Chat with CSV using OpenAI

Project Overview

“Chat with CSV OpenAI” is more than a project; it’s an adventure into the realm of AI and data interaction. This application will serve as a bridge between you and your data, allowing you to converse with your CSV files through the magic of OpenAI’s language models. Imagine asking your data questions and getting answers as if you were chatting with a data scientist!

Setting Up Your Environment

First things first, let’s get our digital workspace ready. We are going to need Python, Streamlit for our web interface, Pandas for managing our data, and the OpenAI API for the intelligent part of our chat. Here’s how to lay the groundwork:

Install Necessary Tools

Open your terminal or command prompt and get ready to install some goodies. Type in the following commands:

!pip install streamlit pandasai pandas --q
!npm install localtunnel

These commands will install Streamlit and the necessary Python packages for data handling and AI integration, plus LocalTunnel to expose our app to the world.

Core Concepts

Before we jump into coding, let’s get acquainted with the key players in our project:

Chat Interfaces

A chat interface is where the magic of conversation happens. It’s the front end where users type in their queries and get responses.

OpenAI API

This is the brain behind our operation. OpenAI’s API provides access to advanced AI models that can understand and generate human-like text, perfect for chatting with our data.

CSV Files

These simple, yet powerful, files will be the source of our data. CSV stands for Comma-Separated Values, and it’s a format that allows us to store tabular data in plain text.

Building the Project

Now, let’s get down to the nitty-gritty and start building our application. We’ll go step by step, so follow along and don’t hesitate to experiment.

Initializing the Application

Create a new Python file named app.py. This file will be the heart of our chat application. Here’s how we start:

import streamlit as st
from pandasai.llm.openai import OpenAI
import pandas as pd
from pandasai import PandasAI

openai_api_key = "YOUR_OPENAI_API_KEY"

In these lines, we import our necessary libraries and set the openai_api_key variable. Replace "YOUR_OPENAI_API_KEY" with your actual OpenAI API key.

Creating the Chat Function

Next, we define a function chat_with_csv that will take our CSV data and a user’s prompt to generate a conversational response:

def chat_with_csv(df, prompt):
    llm = OpenAI(api_token=openai_api_key)
    pandas_ai = PandasAI(llm)
    result = pandas_ai.run(df, prompt=prompt)
    print(result)
    return result

Here, df represents the DataFrame (our CSV data), and prompt is the question or query from the user. We use PandasAI to create a connection between our data and the OpenAI model, generating a response based on the input prompt.

Setting Up the Streamlit Interface

Streamlit makes it easy to create web apps. Let’s set up our app’s title and the file uploader for the CSV:

st.set_page_config(layout='wide')

st.title("ChatCSV powered by LLM")
input_csv = st.file_uploader("Upload your CSV file", type=['csv'])

This code creates a web page with a title and a file uploader. When a CSV file is uploaded, we can process it and set up our chat interface.

Processing the CSV and Chatting

Now, let’s handle the uploaded CSV and create a text area for user queries:

if input_csv is not None:
    data = pd.read_csv(input_csv)
    st.dataframe(data)

    input_text = st.text_area("Enter your query")
    if st.button("Chat with CSV"):
        result = chat_with_csv(data, input_text)
        st.success(result)

In this block, we check if a file has been uploaded, read the CSV into a DataFrame, and display it. Then, we provide a text area for the user to type their query and a button to trigger the chat function.

Testing and Troubleshooting

After setting everything up, it’s time to test our application. Try uploading different CSV files and asking various questions related to the data. If things don’t go as planned, double-check your code for errors, ensure your OpenAI API key is correct, and verify that your CSV files are well-structured.

Expanding Your Project

Once you’ve got the basics down, why stop there? Think about adding more features, like handling multiple CSV files, supporting more complex queries, or integrating other data sources and APIs to make your chatbot even smarter.

Wrapping Up

Congratulations, you’ve just created a “Chat with CSV OpenAI” project! This is more than a technical achievement; it’s a step into the future of data interaction. The skills you’ve learned here are just the beginning. Keep exploring, keep coding, and most importantly, keep the conversation with your data going!

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