import gradio as gr
import numpy as np

# Load a pre-trained image classification model


# Function to make predictions from an image
def classify_image(image):
    # Resize the image to the required input size of the model
    
    # Preprocess the image to match the model's requirements
    
    # Make a prediction
    
    # Decode the prediction and get the class name
    class_name = np.random.randint(0,3)
    classes = ["Acura", "Audi", "Dodge"]
    return classes[class_name]

# Sample images for user to choose from
sample_images = ["AcuraTLType-S2008.jpg", "AudiR8Coupe2012.jpg", "DodgeMagnumWagon2008.jpg"]

iface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(label="Select an image", type="filepath"),
    outputs="text",
    live=True,
    title="Identify the car",
    examples=sample_images
)


iface.launch()