import streamlit as st
import os
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain



api_key = os.environ['openai_api_key']

def get_insurance_heading(data,insurance_prompt):

    llm = OpenAI(temperature=0,openai_api_key=api_key)

    prompt_template = "prompt: " +  insurance_prompt + " and article content: {text} "
    PROMPT = PromptTemplate(
        template=insurance_prompt, input_variables=["text"]
    )
    chain = LLMChain(llm=llm, prompt=PROMPT)
    resp = chain.run(text=data)

    return resp


def get_sports_heading(data,sports_prompt):

    llm = OpenAI(temperature=0,openai_api_key=api_key)

    prompt_template = "prompt: " + sports_prompt + " and  article content: {text} "

    PROMPT = PromptTemplate(
        template=sports_prompt, input_variables=["text"]
    )
    chain = LLMChain(llm=llm, prompt=PROMPT)
    resp = chain.run(text=data)

    return resp


def process_article_content(content,insurance_prompt,sports_prompt):

    return get_insurance_heading(content,insurance_prompt), get_sports_heading(content,sports_prompt)

# Streamlit app
def main():
    st.title("Health Day Demo")

    # Input field for article content
    article_content = st.text_area("Enter Article Content:", "")
    insurance_prompt = st.text_area("insurance prompt", "")
    sports_prompt = st.text_area("sports prompt", "")
    


    # Process button
    if st.button("Process"):
        # Process the article content
        if article_content:
            insurance_user, sports_user = process_article_content(article_content,insurance_prompt,sports_prompt)

            # Display the output
            st.subheader("Processed Output:")
            st.title("Insurance User")
            
            st.write(f"{insurance_user}")
            st.title("Sports User")
            
            st.write(f"{sports_user}")

if __name__ == "__main__":
    main()