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"""Module doc string"""
import streamlit as st
from .constants import ConstantVariables
from .logs import logger
from .openai_utils import OpenAIFunctions
class StreamlitFunctions:
"""Module doc string"""
@staticmethod
def streamlit_page_config():
"""_summary_"""
st.set_page_config(
page_title="simple-chat-bot",
page_icon="👾",
layout="centered",
initial_sidebar_state="auto",
)
st.title("👾👾 Simple Chat Bot 👾👾")
@staticmethod
def streamlit_side_bar():
"""_summary_"""
with st.sidebar:
st.text_input(
label="OpenAI API key",
value=ConstantVariables.api_key,
help="This will not be saved or stored.",
type="password",
key="api_key",
)
st.selectbox(
"Select the GPT model",
ConstantVariables.model_list_tuple,
key="openai_model",
)
st.slider(
"Max Tokens",
min_value=ConstantVariables.min_token,
max_value=ConstantVariables.max_tokens,
step=ConstantVariables.step,
key="openai_maxtokens",
)
st.button(
"Start Chat",
on_click=StreamlitFunctions.start_app,
use_container_width=True,
)
st.button(
"Reset History",
on_click=StreamlitFunctions.reset_history,
use_container_width=True,
)
@staticmethod
def streamlit_initialize_variables():
"""_summary_"""
logger.debug("Initializing Streamlit Variables")
if "messages" not in st.session_state:
st.session_state.messages = []
if "openai_model" not in st.session_state:
st.session_state["openai_model"] = ConstantVariables.default_model
if "openai_api_key" not in st.session_state:
st.session_state["openai_api_key"] = None
if "openai_maxtokens" not in st.session_state:
st.session_state["openai_maxtokens"] = ConstantVariables.default_token
if "start_app" not in st.session_state:
st.session_state["start_app"] = False
@staticmethod
def reset_history():
"""_summary_"""
logger.debug("Resetting Chat State")
st.session_state.openai_api_key = st.session_state.api_key
st.session_state.messages = []
@staticmethod
def start_app():
"""_summary_"""
logger.debug("Starting Application")
st.session_state.start_app = True
st.session_state.openai_api_key = st.session_state.api_key
@staticmethod
def streamlit_print_messages():
"""_summary_"""
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
@staticmethod
def streamlit_invoke_model():
"""_summary_"""
if prompt := st.chat_input("Type your Query"):
with st.chat_message("user"):
st.markdown(prompt)
st.session_state.messages.append({"role": "user", "content": prompt})
response = OpenAIFunctions.invoke_model()
logger.debug(response)
st.session_state.messages.append({"role": "assistant", "content": response[0]})
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