streamlit_app.py

from pathlib import Path

import streamlit as st

from langchain import SQLDatabase
from langchain.agents import AgentType
from langchain.agents import initialize_agent, Tool
from langchain.callbacks import StreamlitCallbackHandler
from langchain.chains import LLMMathChain, SQLDatabaseChain
from langchain.llms import OpenAI
from langchain.utilities import DuckDuckGoSearchAPIWrapper

from streamlit_agent.callbacks.capturing_callback_handler import playback_callbacks
from streamlit_agent.clear_results import with_clear_container

DB_PATH = (Path(__file__).parent / "Chinook.db").absolute()

SAVED_SESSIONS = {
    "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?": "leo.pickle",
    "What is the full name of the artist who recently released an album called "
    "'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs "
    "are in the FooBar database?": "alanis.pickle",
}

st.set_page_config(
    page_title="MRKL", page_icon="🦜", layout="wide", initial_sidebar_state="collapsed"
)

"# 🦜🔗 MRKL"

# Setup credentials in Streamlit
# user_openai_api_key = st.sidebar.text_input(
#     "OpenAI API Key", type="password", help="Set this to run your own custom questions."
# )

# if user_openai_api_key:
#     openai_api_key = user_openai_api_key
#     enable_custom = True
# else:
#     openai_api_key = "not_supplied"
#     enable_custom = False
openai_api_key = st.write(
    "OPENAI_API_KEY",
    os.environ["OPENAI_API_KEY"] == st.secrets["OPENAI_API_KEY"],
)

# Tools setup
llm = OpenAI(temperature=0, openai_api_key=openai_api_key, streaming=True)
search = DuckDuckGoSearchAPIWrapper()
llm_math_chain = LLMMathChain.from_llm(llm)
db = SQLDatabase.from_uri(f"sqlite:///{DB_PATH}")
db_chain = SQLDatabaseChain.from_llm(llm, db)
tools = [
    Tool(
        name="Search",
        func=search.run,
        description="useful for when you need to answer questions about current events. You should ask targeted questions",
    ),
    Tool(
        name="Calculator",
        func=llm_math_chain.run,
        description="useful for when you need to answer questions about math",
    ),
    Tool(
        name="FooBar DB",
        func=db_chain.run,
        description="useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context",
    ),
]

# Initialize agent
mrkl = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)

with st.form(key="form"):
    if not enable_custom:
        "Ask one of the sample questions, or enter your API Key in the sidebar to ask your own custom questions."
    prefilled = st.selectbox("Sample questions", sorted(SAVED_SESSIONS.keys())) or ""
    user_input = ""

    if enable_custom:
        user_input = st.text_input("Or, ask your own question")
    if not user_input:
        user_input = prefilled
    submit_clicked = st.form_submit_button("Submit Question")

output_container = st.empty()
if with_clear_container(submit_clicked):
    output_container = output_container.container()
    output_container.chat_message("user").write(user_input)

    answer_container = output_container.chat_message("assistant", avatar="🦜")
    st_callback = StreamlitCallbackHandler(answer_container)

    # If we've saved this question, play it back instead of actually running LangChain
    # (so that we don't exhaust our API calls unnecessarily)
    if user_input in SAVED_SESSIONS:
        session_name = SAVED_SESSIONS[user_input]
        session_path = Path(__file__).parent / "runs" / session_name
        print(f"Playing saved session: {session_path}")
        answer = playback_callbacks([st_callback], str(session_path), max_pause_time=2)
    else:
        answer = mrkl.run(user_input, callbacks=[st_callback])

    answer_container.write(answer)