BOCAS – using OCI GenAI Agent and Stremlit

Posted on March 10, 2025

BOCAS stands for Brendan’s Oracle Chatbot Agent for Shakespeare. I’ve previously posted on how to go about creating a GenAI Agent on a specific data set. In this post, I’ll share code on how I did this using Python Streamlit.

And here’s the code

import streamlit as st
import time
import oci
from oci import generative_ai_agent_runtime
import json


# Page Title
welcome_msg = "Welcome to BOCAS."
welcome_msg2 = "This is Brendan's Oracle Chatbot Agent for Skakespeare. Ask questions about the works of Shakespeare."
st.title(welcome_msg) 
 
# Sidebar Image
st.sidebar.header("BOCAS")
st.sidebar.image("bocas-3.jpg", use_column_width=True) 
#with st.sidebar:
#    with st.echo:
#        st.write(welcome_msg2)
st.sidebar.markdown(welcome_msg2)
st.sidebar.markdown("The above image above was generated by Copilot using the following prompt.  generate an image icon for a chatbot called BOCAS which means Brendan's Oracle Chat Agent for Shakespeare, add BOCAS to image, Add a modern twist to Shakespeare's elements")
 
st.sidebar.write("")
st.sidebar.write("")
st.sidebar.write("")
st.sidebar.image("https://media.shakespeare.org.uk/images/SBT_SR_OS_37_Shakespeare_Firs.ec42f390.fill-1200x600-c75.jpg")
link="This image is from the [Shakespeare Trust website](https://media.shakespeare.org.uk/images/SBT_SR_OS_37_Shakespeare_Firs.ec42f390.fill-1200x600-c75.jpg)"
st.sidebar.write(link,unsafe_allow_html=True)

# OCI GenAI settings
CONFIG_PROFILE = "DEFAULT"
config = oci.config.from_file('~/.oci/config', CONFIG_PROFILE)
###
SERVICE_EP = <your service endpoint> 
AGENT_EP_ID = <your agent endpoint>
###
 
# Response Generator
def response_generator(text_input):
    #Initiate AI Agent runtime client
    genai_agent_runtime_client = generative_ai_agent_runtime.GenerativeAiAgentRuntimeClient(config, service_endpoint=SERVICE_EP, retry_strategy=oci.retry.NoneRetryStrategy())

    create_session_details = generative_ai_agent_runtime.models.CreateSessionDetails()
    create_session_details.display_name = "Welcome to BOCAS"
    create_session_details.idle_timeout_in_seconds = 20
    create_session_details.description = welcome_msg

    create_session_response = genai_agent_runtime_client.create_session(create_session_details, AGENT_EP_ID)

    #Define Chat details and input message/question
    session_details = generative_ai_agent_runtime.models.ChatDetails()
    session_details.session_id = create_session_response.data.id
    session_details.should_stream = False
    session_details.user_message = text_input

    #Get AI Agent Respose
    session_response = genai_agent_runtime_client.chat(agent_endpoint_id=AGENT_EP_ID, chat_details=session_details)
 
    #print(str(response.data))
    response = session_response.data.message.content.text
    return response
 
# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []
 
# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])
 
# Accept user input
if prompt := st.chat_input("How can I help?"):
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})
    # Display user message in chat message container
    with st.chat_message("user"):
        st.markdown(prompt)
 
    # Display assistant response in chat message container
    with st.chat_message("assistant"):
        response = response_generator(prompt)
        write_response = st.write(response)
        st.session_state.messages.append({"role": "ai", "content": response})
    # Add assistant response to chat history