polygon_app.py

import os
from langchain import hub
from langchain.agents import create_openai_functions_agent
from langchain_openai.chat_models import ChatOpenAI
from langchain.tools import BaseTool, StructuredTool, Tool, tool
from langchain_community.utilities.polygon import PolygonAPIWrapper
from langchain_community.tools import PolygonLastQuote, PolygonTickerNews, PolygonFinancials, PolygonAggregates
from langchain_core.runnables import RunnablePassthrough
from langchain_core.agents import AgentFinish
from langgraph.graph import END, Graph
from dotenv import load_dotenv
import gradio as gr



load_dotenv(".env")

openAI = os.getenv("OPENAI_API_KEY")
polygon = os.getenv("POLYGON_API_KEY")

langsmith = os.getenv("LANGSMITH_API_KEY")
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_PROJECT"] = "langgraph-financial-bot"
os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
os.environ["LANGCHAIN_API_KEY"] = str(os.getenv("LANGCHAIN_API_KEY"))

prompt = hub.pull("hwchase17/openai-functions-agent")
llm = ChatOpenAI(model="gpt-4-0125-preview")

polygon = PolygonAPIWrapper()
tools = [
    PolygonLastQuote(api_wrapper=polygon),
    PolygonTickerNews(api_wrapper=polygon),
    PolygonFinancials(api_wrapper=polygon),
    PolygonAggregates(api_wrapper=polygon),
]

# DEFINE THE AGENT
agent_runnable = create_openai_functions_agent(llm, tools, prompt)
agent = RunnablePassthrough.assign(
    agent_outcome = agent_runnable
)

# DEFINE THE FUNCTIONS TO USE
def execute_tools(data):
    agent_action = data.pop('agent_outcome')
    tool_to_use = {t.name: t for t in tools}[agent_action.tool]
    observation = tool_to_use.invoke(agent_action.tool_input)
    data['intermediate_steps'].append((agent_action, observation))
    return data


# DEFINE LOGIC THAT WILL BE USED TO DETERMINE WHICH CONDITIONAL EDGE TO USE 
def should_continue(data):
    if isinstance(data['agent_outcome'], AgentFinish):
        return "exit"
    else:
        return "continue"

workflow = Graph()
workflow.add_node("agent", agent)
workflow.add_node("tools", execute_tools)
workflow.set_entry_point("agent")
workflow.add_conditional_edges(
    "agent",
    should_continue,
    {
        "continue": "tools",
        "exit": END
    }
)
workflow.add_edge('tools', 'agent')
#
# COMPILE GRAPH
chain = workflow.compile()

def financial_agent(input_text):
    result = chain.invoke({"input": input_text, "intermediate_steps": []})
    output = result['agent_outcome'].return_values["output"]
    return output

# CREATE INTERFACE  
iface = gr.Interface(
    fn=financial_agent,
    inputs=gr.Textbox(lines=2, placeholder="Enter your query here..."),
    outputs=gr.Markdown(),
    title="Financial Agent",
    description="Financial Data Explorer: Leveraging Advanced API Tools for Market Insights"
)

iface.launch()