import gradio as gr
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.agents.agent_types import AgentType
from langchain_experimental.agents.agent_toolkits import create_csv_agent
import os

spaces_key = os.getenv("open_ai")

def call_csv(file_path):
  # Set up the agent
  agent = create_csv_agent(
      ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k", openai_api_key=spaces_key),
      file_path,
      verbose=True,
      agent_type=AgentType.OPENAI_FUNCTIONS,)
  return agent

# Define the Gradio function
def qa_app(csv_file, question):
    # Update the agent with the new CSV file
    agent = call_csv(csv_file)

    # Perform QA using the provided question
    response = agent.run(question)
    
    return response

# Set up the Gradio interface
iface = gr.Interface(
    fn=qa_app,
    inputs=["file", "text"],
    outputs="text",
    live=False,
    title="CSV QA App",
    description="Upload a CSV file and ask a question about the data.",
)
iface.launch()