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

Imagine accelerating your machine learning projects with an AI assistant that will save you hours and hours of work. In this video, the first in our series, we are building an AI-powered assistant that will transform the way you explore and analyse data. Say goodbye to complex data analysis processes and hello to a more intuitive and interactive experience! This video is part of the series Building an AI Assistant to make your data science life easier in which we will develop an AI assistant using Streamlit, LangChain and OpenAI’s GPT models, designed to help users with their data science projects. This AI assistant will streamline the entire process of a data science project, including exploratory data analysis (EDA), model selection and prediction, saving valuable time and resources. I'll walk you through the entire process, from installing the required libraries to solving a machine learning problem using AI. By the end of this series, you will have a powerful tool at your disposal, ready to assist you in every step of your data science journey. If you want to take a deeper dive in data science, check out our library of courses on DigiLab Academy πŸ”— https://academy.digilab.co.uk/courses β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€” 🎡 Music β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€” Downtown Walk by | e s c p | https://escp-music.bandcamp.com Music promoted by https://www.free-stock-music.com Creative Commons / Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/... β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€” πŸ“Œ Timestamps β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€” Intro - 0:00 What’s covered in this video - 00:39 Setting OpenAI key - 03:10 Running Streamlit - 04:01 Importing required packages - 4:24 Titles headings and subheadings - 5:35 Writing text - 7:00 Sidebar - 7:57 Using st.caption() - 9:39 Further text formatting - 11:03 Adding a divider - 11:45 Integrating HTML - 12:36 Adding expanders to the sidebar - 13:26 Buttons - 14:25 Integrating a CSV file uploader - 15:35 Session state - 17:08 Converting CSV file to dataframe - 18:46 Loading our LLM - 20:05 Generation information using our LLM - 21:10 Creating our Pandas agent - 23:46 Using Pandas agent to answer specific questions about the data - 24:43 Using Pandas agent to answer questions about a specific variable chosen by the user - 28:04 Using st.text_input() to store variable chosen by the user - 28:04 Caching - 29:20 Creating visualisations - 37:00 Answering user questions - 41:54 Answering more user questions - 45:33 What’s next - 45:55 β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€” πŸ“Œ Resources β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€” Written tutorial - https://academy.digilab.co.uk/posts/b... Streamlit documentation - https://streamlit.io/ Open AI website - https://openai.com/ LangChain documentation - https://python.langchain.com/ β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€” πŸ‘±πŸ»β€β™€οΈ Connect with me β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€” digiLab Academy - https://academy.digilab.co.uk/ Twitter - Β Β /Β digilab_academyΒ Β  LinkedIn - Β Β /Β digilab-solutions-ltdΒ Β  β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€” 🏷️ Tags β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€” Building an AI assistant to make your data science life easier Simplifying your data science journey with an AI assistant Crafting an AI assistant using Streamlit, Langchain and OpenAI models Enhancing data science efficiency through an AI-driven assistant Creating an AI assistant to ease your path in data science Developing a data science ally using Streamlit, Langchain and OpenAI models Developing an AI assistant for smoother workflows Designing an AI assistant to simplify your data science projects Making data science easy with the aid of an intelligent assistant Making data science effortless with the implementation of an AI assistant β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€” ✨ Hashtags β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€” [#howtobuildanaiassistant] (https://www.youtube.com/hashtag/howto...) [#AIassistant] (https://www.youtube.com/hashtag/AIass...) [#BuildyourownAIassistant] (https://www.youtube.com/hashtag/Build...) [#Python] (https://www.youtube.com/hashtag/Python) [#Streamlit] (https://www.youtube.com/hashtag/Strea...) [#LangChain] (https://www.youtube.com/hashtag/LangC...) [#Exploratorydataanalysis] (https://www.youtube.com/hashtag/Explo...) [#Dataanalysis] (https://www.youtube.com/hashtag/Dataa...) [#Datascience] (https://www.youtube.com/hashtag/Datas...) [#Machinelearning] (https://www.youtube.com/hashtag/Machi...) [#AI] (https://www.youtube.com/hashtag/AI) [#Artificialintelligence] (https://www.youtube.com/hashtag/Artif...) [#Tutorial] (https://www.youtube.com/hashtag/Tutorial) [#Datascienceproject] (https://www.youtube.com/hashtag/Datas...) [#OpenAI] (https://www.youtube.com/hashtag/OpenAI) [#GPT] (https://www.youtube.com/hashtag/GPT)

Building an AI Data Assistant with Streamlit, LangChain and OpenAI | Part 2
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