app.py

import streamlit as st
from transformers import pipeline

@st.cache_resource
def load_model():
    return pipeline("text-generation", model="PeterBrendan/pbjs_gpt2")

def main():
    st.title("Pbjs GPT2: Optimize your Prebid config")
    st.write("Enter some text like **bidderTimeout** and get a generated Prebid config output.")

    # Default prompts
    default_prompts = ["{", "bidderTimeout", "bidderSequence", "Usebidcache", "customPriceBucket"]

    # Create a select box for default prompts
    default_prompt = st.selectbox("Choose a default prompt:", default_prompts)

    # Create a text input field for a custom prompt
    custom_prompt = st.text_input("Enter a custom prompt:", "")

    # Check if a default prompt is selected
    if default_prompt:
        user_input = default_prompt
    else:
        user_input = custom_prompt

    # Check if the user input is empty
    if user_input:
        # Load the Hugging Face model
        generator = load_model()

        # Generate text based on user input
        generated_text = generator(user_input, max_length=500, num_return_sequences=1)[0]["generated_text"]

        # Display the generated text
        st.write("Generated Text:")
        st.write(generated_text)

# Run the app
if __name__ == "__main__":
    main()