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import gradio as gr
import os
import matplotlib.pyplot as plt
import pandas as pd
from langgraph.graph import StateGraph
from langgraph.prebuilt import create_react_agent
from langgraph.types import Command
from langchain_core.messages import HumanMessage
from langchain_anthropic import ChatAnthropic
# Set the API key from Hugging Face secrets
os.environ["ANTHROPIC_API_KEY"] = os.getenv("ANTHROPIC_API_KEY")
# Claude 3.5 Sonnet
llm = ChatAnthropic(model="claude-3-5-sonnet-latest")
# Create the system prompt
def make_system_prompt(suffix: str) -> str:
return (
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK—another assistant with different tools"
" will help where you left off. Execute what you can to make progress."
" If you or any of the other assistants have the final answer or deliverable,"
" prefix your response with FINAL ANSWER so the team knows to stop."
f"\n{suffix}"
)
# Workflow node: research
def research_node(state):
agent = create_react_agent(
llm,
tools=[],
state_modifier=make_system_prompt("You can only do research.")
)
result = agent.invoke(state)
goto = "chart_generator" if "FINAL ANSWER" not in result["messages"][-1].content else "__end__"
result["messages"][-1] = HumanMessage(
content=result["messages"][-1].content,
name="researcher"
)
return Command(update={"messages": result["messages"]}, goto=goto)
# Workflow node: chart generation
def chart_node(state):
agent = create_react_agent(
llm,
tools=[],
state_modifier=make_system_prompt("You can only generate charts.")
)
result = agent.invoke(state)
result["messages"][-1] = HumanMessage(
content=result["messages"][-1].content,
name="chart_generator"
)
return Command(update={"messages": result["messages"]}, goto="__end__")
# LangGraph setup
workflow = StateGraph(dict)
workflow.add_node("researcher", research_node)
workflow.add_node("chart_generator", chart_node)
workflow.set_entry_point("researcher")
workflow.set_finish_point("__end__")
workflow.add_edge("researcher", "chart_generator")
graph = workflow.compile()
# LangGraph runner
def run_langgraph(input_text):
try:
events = graph.stream({"messages": [("user", input_text)]})
output = []
for event in events:
output.append(event)
final_response = output[-1]["messages"][-1].content
if "FINAL ANSWER" in final_response:
# Simulated chart creation from dummy data
years = [2020, 2021, 2022, 2023, 2024]
gdp = [21.4, 22.0, 23.1, 24.8, 26.2]
plt.figure()
plt.plot(years, gdp, marker="o")
plt.title("USA GDP Over Last 5 Years")
plt.xlabel("Year")
plt.ylabel("GDP in Trillions USD")
plt.grid(True)
plt.tight_layout()
plt.savefig("gdp_chart.png")
return "Chart generated based on FINAL ANSWER.", "gdp_chart.png"
else:
return final_response, None
except Exception as e:
return f"Error: {str(e)}", None
# Gradio interface
def process_input(user_input):
return run_langgraph(user_input)
interface = gr.Interface(
fn=process_input,
inputs=gr.Textbox(label="Enter your research task"),
outputs=[gr.Textbox(label="Output"), gr.Image(type="filepath", label="Chart")],
title="LangGraph Research Automation",
description="Enter a research prompt and view chart output when applicable."
)
if __name__ == "__main__":
interface.launch()
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