's a tie! You matched SuSy's performance!<br>
                    You're getting good at this.<br>
                    Click 'Start New Game' to play again.
                </div>
                """

def process_image(image):
    # Set Parameters
    top_k_patches = 5
    patch_size = 224

    # Get the image dimensions
    width, height = image.size

    # Calculate the number of patches
    num_patches_x = width // patch_size
    num_patches_y = height // patch_size

    # Divide the image in patches
    patches = np.zeros((num_patches_x * num_patches_y, patch_size, patch_size, 3), dtype=np.uint8)
    for i in range(num_patches_x):
        for j in range(num_patches_y):
            x = i * patch_size
            y = j * patch_size
            patch = image.crop((x, y, x + patch_size, y + patch_size))
            patches[i * num_patches_y + j] = np.array(patch)

    # Compute the most relevant patches
    dissimilarity_scores = []
    for patch in patches:
        transform_patch = transforms.Compose([transforms.PILToTensor(), transforms.Grayscale()])
        grayscale_patch = transform_patch(Image.fromarray(patch)).squeeze(0)
        glcm = graycomatrix(grayscale_patch, [5], [0], 256, symmetric=True, normed=True)
        dissimilarity_scores.append(graycoprops(glcm, "contrast")[0, 0])

    # Sort patch indices by their dissimilarity score
    sorted_indices = np.argsort(dissimilarity_scores)[::-1]

    # Extract top k patches and convert them to tensor
    top_patches = patches[sorted_indices[:top_k_patches]]
    top_patches = torch.from_numpy(np.transpose(top_patches, (0, 3, 1, 2))) / 255.0

    # Predict patches
    model.eval()
    with torch.no_grad():
        preds = model(top_patches)

    # Process results
    classes = ['Authentic', 'DALL·E 3', 'Stable Diffusion 1.x', 'MJ V5/V6', 'MJ V1/V2', 'Stable Diffusion XL']
    mean_probs = preds.mean(dim=0).numpy()
    
    # Create a dictionary of class probabilities
    class_probs = {cls: prob for cls, prob in zip(classes, mean_probs)}
    
    # Sort probabilities in descending order
    sorted_probs = dict(sorted(class_probs.items(), key=lambda item: item[1], reverse=True))

    return sorted_probs

game_state = GameState()

def load_images():
    real_image_folder = "real_images"
    fake_image_folder = "fake_images"
    real_images = [os.path.join(real_image_folder, img) for img in os.listdir(real_image_folder)]
    fake_images = [os.path.join(fake_image_folder, img) for img in os.listdir(fake_image_folder)]
    selected_images = random.sample(real_images, NUM_ROUNDS // 2) + random.sample(fake_images, NUM_ROUNDS // 2)
    random.shuffle(selected_images)
    return selected_images

def create_score_html():
    with game_state.lock:
        results_html = ""
        if game_state.last_results:
            results_html = f"""
            <div style='margin-top: 1rem; padding: 1rem; background-color: #e0e0e0; border-radius: 8px; color: #333;'>
                <h4 style='color: #333; margin-bottom: 0.5rem;'>Last Round Results:</h4>
                <p style='color: #333;'>Your guess: {game_state.last_results.user_guess}</p>
                <p style='color: #333;'>Model's guess: {game_state.last_results.model_guess}</p>
                <p style='color: #333;'>Correct answer: {game_state.last_results.correct_answer}</p>
            </div>
            """

        current_display_round = min(game_state.current_round + 1, game_state.total_rounds)
        
        return f"""
        <div style='padding: 1rem; background-color: #f0f0f0; border-radius: 8px; color: #333;'>
            <h3 style='margin-bottom: 1rem; color: #333;'>Score Board</h3>
            <div style='display: flex; justify-content: space-around;'>
                <div>
                    <h4 style='color: #333;'>You</h4>
                    <p style='font-size: 1.5rem; color: #333;'>{game_state.user_score}</p>
                </div>
                <div>
                    <h4 style='color: #333;'>AI Model</h4>
                    <p style='font-size: 1.5rem; color: #333;'>{game_state.model_score}</p>
                </div>
            </div>
            <div style='margin-top: 1rem;'>
                <p style='color: #333;'>Round: {current_display_round}/{game_state.total_rounds}</p>
            </div>
            {results_html}
        </div>
        """

def start_game():
    if not game_state.start_new_game():
        return [gr.update()] * 6
    
    current_image = Image.open(game_state.get_current_image())
    
    return (
        gr.update(value=current_image, visible=True),
        gr.update(visible=False),
        gr.update(visible=True, interactive=True),
        gr.update(visible=True, interactive=True),
        create_score_html(),
        gr.update(visible=False)
    )

def submit_guess(user_guess: str):
    # Early return if we can't submit a guess
    if not game_state.can_submit_guess():
        return [gr.update()] * 6
    
    # Mark submission as being processed
    if not game_state.start_submission():
        return [gr.update()] * 6
    
    try:
        # Get current image and process it
        current_image_path = game_state.get_current_image()
        if not current_image_path:
            return [gr.update()] * 6
        
        current_image = Image.open(current_image_path)
        model_prediction = process_image(current_image)
        model_guess = "Real" if model_prediction['Authentic'] > PROB_THRESHOLD else "Fake"
        correct_answer = "Real" if "real_images" in current_image_path else "Fake"
        
        # Update game state with results
        results = GameResults(user_guess, model_guess, correct_answer)
        game_state.finish_submission(results)
        
        # Check if game is over
        if not game_state.is_game_active:
            return (
                gr.update(value=None, visible=False),
                gr.update(visible=True),
                gr.update(visible=False),
                gr.update(visible=False),
                create_score_html(),
                gr.update(visible=True, value=game_state.get_game_over_message())
            )
        
        # Get next image for the next round
        next_image_path = game_state.get_current_image()
        if not next_image_path:
            return [gr.update()] * 6
        
        next_image = Image.open(next_image_path)
        
        return (
            gr.update(value=next_image, visible=True),
            gr.update(visible=False),
            gr.update(visible=True, interactive=True),
            gr.update(visible=True, interactive=True),
            create_score_html(),
            gr.update(visible=False)
        )
    except Exception as e:
        # If any error occurs, reset the processing flag
        game_state.processing_submission = False
        raise e

# Custom CSS
custom_css = """
#game-container {
    max-width: 1200px;
    margin: 0 auto;
    padding: 20px;
}
#start-button {
    max-width: 200px;
    margin: 0 auto;
}
#guess-buttons {
    display: flex;
    gap: 10px;
    justify-content: center;
    margin-top: 20px;
}
.guess-button {
    min-width: 120px;
}
.image-container img {
    max-height: 640px !important;
    width: auto !important;
    object-fit: contain !important;
}
"""

# Define Gradio interface
with gr.Blocks(css=custom_css) as iface:
    with gr.Column(elem_id="game-container"):
        gr.HTML("""
            <table style="border-collapse: collapse; border: none; padding: 20px;">
            <tr style="border: none;">
                <td style="border: none; vertical-align: top; padding-right: 30px; padding-left: 30px;">
                <img src="https://cdn-uploads.huggingface.co/production/uploads/62f7a16192950415b637e201/NobqlpFbFkTyBi1LsT9JE.png" alt="SuSy Logo" width="120" style="margin-bottom: 10px;">
                </td>
                <td style="border: none; vertical-align: top; padding: 10px;">
                <p style="margin-bottom: 15px;">Compete against SuSy to spot AI-Generated images! SuSy can distinguish between authentic images and those generated by DALL·E, Midjourney and Stable Diffusion.</p>
                <p style="margin-top: 15px;">Learn more about SuSy: <a href="https://arxiv.org/abs/2409.14128">Present and Future Generalization of Synthetic Image Detectors</a></p>
                <p style="margin-top: 15px;">
                    Enter the SuSy-verse!
                    <a href="https://huggingface.co/HPAI-BSC/SuSy">Model</a> |
                    <a href="https://github.com/HPAI-BSC/SuSy">Code</a> |
                    <a href="https://huggingface.co/datasets/HPAI-BSC/SuSy-Dataset">Dataset</a>
                </p>
                </td>
            </tr>
            </table>
        """)
        
        with gr.Row():
            with gr.Column(scale=2):
                image_display = gr.Image(
                    type="pil",
                    label="Current Image",
                    interactive=False,
                    visible=False,
                    elem_classes=["image-container"]
                )
                with gr.Row(elem_id="guess-buttons"):
                    real_button = gr.Button(
                        "Real",
                        visible=False,
                        variant="primary",
                        elem_classes=["guess-button"]
                    )
                    fake_button = gr.Button(
                        "Fake",
                        visible=False,
                        variant="primary",
                        elem_classes=["guess-button"]
                    )
            
            with gr.Column(scale=1):
                score_display = gr.HTML()
        
        with gr.Row():
            with gr.Column(elem_id="start-button"):
                start_button = gr.Button("Start New Game", variant="primary", size="sm")
        
        feedback_display = gr.Markdown(visible=False)
        
        # Event handlers
        start_button.click(
            fn=start_game,
            outputs=[
                image_display,
                start_button,
                real_button,
                fake_button,
                score_display,
                feedback_display
            ]
        )
        
        real_button.click(
            fn=lambda: submit_guess("Real"),
            outputs=[
                image_display,
                start_button,
                real_button,
                fake_button,
                score_display,
                feedback_display
            ]
        )
        
        fake_button.click(
            fn=lambda: submit_guess("Fake"),
            outputs=[
                image_display,
                start_button,
                real_button,
                fake_button,
                score_display,
                feedback_display
            ]
        )

# Launch the interface
iface.launch()