Building an AI Data Assistant with Streamlit, LangChain and OpenAI | Part 1

Introduction

Welcome to DigiL Academy! If you are looking to accelerate your machine learning projects, you are in the right place. This article will guide you through building your own AI assistant using Streamlit and LangChain. Let's dive into the process, covering everything from setting up your libraries to creating an interactive data science assistant.

What to Expect In This Series

In this series, you will learn how to utilize Streamlit to create a user interface that enables users to upload CSV files, visualize data, and interact with an AI assistant. The elegance of Streamlit lies in its simplicity, making it beginner-friendly while offering customization options for more advanced users. Furthermore, Streamlit allows easy deployment of web applications to the cloud for collaborative data projects.

Steps to Build Your AI Assistant

1. Setting Up Your Environment:

   • Before we begin coding the AI assistant, make sure to set up your OpenAI API key and run Streamlit. After creating a file to store your key, run the command: streamlit run your_script_name.py in the terminal.

2. Import Necessary Libraries:

   • You will be importing several essential libraries, such as:

     import os
     from api_key import API_KEY
     import streamlit as st
     from langchain import LangChain
     import pandas as pd
     

3. Building Your User Interface (UI):

   • Start by adding titles, headings, and a welcome message. To create an appealing UI, use:

     st.title("AI Assistant for Data Science")
     st.header("Exploratory Data Analysis Part")
     st.subheader("Solution")
     st.write("Hello, I'm your AI assistant and here to assist you with your machine learning projects.")
     

   • Introduce sections utilizing dividers and format text for easier navigation.

4. Sidebar and Expander:

   • Add a sidebar with relevant instructions using st.sidebar. Implement expanders to provide more details when the user clicks on them.

5. File Upload Functionality:

   • Incorporate a file uploader to allow users to upload CSV files using:

     uploaded_file = st.file_uploader("Upload your CSV file", type="csv")
     

6. Session State Management:

   • Understand session state to maintain variables across reruns. This is crucial when handling button click events.

7. Introducing LangChain:

   • Integrate LangChain with OpenAI’s GPT-3.5 Turbo to handle user queries efficiently.

8. Using Pandas Agent:

   • Set up a Pandas agent to perform specific data analysis on the uploaded dataset, allowing users to ask predefined questions about the data.

9. Optimizing with Caching:

   • Utilize Streamlit’s caching feature to ensure that only necessary computations run, enhancing the performance of the application.

10. Exploratory Data Analysis:

    • Display insights, generate visualizations like line charts, and allow users to ask additional questions, making the assistant interactive and informative.

Conclusion

In this first part of our tutorial, you’ve built the basic components of an AI data assistant capable of assisting with exploratory data analysis. Stay tuned for the next installment, where we’ll dive deeper into converting business challenges into data science frameworks, model selection, predictions, and more.

Don't forget to download the final project and sample dataset from the DigiL Academy website for hands-on practice. Thank you for joining me on this journey of data science exploration!

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Keywords

• Streamlit
• LangChain
• AI Assistant
• Data Science
• OpenAI
• API Key
• CSV Upload
• User Interface
• Exploratory Data Analysis
• Pandas Agent
• Caching

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FAQ

1. What is Streamlit?
Streamlit is an open-source app framework for Machine Learning and Data Science projects that allows users to create interactive web applications quickly.

2. How do I set up my OpenAI API key?
You need to create a file in your working directory to store your OpenAI API key as a string. This file should not be shared as it is sensitive information.

3. Can I customize my Streamlit app?
Yes! Streamlit offers many customization options to create a more sophisticated application that fits your project's needs.

4. What is LangChain?
LangChain is a framework designed to build conversational AI systems that can understand and respond to user queries effectively.

5. How does the caching feature work in Streamlit?
Caching in Streamlit allows you to store and reuse the results of expensive computations, improving the performance of the application. It's essential for maintaining the responsiveness of interactive apps.