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import streamlit as st
from transformers import pipeline
#from datasets import load_dataset, Image
from huggingface_hub import from_pretrained_keras
import keras
import numpy as np
from PIL import Image
loaded_model = keras.saving.load_model("best_model.keras")
uploaded_img = st.file_uploader("Upload your file here...",type=['png', 'jpeg', 'jpg'])
if uploaded_img is not None:
st.image(uploaded_img)
img = Image.open(uploaded_img).resize((160, 160))
img = np.array(img)
result = loaded_model.predict(img[None,:,:])
st.write(f"Your prediction is: {result}")
#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)
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