import gradio as gr
from langchain.llms.openai import OpenAI
import os 
from langchain.agents import initialize_agent
from langchain.agents import load_tools

os.environ["OPENAI_API_KEY"] = "sk-dDPyQHpuXcMDDP5PmFgnT3BlbkFJLdhOV60RNrnf5xp5DUc"
os.environ["SERPAPI_API_KEY"] = "e109a79c9b6a844c889c8b3f65430f3ea17c4362de514eafeb6030414ec6f808"

llm = OpenAI(temperature=0, max_tokens=1000, model_name='text-davinci-003')

def answer_question(question):
    agent_exe = initialize_agent(
        llm=OpenAI(temperature=0),
        tools=load_tools(["python_repl", "serpapi", "llm-math"], llm=llm),
        return_intermediate_steps=True,
        verbose=True,
    )
    response = agent_exe({"input": question})
    answer = response["output"]
    steps = response["intermediate_steps"]
    return answer, steps

ifaces = gr.Interface(
    fn=answer_question,
    inputs=gr.Textbox(label="Question", 
    placeholder="What's the square root, of the age, of Leonardo DiCaprio's latest girlfriend"),
    outputs=[gr.Textbox(label="Answer"), gr.JSON(label="Steps", show_label=False)],
    title="Helpful Agent",
    description="This is an Agent, which uses OpenAI's text-davinci-003 model, and the tools: SerpAPI, Python REPL, and Language Learning Machine Math, depending on your request"
)
ifaces.launch()