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#fix workflow
import os
import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
import networkx as nx
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_openai import ChatOpenAI
from langgraph.graph import MessagesState
from langgraph.graph import START, StateGraph
from langgraph.prebuilt import tools_condition
from langgraph.prebuilt import ToolNode
from langchain_core.messages import HumanMessage, SystemMessage
# ------------------- Environment Variable Setup -------------------
# Fetch API keys from environment variables
openai_api_key = os.getenv("OPENAI_API_KEY")
tavily_api_key = os.getenv("TAVILY_API_KEY")
# Verify if API keys are set
if not openai_api_key:
raise ValueError("Missing required environment variable: OPENAI_API_KEY")
if not tavily_api_key:
raise ValueError("Missing required environment variable: TAVILY_API_KEY")
# ------------------- Tool Definitions -------------------
tavily_tool = TavilySearchResults(max_results=5)
def multiply(a: int, b: int) -> int:
"""Multiply two numbers."""
return a * b
def add(a: int, b: int) -> int:
"""Add two numbers."""
return a + b
def divide(a: int, b: int) -> float:
"""Divide two numbers."""
if b == 0:
raise ValueError("Division by zero is not allowed.")
return a / b
tools = [add, multiply, divide, tavily_tool]
# ------------------- LLM Setup -------------------
llm = ChatOpenAI(model="gpt-4o-mini")
llm_with_tools = llm.bind_tools(tools, parallel_tool_calls=False)
sys_msg = SystemMessage(content="You are a helpful assistant tasked with performing arithmetic and search on a set of inputs.")
# ------------------- LangGraph Workflow -------------------
def assistant(state: MessagesState):
"""Assistant node to invoke LLM with tools."""
return {"messages": [llm_with_tools.invoke([sys_msg] + state["messages"])]}
app_graph = StateGraph(MessagesState)
app_graph.add_node("assistant", assistant)
app_graph.add_node("tools", ToolNode(tools))
app_graph.add_edge(START, "assistant")
app_graph.add_conditional_edges("assistant", tools_condition)
app_graph.add_edge("tools", "assistant")
react_graph = app_graph.compile()
# ------------------- Streamlit Interface -------------------
st.title("ReAct Agent")
# Display the workflow graph using NetworkX
st.header("LangGraph Workflow Visualization")
G = nx.DiGraph()
G.add_edge("START", "assistant")
G.add_edge("assistant", "tools", label="tools_condition")
G.add_edge("tools", "assistant", label="loop back")
plt.figure(figsize=(10, 6))
pos = nx.spring_layout(G, seed=42)
nx.draw(G, pos, with_labels=True, node_size=3000, node_color="lightblue", font_size=10, font_weight="bold")
nx.draw_networkx_edge_labels(G, pos, edge_labels={
("assistant", "tools"): "tools_condition",
("tools", "assistant"): "loop back"
}, font_color="red")
st.pyplot(plt)
# User input
user_question = st.text_area("Enter your question:", placeholder="Example: 'Add 3 and 4. Multiply the result by 2. Divide it by 5.'")
if st.button("Submit"):
if not user_question.strip():
st.error("Please enter a valid question.")
st.stop()
st.info("Processing your question...")
messages = [HumanMessage(content=user_question)]
response = react_graph.invoke({"messages": messages})
st.subheader("Responses")
for m in response['messages']:
st.write(m.content)
st.success("Processing complete!")
# Example Questions
st.sidebar.subheader("Example Questions")
st.sidebar.write("- Add 3 and 4. Multiply the result by 2. Divide it by 5.")
st.sidebar.write("- Tell me how many centuries Virat Kohli scored.")
st.sidebar.write("- Search for the tallest building in the world.")
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