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## Conversational Q&A Chatbot
import streamlit as st
from langchain.schema import HumanMessage, SystemMessage, AIMessage
# from openai import AzureChatOpenAI
from langchain_openai import AzureChatOpenAI
import os
# import dotenv
# dotenv.load_dotenv()
AZURE_OPENAI_KEY = "7a8f58dd922e4c78b1de2b660ebe61d6"
AZURE_OPENAI_ENDPOINT = "https://mlsdaiinstance.openai.azure.com/"
AZURE_OPENAI_VERSION = "2024-05-01-preview"
EMBEDDING_MODEL = "text-embedding-ada-002"
CHAT_MODEL = "gpt-35-turbo"
# Initialize the Azure OpenAI client
llm = AzureChatOpenAI(
# azure_endpoint="https://azureopenai16.openai.azure.com/",
# api_key="75db73a3b9da40b0b6e0e98273a6029f",
# api_version="2024-05-01-preview",
# deployment_name="gpt-35-turbo",
# temperature=0.5
openai_api_type="azure",
openai_api_version=AZURE_OPENAI_VERSION,
openai_api_key=AZURE_OPENAI_KEY,
azure_endpoint=AZURE_OPENAI_ENDPOINT,
deployment_name=CHAT_MODEL,
temperature=0
)
# '''
## Streamlit UI
st.set_page_config(page_title="Conversational Q&A Chatbot")
st.header("Hey, Let's Chat")
if 'flow_messages' not in st.session_state:
st.session_state['flow_messages'] = [
SystemMessage(content="You are an AI assitant who answers the questions asked truthfully!")
]
## Function to load OpenAI model and get respones
def get_chatmodel_response(question):
st.session_state['flow_messages'].append(HumanMessage(content=question))
response = llm(st.session_state['flow_messages'])
st.session_state['flow_messages'].append(AIMessage(content=response.content))
return response.content
input = st.text_input("Input: ", key="input")
response = get_chatmodel_response(input)
submit = st.button("Ask the question")
## If ask button is clicked
if submit:
st.subheader("The Response is")
st.write(response)
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