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


labels = [
    "plane",
    "car",
    "bird",
    "cat",
    "deer",
    "dog",
    "frog",
    "horse",
    "ship",
    "truck",
]

# a file
url = "https://drive.google.com/uc?id=12700bE-pomYKoVQ214VrpBoJ7akXcTpL"
output = "modelV2Lmixed.keras"
gdown.download(url, output, quiet=False)

inception_net = tf.keras.models.load_model("./modelV2Lmixed.keras")


def classify_image(inp):
    inp = inp.reshape((-1, 224, 224, 3))
    inp = tf.keras.applications.efficientnet.preprocess_input(inp)
    prediction = inception_net.predict(inp).flatten()
    confidences = {labels[i]: float(prediction[i]) for i in range(10)}
    return confidences


import gradio as gr

gr.Interface(
    fn=classify_image,
    inputs=gr.inputs.Image(shape=(32, 32)),
    outputs=gr.outputs.Label(num_top_classes=3),
    examples=["03_cat.jpg", "05_dog.jpg"],
    theme="default",
    css=".footer{display:none !important}",
).launch()