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import streamlit as st
from openai import OpenAI
from app_config import *
from app_access_db import *
# model = "gpt-3.5-turbo"
# model = "gpt-4-turbo"
model = "gpt-4o"
gpt_base_url = None
# model = "lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF"
# gpt_base_url = "http://localhost:1234/v1/"
# ------------------------------------------------------------------------------------------------
# SIDEBAR
# ------------------------------------------------------------------------------------------------
st.sidebar.title('OpenAI Business Chat')
st.sidebar.write('This chat bot is build with Tools and Function feature of OpenAI to be able to answer question regarding applications and performance of officers')
st.sidebar.markdown("""
### Having a sample database with a structure
- application
- app_number
- amount
- amount_paid
- state. (APPROVED, REJECTED, PENDING_PAYMENT, PAID)
- office_code [FK]
- service_code [FK]
- date_created
- date_paid
- date_processed
- office
- office_name
- office_location_code [FK]
- location
- location_name
- location_code
- service
- service_code
- service_name
### The chatbot will provide answers from that database
- The number of applications rejected is a location during the current month
- The trend of applications in particular states, for a location
- Any question you think relevant from this DB
""")
def onchange_openai_key():
print(st.session_state.openai_key)
openai_key = st.sidebar.text_input('OpenAI key', on_change=onchange_openai_key, key='openai_key')
def submit_openai_key(model=model):
if(openai_key == None or openai_key==''):
st.sidebar.write('Please provide the key before')
return
else:
client = OpenAI(api_key=openai_key, base_url=gpt_base_url)
model = model
completion = client.chat.completions.create(
model=model,
messages=[
{"role": "system", "content": "You are an assistant giving simple and short answer for question of child. No questions, and no explanations"},
{"role": "user", "content": "count from 0 to 10"}
],
temperature=0
)
st.sidebar.write(f'Simple count : {completion.choices[0].message.content}')
submit_key = st.sidebar.button(label='Submit', on_click=submit_openai_key)
# ------------------------------------------------------------------------------------------------
# CHAT
# ------------------------------------------------------------------------------------------------
st.title('OpenAI Business Chat')
st.write(f'Ask any question that can be answer by the LLM {model}.')
def askQuestion(model=model, question=''):
if(openai_key == None or openai_key==''):
print('Please provide the key before')
return 'LLM API is not defined. Please provide the key before'
else:
client = OpenAI(api_key=openai_key, base_url=gpt_base_url)
model = model
completion = client.chat.completions.create(
model=model,
messages=[
{"role": "system", "content": f'{query_context}'},
{"role": "user", "content": f'{question}'}
],
temperature=0
)
return completion.choices[0].message.content
class AssistantMessage:
def __init__(self):
self.sql : str
self.response_data : DataFrame
def displayAssistantMessage( assistantMessage: AssistantMessage):
with st.chat_message("assistant"):
st.code(assistantMessage.sql, language='sql')
st.code(assistantMessage.response_data, language='markdown')
if assistantMessage.response_data.columns.size == 2:
st.bar_chart(assistantMessage.response_data, x=assistantMessage.response_data.columns[0], y=assistantMessage.response_data.columns[1])
if assistantMessage.response_data.columns.size == 1:
st.metric(label=assistantMessage.response_data.columns[0], value=f'{assistantMessage.response_data.values[0][0]}')
# 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:
if message["role"] == "user":
with st.chat_message(message["role"]):
st.markdown(message["content"])
elif message["role"] == "assistant":
displayAssistantMessage(message["content"])
# React to user input
if prompt := st.chat_input("What is up?"):
with st.status('Running', expanded=True) as status:
# Display user message in chat message container
st.chat_message("user").markdown(prompt)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
response = askQuestion(question=prompt)
# st.code(response, language='sql')
response_data = run_query(response.replace('```',''))
# Display assistant response in chat message container
assistanMsg = AssistantMessage()
assistanMsg.sql = response.replace('```','')
assistanMsg.response_data = response_data
displayAssistantMessage(assistanMsg)
# with st.chat_message("assistant"):
# st.code(response, language='sql')
# st.caption(response_data)
# st.bar_chart(response_data, x=response_data.columns[0], y=response_data.columns[1])
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": assistanMsg})
status.update(label='Response of last question', state="complete", expanded=True)
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