CF

Build a ChatGPT Deep Research Clone with Streamlit

1h 39m 27s
English
Paid

You will build your own AI tool that can search the web, gather facts, and write clear reports for you.This course shows you how to do it step by step.

What You Build

You create a clone of the Deep Research tool from ChatGPT. Your tool will take a user question, look for useful sources, collect key points, and write a clean report. You use Python and modern GPT models for each part.

You also turn the tool into a simple web app with Streamlit. You can run it on your own machine or deploy it online.

How the Course Works

You start with the main flow of the app. You learn how to read the user query and break it into steps. You then add code that searches the web and gathers the data you need.

After that, you guide the model to write a clear report with sources. You test each part as you go. This helps you see how the system works inside.

What You Learn

You learn how a deep research pipeline works. You also learn how to use GPT models with Python and how to control the output. You see how to build a Streamlit interface that makes the tool easy to use.

By the end, you have a working AI research app and the skills to build other custom AI tools.

About the Author: Zero To Mastery

Zero To Mastery thumbnail

Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.

The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.

The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.

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#1: Introduction
All Course Lessons (18)
#Lesson TitleDurationAccess
1
Introduction Demo
09:04
2
Colab Setup
02:35
3
Inputs
02:19
4
5 Clarifying Questions
05:29
5
Answering the 5 Questions
05:07
6
Define Goals and Queries for the Research
07:18
7
Web Search with OpenAI
05:10
8
Define Web Search Function
02:09
9
Confirm if Goal Was Achieved
06:21
10
Web Search if the Goal Was Not Achieved
05:52
11
Final Deep Research Report
04:38
12
Download Cursor
04:37
13
.env File
01:48
14
Prompt Cursor to Build the Streamlit App
07:24
15
Launching the App Locally
06:33
16
Debugging
07:58
17
Push to Github
09:07
18
Deploy to Streamlit
05:58
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Frequently asked questions

What prerequisites do I need before enrolling in this course?
Before enrolling, you should have a basic understanding of Python programming. Familiarity with web development concepts and tools, such as Streamlit, will also be beneficial, as the course involves building a web app. No prior experience with GPT models is required, as the course covers their usage and integration.
What will I build by the end of the course?
By the end of the course, you will have built a functional clone of the ChatGPT Deep Research tool. This tool can process user questions, search the web for relevant information, compile key points, and generate a comprehensive report. Additionally, you'll develop a user-friendly web app using Streamlit, which can be deployed on your local machine or online.
Who is the target audience for this course?
The course is designed for developers and tech enthusiasts interested in artificial intelligence and web development. It's suitable for those who want to learn how to integrate GPT models in practical applications and are keen on building custom AI tools. Beginners with basic programming skills may find it challenging but rewarding.
How does this course compare to other AI courses in terms of scope?
Unlike other AI courses that may focus on theoretical concepts, this course is project-based and practical. It guides you through building an AI research tool step by step, providing hands-on experience with Python, GPT models, and web app deployment using Streamlit. It emphasizes real-world application rather than just theoretical knowledge.
What specific tools or platforms will I use during the course?
Throughout the course, you'll use Python for programming and modern GPT models for AI functionalities. Streamlit will be employed to create the web app interface. The course also involves using Colab for setup, GitHub for version control, and Streamlit for app deployment. OpenAI's APIs are utilized for web searches.
What topics or skills are not covered in this course?
The course does not cover advanced machine learning concepts or the inner workings of GPT models beyond their application. It is also not focused on in-depth web development or user interface design, other than basic Streamlit usage. Networking, advanced data gathering techniques, and comprehensive deployment strategies are also not covered.
How much time should I expect to commit to complete this course?
The total runtime of the course is not specified, but with 18 lessons, you should expect to dedicate a few hours to each lesson, depending on your pace and prior experience. This includes time for coding, testing, and deploying your project. A consistent schedule will help in completing the course efficiently.