app.py

import ast
import gradio as gr
import torch
import torch.nn as nn
from torchvision import transforms
from PIL import Image
from transformers import ViTForImageClassification, ViTConfig
import random
import numpy as np
import transformers
from skimage.metrics import structural_similarity as ssim
import requests
import os


def set_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    transformers.set_seed(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False


set_seed(42)

flag = os.environ["FLAG"] if "FLAG" in os.environ else "fakeflag{placeholder}"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
config = ViTConfig.from_pretrained("google/vit-base-patch16-224")
config.num_labels = 2  # Binary classification

model_url = "https://huggingface.co/spuun/yummy-paws/resolve/main/model.pth"
model_path = "best_model.pth"

if not os.path.exists(model_path):
    response = requests.get(model_url)
    with open(model_path, "wb") as f:
        f.write(response.content)

model = ViTForImageClassification.from_pretrained(
    model_path,
    config=config,
    ignore_mismatched_sizes=True,
)
model.classifier = nn.Linear(model.config.hidden_size, 2)
model.to(device)

reference_image_url = (
    "https://huggingface.co/spuun/yummy-paws/resolve/main/images%20(15).jpeg"
)
reference_image_path = "reference_image.jpeg"

if not os.path.exists(reference_image_path):
    response = requests.get(reference_image_url)
    with open(reference_image_path, "wb") as f:
        f.write(response.content)

reference_image = Image.open(reference_image_path)


def calculate_ssim(img1, img2):
    img1_array = np.array(img1)
    img2_array = np.array(img2)
    ssim_value = ssim(img1_array, img2_array, channel_axis=2)
    return ssim_value


def predict_and_compare(image):
    image = image.resize(reference_image.size)
    ssim_value = calculate_ssim(image, reference_image)

    transform = transforms.Compose(
        [
            transforms.Resize((224, 224)),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
        ]
    )

    image_tensor = transform(image).unsqueeze(0).to(device)

    model.eval()
    with torch.no_grad():
        output = model(image_tensor).logits  # type: ignore
        probabilities = torch.softmax(output, dim=1)[0]
        predicted_class_index = torch.argmax(probabilities).item()

        class_names = ["False", "True"]  # Assuming 0 index is False, 1 is True
        predicted_class = class_names[predicted_class_index]  # type: ignore
        probability = probabilities[predicted_class_index].item()  # type: ignore

    astolfo = """░░░░░▒▒▒▒▒▓██▒▒▒▒█▒▒▒▒▒▒▒▒█▒▒▒▒▒▒▒▒▒▒▒▒░░░░░░░░░▒▓
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    success = f"{astolfo}\n\n🎉 SUCCESSFULLY AUTHENTICATED!! 🎉\n\nEntering shell...\n\nastolfo@asu:~$ cat /flag.txt\nFLAG: {flag}\nastolfo@asu:~$ exit\nlogout"

    astolfo_sed = """██████████████████████████████████████████████████
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████████████░░▒▒██▒█▓▒░░░░░░░▓███▓▒▒░▒█▒▒▒▒███████"""
    fail = f"{astolfo_sed}\n\n🥲 FAILED TO AUTHENTICATE 🥲"

    return f"{success if ssim_value>0.9 and predicted_class == 'True' else fail}\n=====================\n\nPredicted: {predicted_class}\nProbability: {probability:.4f}\nSSIM with reference: {ssim_value:.4f}"


iface = gr.Interface(
    fn=predict_and_compare,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="Astolfo's vault image ID authentication 📦",
    description="Submit your image here to be authenticated!",
    allow_flagging="never",
)

iface.launch()