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# 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)])
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