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import streamlit as st
from configfile import Config
from src.graph.graph_builder import GraphBuilder
from src.streamlitui.loadui import LoadStreamlitUI
from src.LLMS.groqllm import GroqLLM
from src.langgraphagent.caller_agent import Caller_Agent
from langchain_core.messages import HumanMessage,AIMessage,ToolMessage
from src.tools.langgraphtool import APPOINTMENTS
def submit_message(model):
obj_caller_agent = Caller_Agent(model)
# caller agent
return obj_caller_agent.receive_message_from_caller(st.session_state["message"])
# MAIN Function START
if __name__ == "__main__":
# config
obj_config = Config()
# load ui
ui = LoadStreamlitUI()
user_input = ui.load_streamlit_ui()
graph_display =''
# is_add_message_to_history = st.session_state["chat_with_history"]
if user_input['selected_usecase'] == "Appointment Receptionist":
if st.chat_input("Type message here", key="message") :
# Configure LLM
obj_llm_config = GroqLLM(user_controls_input=user_input)
model = obj_llm_config.get_llm_model()
CONVERSATION,APPOINTMENTS,graph_display= (submit_message(model))
col1, col2 = st.columns(2)
with col1:
for message in CONVERSATION:
if type(message) == HumanMessage:
with st.chat_message("user"):
st.write(message.content)
else:
with st.chat_message("assistant"):
st.write(message.content)
with col2:
st.header("Appointments")
st.write(APPOINTMENTS)
elif user_input['selected_usecase'] == "Customer Support":
from src.csbot.customer_support_chatbot import Customer_Support_Bot
from langchain_core.messages import AIMessage, HumanMessage
from src.tools.customer_support_tools import customers_database, data_protection_checks
st.subheader('Flower Shop Chatbot' + '💐')
if 'message_history' not in st.session_state:
st.session_state.message_history = [AIMessage(content="Hiya, Im the flower shop chatbot. How can I help?")]
main_col, right_col = st.columns([2, 1])
# 1. Buttons for chat - Clear Button
with st.sidebar:
if st.button('Clear Chat'):
st.session_state.message_history = []
# 2. Chat history and input
with main_col:
user_message = st.chat_input("Type here...")
if user_message:
st.session_state.message_history.append(HumanMessage(content=user_message))
obj_llm_config = GroqLLM(user_controls_input=user_input)
llm = obj_llm_config.get_llm_model()
obj_cs_bot = Customer_Support_Bot(llm=llm)
app = obj_cs_bot.chat_bot()
response = app.invoke({
'messages': st.session_state.message_history
})
st.session_state.message_history = response['messages']
for i in range(1, len(st.session_state.message_history) + 1):
this_message = st.session_state.message_history[-i]
if isinstance(this_message, AIMessage):
message_box = st.chat_message('assistant')
else:
message_box = st.chat_message('user')
message_box.markdown(this_message.content)
# 3. State variables
with right_col:
st.title('customers database')
st.write(customers_database)
st.title('data protection checks')
st.write(data_protection_checks)
else:
# Basic Examples - chatbot and chatbot with tool
# Text input for user message
user_message = st.chat_input("Enter your message:")
if user_message:
# Configure LLM
obj_llm_config = GroqLLM(user_controls_input=user_input)
model = obj_llm_config.get_llm_model()
# Initialize and set up the graph based on use case
usecase = user_input['selected_usecase']
graph_builder = GraphBuilder(model)
graph_display = graph = graph_builder.setup_graph(usecase)
# Prepare state and invoke the graph
initial_state = {"messages": [user_message]}
entry_points = {"Basic Chatbot": "chatbot", "Chatbot with Tool": "chatbot_with_tool"}
entry_points = {"Basic Chatbot": "chatbot", "Chatbot with Tool": "chatbot_with_tool"}
if usecase =="Basic Chatbot":
for event in graph.stream({'messages':("user",user_message)}):
print(event.values())
for value in event.values():
print(value['messages'])
with st.chat_message("user"):
st.write(user_message)
with st.chat_message("assistant"):
st.write(value["messages"].content)
else:
res = graph.invoke(initial_state)
for message in res['messages']:
if type(message) == HumanMessage:
with st.chat_message("user"):
st.write(message.content)
elif type(message)==ToolMessage:
with st.chat_message("ai"):
st.write("Tool Call Start")
st.write(message.content)
st.write("Tool Call End")
elif type(message)==AIMessage and message.content:
with st.chat_message("assistant"):
st.write(message.content)
# display graph
if graph_display:
st.write('state graph workflow')
st.image(graph_display.get_graph(xray=True).draw_mermaid_png())
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