How can i set a model from hugging face into streamlit

Using Streamlit
1

Summary

i want to create a web app where i use stable diffusion in Streamlit
how can i do that ?

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Debug info

  • Streamlit version: (get it with $ streamlit version)
  • Python version: (get it with $ python --version)
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2

Install the Hugging Face Transformers library by running pip install transformers.

import streamlit as st
from transformers import pipeline

Choose a pre-trained model from the Hugging Face model hub. (You can find a list of available models at https://huggingface.co/models.)
Load the model using the pipeline class from the Transformers library. For example, to load a sentiment analysis model:

model = pipeline("sentiment-analysis")

create a simple UI for your streamlit app:

def main():
    st.title("Hugging Face Model Demo")

    # Create an input text box
    input_text = st.text_input("Enter your text", "")

    # Create a button to trigger model inference
    if st.button("Analyze"):
        # Perform inference using the loaded model
        result = model(input_text)
        st.write("Prediction:", result[0]['label'], "| Score:", result[0]['score'])

if __name__ == "__main__":
    main()
3

if i deploy this app , will it be downloading the model from hugging face every other time the page is refreshed?

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