Creating a Streamlit interface for your chatbot enables users to interact with it through an intuitive web app. This guide walks you through building a Streamlit front end for your chatbot powered by a Large Language Model (LLM).
Step 1: Install Dependencies
First, install Streamlit and other required libraries:
pip install streamlit langchain faiss-cpu sentence-transformers
Step 2: Set Up Your Python Environment
Ensure your chatbot logic is in place. You can use LangChain or another framework to process user queries. For this example, we’ll assume you’ve already created a chatbot function, ask_question(chain, question), as outlined in the earlier blog.
Step 3: Create a Streamlit App File
Create a new Python file, e.g., app.py, to serve as your Streamlit app.
Step 4: Design the User Interface
Set up a simple layout with input fields for user questions and a section for chatbot responses:
import streamlit as st
from langchain.chains import RetrievalQA
# Load your chatbot chain (replace with your actual implementation)
def load_chain():
# Replace with your chain initialization code
return qa_chain
qa_chain = load_chain()
# Streamlit app layout
st.title(“LLM Chatbot”)
st.markdown(“Ask your questions and get instant answers from the chatbot!”)
# Input text box for the user
user_question = st.text_input(“Your Question:”)
# Display the response when the user submits a question
if st.button(“Ask”):
if user_question.strip():
with st.spinner(“Generating response…”):
answer = ask_question(qa_chain, user_question)
st.success(“Answer:”)
st.write(answer)
else:
st.warning(“Please enter a question.”)
Step 5: Customize the Interface
Add features to enhance user experience:
- File Upload for Document Processing:
uploaded_file = st.file_uploader(“Upload a PDF for chatbot context:”, type=”pdf”)
if uploaded_file:
with open(“temp_uploaded_file.pdf”, “wb”) as f:
f.write(uploaded_file.getbuffer())
st.success(“File uploaded successfully!”)
# Add logic to process the uploaded file
- Chat History:
if “chat_history” not in st.session_state:
st.session_state.chat_history = []
st.write(“### Chat History”)
for chat in st.session_state.chat_history:
st.write(f”**Q:** {chat[‘question’]}\n**A:** {chat[‘answer’]}”)
if st.button(“Clear History”):
st.session_state.chat_history = []
Step 6: Run the Streamlit App
Run the app locally by executing:
streamlit run app.py
This will open a web browser with your chatbot interface.
Step 7: Deploy Your App
Deploy your app to a hosting platform like Streamlit Community Cloud or a cloud provider such as AWS, Azure, or GCP.
- For Streamlit Cloud:
- Push your app to a GitHub repository.
- Log in to Streamlit Cloud and deploy the app directly from your repository.
Conclusion
With Streamlit, you can quickly create a dynamic and user-friendly interface for your LLM chatbot. Customize it further with features like file uploads, chat history, and deployment options to enhance usability.
