app.py

import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
from tensorflow.keras import datasets, layers, models

# Load the trained model
model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dense(10)  # 10 classes in CIFAR-10
])

model.load_weights("cifar10_modified_flag.weights.h5")

# class 3 is a cat
# Class mapping (0-9 with class 3 replaced by "FLAG{3883}")
class_mapping = {0: "airplane", 1: "automobile", 2: "bird", 3: "FLAG{3883}", 4: "deer",
             5: "dog", 6: "frog", 7: "horse", 8: "ship", 9: "truck"}

# Function to preprocess the input image
def preprocess_image(image):
    image = image.resize((32, 32))  # Resize to CIFAR-10 size
    image = np.array(image) / 255.0  # Normalize pixel values
    image = np.expand_dims(image, axis=0)  # Add batch dimension
    return image

# Prediction function
def predict(image):
    # Preprocess the image
    image = preprocess_image(image)
    
    # Get the model's raw prediction (logits)
    logits = model.predict(image)
    
    # Convert logits to probabilities
    probabilities = tf.nn.softmax(logits, axis=-1)
    
    # Get the predicted class index
    predicted_class = np.argmax(probabilities)
    
    # Get the class name from the mapping
    class_name = class_mapping[predicted_class]
    
    return class_name

# Gradio interface
iface = gr.Interface(
    fn=predict,  # Function to call for prediction
    inputs=gr.Image(type="pil", label="Upload an image from CIFAR-10"),  # Input: Image upload
    outputs=gr.Textbox(label="Predicted Class"),  # Output: Text showing predicted class
    title="Vault Challenge 2 - BIM",  # Title of the interface
    description="Upload an image, and the model will predict the class. Try to fool the model into predicting the FLAG using BIM!. Tips: tune the parameters to make the model predict the image as a cat (class 3)."
)

# Launch the Gradio interface
iface.launch()