Build A Chatbot Interface with Streamlit

Let's build a chatbot interface with just Python using the Streamlit library. You can find more info here in below in the official Streamlit docs and check out the repository with all the code.

Build a basic LLM chat app - Streamlit Docs

https://docs.streamlit.io/knowledge-base/tutorials/build-conversational-apps

Steps

1. Create a new venv or conda environment

2. Install Streamlit

pip install streamlit openai python-dotenv

3. Test installation

streamlit hello

4. Create .env file with OPENAI_API_KEY

OPENAI_API_KEY="your-opnai-api-key"

5. Create your app.py file

   a. Add imports

   from openai import OpenAI
   import streamlit as st
   from dotenv import load_dotenv
   import os
   import shelve

   b. Add setup

   load_dotenv()
   st.title("ChatGPT-like Chatbot Demo")
   
   USER_AVATAR = "👤"
   BOT_AVATAR = "🤖"
   client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

   c. Configure model and messages

   # Ensure openai_model is initialized in session state
   if "openai_model" not in st.session_state:
       st.session_state["openai_model"] = "gpt-3.5-turbo"
   
   
   # Load chat history from shelve file
   def load_chat_history():
       with shelve.open("chat_history") as db:
           return db.get("messages", [])
   
   
   # Save chat history to shelve file
   def save_chat_history(messages):
       with shelve.open("chat_history") as db:
           db["messages"] = messages
   
   
   # Initialize or load chat history
   if "messages" not in st.session_state:
       st.session_state.messages = load_chat_history()
   
   # Sidebar with a button to delete chat history
   with st.sidebar:
       if st.button("Delete Chat History"):
           st.session_state.messages = []
           save_chat_history([])

   d. Configure main chat interface

   # Display chat messages
   for message in st.session_state.messages:
       avatar = USER_AVATAR if message["role"] == "user" else BOT_AVATAR
       with st.chat_message(message["role"], avatar=avatar):
           st.markdown(message["content"])
   
   # Main chat interface
   if prompt := st.chat_input("How can I help?"):
       st.session_state.messages.append({"role": "user", "content": prompt})
       with st.chat_message("user", avatar=USER_AVATAR):
           st.markdown(prompt)
   
       with st.chat_message("assistant", avatar=BOT_AVATAR):
           message_placeholder = st.empty()
           full_response = ""
           for response in client.chat.completions.create(
               model=st.session_state["openai_model"],
               messages=st.session_state["messages"],
               stream=True,
           ):
               full_response += response.choices[0].delta.content or ""
               message_placeholder.markdown(full_response + "|")
           message_placeholder.markdown(full_response)
       st.session_state.messages.append({"role": "assistant", "content": full_response})
   
   # Save chat history after each interaction
   save_chat_history(st.session_state.messages)

6. Run your Streamlit app with

streamlit run app.py