app.py

import pandas as pd
import gradio as gr
from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_experimental.agents.create_pandas_dataframe_agent import create_pandas_dataframe_agent

import os
import openai
openai.api_key = os.getenv('api_token')

# Load the CSV file
def load_csv(file):
    print ("File is",file)
    df = pd.read_csv(file.name)
    print (df.head())
    return df

# Define the function that generates the response
def generate_response(question, file):
    # Load the CSV file
    df = load_csv(file)

    # Initialize OpenAI pipeline
    agent = create_pandas_dataframe_agent(OpenAI(temperature=0,openai_api_key=openai.api_key), df, verbose=True)

    # Generate response using OpenAI
    response = agent.run(question)

    return response

# Define the input and output interfaces
title = "Data Detective: Ask questions directly to your data"
csv_file = gr.inputs.File(label="CSV File")
question = gr.inputs.Textbox(label="Question")
output_text = gr.outputs.Textbox(label="Response")

# Create the Gradio app
gr.Interface(generate_response, inputs=[question, csv_file], outputs=output_text, title=title).launch(debug=True)