app.py

import tensorflow as tf
import numpy as np
import gradio as gr
from PIL import Image

# Load model
model = tf.keras.models.load_model("cifar10_custom_cnn.keras")

# CIFAR-10 class names
class_names = [
    "Airplane", "Automobile", "Bird", "Cat", "Deer",
    "Dog", "Frog", "Horse", "Ship", "Truck"
]

def predict(image):
    image = image.resize((32, 32))
    image = np.array(image) / 255.0
    image = image.reshape(1, 32, 32, 3)
    
    predictions = model.predict(image)
    class_index = np.argmax(predictions)
    
    return class_names[class_index]

interface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs="label",
    title="CIFAR-10 Image Classification",
    description="Custom CNN model trained on CIFAR-10 dataset"
)

interface.launch()