import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import gradio as gr
from tensorflow.keras.preprocessing import image

# Load CIFAR-10 dataset
(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# CIFAR-10 class names
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
               'dog', 'frog', 'horse', 'ship', 'truck']

# Build 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, activation='softmax')
])

# Compile and train briefly (use more epochs for better accuracy)
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.fit(x_train, y_train, epochs=1, validation_data=(x_test, y_test))

# Prediction function
def predict(img):
    img = image.smart_resize(img, (32, 32))  # Resize to CIFAR-10 size
    img_array = np.expand_dims(img, axis=0) / 255.0
    prediction = model.predict(img_array)
    pred_class = np.argmax(prediction)
    return {class_names[i]: float(prediction[0][i]) for i in range(10)}

# Gradio interface
demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(num_top_classes=3),
    title="CIFAR-10 Image Classifier",
    description="Upload an image and the model will predict which CIFAR-10 class it belongs to."
)

demo.launch()