app.py

# LC_Streaming.py



from langchain.callbacks.base import BaseCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage, SystemMessage
import streamlit as st
import os

# from langchain.prompts.chat import (
#      ChatPromptTemplate,
#      SystemMessagePromptTemplate,
#      HumanMessagePromptTemplate,
# )


class StreamHandler(BaseCallbackHandler):
    def __init__(self, container, initial_text="", display_method='markdown'):
        self.container = container
        self.text = initial_text
        self.display_method = display_method

    def on_llm_new_token(self, token: str, **kwargs) -> None:
        self.text += token # + "/"
        display_function = getattr(self.container, self.display_method, None)
        if display_function is not None:
            display_function(self.text)
        else:
            raise ValueError(f"Invalid display_method: {self.display_method}")



def check_password():
    """Returns `True` if the user had the correct password."""

    def password_entered():
        """Checks whether a password entered by the user is correct."""
        if st.session_state["password"] == os.environ["USER_PWORD"]:
            st.session_state["password_correct"] = True
            del st.session_state["password"]  # don't store password
        else:
            st.session_state["password_correct"] = False

    if "password_correct" not in st.session_state:
        # First run, show input for password.
        st.text_input(
            "Password", type="password", on_change=password_entered, key="password"
        )
        return False
    elif not st.session_state["password_correct"]:
        # Password not correct, show input + error.
        st.text_input(
            "Password", type="password", on_change=password_entered, key="password"
        )
        st.error("😕 Password incorrect")
        return False
    else:
        return True





if check_password():
    st.markdown("Get instant feedback on your research question")
    query = st.text_input("Input your research question", value="How do biases in AI student evaluations compare to documented biases in human evaluations?")
    ask_button = st.button("ask")

    st.markdown("### GPT-3.5 response")
    chat_box = st.empty()
    stream_handler = StreamHandler(chat_box, display_method='write')
    chat = ChatOpenAI(streaming=True, callbacks=[stream_handler])

    query_combined = "Please provide constructive feedback in English on this proposed research question, suggesting how it might be improved: <research_question>" + query + "</research_question>."
    #st.markdown("### together box")
    messages = [
        SystemMessage(
            content="You are a helpful research assistant that provides feedback to university students and " \
            "researchers on their ideas for a research question, in particular for Masters students planning to write a Master's Thesis. " \
            "A good research question should be narrow enough that it can be well-addressed in a 20-30 page " \
            "literature review, and provides guidance to focus the literature review, and points towards specific areas " \
            "of scientific literature that should be included. A good research question also provides guidance on the " \
            "methodological (empirical) approach needed to answer the question. "
        ),
        HumanMessage(
            content=query_combined
        ),
    ]

    if query and ask_button:
        response = chat(messages) # [HumanMessage(content=query)])