Integrating Chatbots with the Streamlit Interface
Explore how to build and integrate conversational chatbots using Streamlit's chat components combined with large language models. Understand the code structure for managing chat history, user inputs, and AI-generated responses. Gain practical skills in setting up real-time interactive dialogue systems with seamless back-end and front-end integration.
We'll cover the following...
Introduction to chat elements
Streamlit provides a couple of chat elements specifically designed to build conversational chatbots. These elements allow us to build a question-answer dialogue between the user and the LLM.
st.chat_input: This displays a chat input widget that allows the user to type in queries.st.chat_message: This displays a chat message container that allows the app to display messages from the user or the LLM.st.status: This displays output from long-running processes and external API calls so that the user can get updates while the chatbot is working on its response.
Understanding the coding process
Let’s build a chatbot using Streamlit, Groq, and ...