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"""
This is a Python script that serves as a frontend for a conversational AI model built with the `langchain` and `llms` libraries.
The code creates a web application using Streamlit, a Python library for building interactive web apps.
# Author: Avratanu Biswas
# Date: March 11, 2023
"""
# Import necessary libraries
import streamlit as st
from langchain.chains import ConversationChain
from langchain.chains.conversation.memory import ConversationEntityMemory
from langchain.chains.conversation.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE
from langchain.llms import OpenAI
# Set Streamlit page configuration
st.set_page_config(page_title='🧠MemoryBot🤖', layout='wide')
# Initialize session states
if "generated" not in st.session_state:
st.session_state["generated"] = []
if "past" not in st.session_state:
st.session_state["past"] = []
if "input" not in st.session_state:
st.session_state["input"] = ""
if "stored_session" not in st.session_state:
st.session_state["stored_session"] = []
# Define function to get user input
def get_text():
"""
Get the user input text.
Returns:
(str): The text entered by the user
"""
input_text = st.text_input("You: ", st.session_state["input"], key="input",
placeholder="Your AI assistant here! Ask me anything ...",
label_visibility='hidden')
return input_text
# Define function to start a new chat
def new_chat():
"""
Clears session state and starts a new chat.
"""
save = []
for i in range(len(st.session_state['generated'])-1, -1, -1):
save.append("User:" + st.session_state["past"][i])
save.append("Bot:" + st.session_state["generated"][i])
st.session_state["stored_session"].append(save)
st.session_state["generated"] = []
st.session_state["past"] = []
st.session_state["input"] = ""
st.session_state.entity_memory.entity_store = {}
st.session_state.entity_memory.buffer.clear()
# Set up sidebar with various options
with st.sidebar.expander("🛠️ ", expanded=False):
# Option to preview memory store
if st.checkbox("Preview memory store"):
with st.expander("Memory-Store", expanded=False):
st.session_state.entity_memory.store
# Option to preview memory buffer
if st.checkbox("Preview memory buffer"):
with st.expander("Bufffer-Store", expanded=False):
st.session_state.entity_memory.buffer
MODEL = st.selectbox(label='Model', options=['gpt-3.5-turbo','text-davinci-003','text-davinci-002','code-davinci-002'])
K = st.number_input(' (#)Summary of prompts to consider',min_value=3,max_value=1000)
# Set up the Streamlit app layout
st.title("🤖 Chat Bot with 🧠")
st.subheader(" Powered by 🦜 LangChain + OpenAI + Streamlit")
# Ask the user to enter their OpenAI API key
API_O = st.sidebar.text_input("API-KEY", type="password")
# Session state storage would be ideal
if API_O:
# Create an OpenAI instance
llm = OpenAI(temperature=0,
openai_api_key=API_O,
model_name=MODEL,
verbose=False)
# Create a ConversationEntityMemory object if not already created
if 'entity_memory' not in st.session_state:
st.session_state.entity_memory = ConversationEntityMemory(llm=llm, k=K )
# Create the ConversationChain object with the specified configuration
Conversation = ConversationChain(
llm=llm,
prompt=ENTITY_MEMORY_CONVERSATION_TEMPLATE,
memory=st.session_state.entity_memory
)
else:
st.sidebar.warning('API key required to try this app.The API key is not stored in any form.')
# st.stop()
# Add a button to start a new chat
st.sidebar.button("New Chat", on_click = new_chat, type='primary')
# Get the user input
user_input = get_text()
# Generate the output using the ConversationChain object and the user input, and add the input/output to the session
if user_input:
output = Conversation.run(input=user_input)
st.session_state.past.append(user_input)
st.session_state.generated.append(output)
# Allow to download as well
download_str = []
# Display the conversation history using an expander, and allow the user to download it
with st.expander("Conversation", expanded=True):
for i in range(len(st.session_state['generated'])-1, -1, -1):
st.info(st.session_state["past"][i],icon="🧐")
st.success(st.session_state["generated"][i], icon="🤖")
download_str.append(st.session_state["past"][i])
download_str.append(st.session_state["generated"][i])
# Can throw error - requires fix
download_str = '\n'.join(download_str)
if download_str:
st.download_button('Download',download_str)
# Display stored conversation sessions in the sidebar
for i, sublist in enumerate(st.session_state.stored_session):
with st.sidebar.expander(label= f"Conversation-Session:{i}"):
st.write(sublist)
# Allow the user to clear all stored conversation sessions
if st.session_state.stored_session:
if st.sidebar.checkbox("Clear-all"):
del st.session_state.stored_session |