File size: 1,952 Bytes
ecf7f5f 268eef5 ecf7f5f 268eef5 ecf7f5f 268eef5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | import streamlit as st
from langchain_core.messages import HumanMessage,AIMessage,ToolMessage
import json
class DisplayResultStreamlit:
def __init__(self,usecase,graph,user_message):
self.usecase= usecase
self.graph = graph
self.user_message = user_message
def display_result_on_ui(self):
usecase= self.usecase
graph = self.graph
user_message = self.user_message
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)
elif usecase=="Chatbot with Tool":
# Prepare state and invoke the graph
initial_state = {"messages": [user_message]}
with st.chat_message("user"):
st.write(user_message)
for event in graph.stream(initial_state):
print(event.values())
for value in event.values():
print(value['messages'])
for message in value['messages']:
if type(message) == HumanMessage:
continue
if 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) |