import streamlit as st
from transformers import pipeline

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

def main():
    if "generated_widget_id" not in st.session_state:
        st.session_state["generated_widget_id"] = None

    st.title("Pbjs GPT2")
    st.write("Enter Prebid config text like **bidderTimeout** and get a generated Prebid config output from that setting onward. Using **{** will generate a Prebid config from the beginning.")
    st.write("**Intended uses:** This model is intended to assist publishers in understanding and exploring how other publishers configure their Prebid settings. The model was trained on 1100+ Prebid config files. It can serve as a reference to gain insights into common configurations, best practices, and different approaches used by publishers across various domains. To learn more about the model go to: [pbjs_gpt2](https://huggingface.co/PeterBrendan/pbjs_gpt2). [Link to official Prebid Documentation on pbjs.setConfig](https://docs.prebid.org/dev-docs/publisher-api-reference/setConfig.html)")
    st.write("*Note:* The model does take some time to run")

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

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

    # Create a text input field for 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()

        # Display 'Generating Output' message
        output_placeholder = st.empty()
        with output_placeholder:
            st.write("Generating Output...")

        # Generate text based on user input
        generated_text = generator(user_input, max_length=700, num_return_sequences=1)[0]["generated_text"]

        # Clear 'Generating Output' message and display the generated text
        output_placeholder.empty()
        st.write("Generated Text:")
        st.write(generated_text)

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