import tarfile
import gradio as gr
from huggingface_hub import hf_hub_download
from tensorflow.keras.models import load_model
from tensorflow.keras.applications import resnet50

def load_model(tar_file: str='model.tar.gz'):
    tar_file = tarfile.open(tar_file)
    tar_file.extractall('./')
    tar_file.close()

    model_path = './model'
    return load_model(model_path)

hf_hub_download(repo_id='chansung/', filename='outputs/model.tar.gz')
model = load_model()
labels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

def classify_image(inp):
  inp = inp.reshape((-1, 224, 224, 3))
  inp = resnet50.preprocess_input(inp)

  prediction = model.predict(inp).flatten()
  confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
  return confidences

gr.Interface(fn=classify_image, 
             inputs=gr.inputs.Image(shape=(224, 224)),
             outputs=gr.outputs.Label(num_top_classes=3),
             examples=["banana.jpg", "car.jpg"]).launch()