app.py

import streamlit as st
from transformers import pipeline
#from datasets import load_dataset, Image
from huggingface_hub import from_pretrained_keras


model = from_pretrained_keras("jableable/road_model")

#pipe = pipeline('sentiment-analysis')
#text = st.text_area('enter some text!')

#if text:
    #out = pipe(text)
    #st.json(out)

#loaded_model = keras.saving.load_model("jableable/road_model")

#model = from_pretrained_keras("keras-io/ocr-for-captcha")
#model.summary()
#prediction = model.predict(image)
#prediction = tf.squeeze(tf.round(prediction))
#print(f'The image is a {classes[(np.argmax(prediction))]}!')


#dataset = load_dataset("beans", split="train")

#loaded_img = dataset[0]["image"]
#print(loaded_img)