import streamlit as st
from transformers import pipeline
from concurrent.futures import ProcessPoolExecutor

prompt_template = (
    "<|system|>\n"
    "You are a friendly chatbot who always gives helpful, detailed, and polite answers.</s>\n"
    "<|user|>\n"
    "{input_text}</s>\n"
    "<|assistant|>\n"
)

def generate_base_response(input_text):
    base_pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", max_length=512)
    return base_pipe(input_text)[0]["generated_text"]

def generate_irai_response(input_text):
    irai_pipe = pipeline("text-generation", model="InvestmentResearchAI/LLM-ADE_tiny-v0.001", max_length=512)
    return irai_pipe(prompt_template.format(input_text=input_text))[0]["generated_text"].split("<|assistant|>")[1].strip()

def generate_response(input_text):
    with ProcessPoolExecutor() as executor:
        try:
            future_base = executor.submit(generate_base_response, input_text)
            future_irai = executor.submit(generate_irai_response, input_text)
            base_resp = future_base.result()
            irai_resp = future_irai.result()
        except Exception as e:
            st.error(f"An error occurred: {e}")
            return None, None
    return base_resp, irai_resp

st.title("IRAI LLM-ADE Model vs Base Model")
user_input = st.text_area("Enter a financial question:", "")
if st.button("Generate"):
    if user_input:
        base_response, irai_response = generate_response(user_input)
        col1, col2 = st.columns(2)  # Updated to use `st.columns`
        with col1:
            st.header("Base Model Response")
            st.text_area("", base_response, height=300)
        with col2:
            st.header("IRAI LLM-ADE Model Response")
            st.text_area("", irai_response, height=300)
    else:
        st.warning("Please enter some text to generate a response.")