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Build a ChatGPT Deep Research Clone with Streamlit

1h 39m 27s
English
Paid

Imagine having your own AI assistant that conducts deep research for you: figuring out exactly what you need, searching for information online, collecting data, and presenting it in the form of a neat report or essay...

This is exactly what the Deep Research tool from ChatGPT does!

It is a powerful tool that allows you to use AI to explore complex topics and generate substantiated reports with sources.

And what could be better?

Create your own version of Deep Research!

In this course, you will step by step build your own clone of Deep Research. You will program all the stages - from query recognition and data collection to report writing - using Python and modern GPT models. This will not only give you the ability to adapt the tool to your needs but also help you understand how AI works on the inside.

Most importantly, you will package it all into a convenient web application on Streamlit and deploy it.

No "filler" - just practical code, clear explanations, and a result that you can actually use.

By the end of the course, you will have your own AI tool for deep research and the skills to create others.

About the Author: zerotomastery.io

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Whether you are just starting to learn to code or want to advance your skills, Zero To Mastery Academy will teach you React, Javascript, Python, CSS and more to help you advance your career, get hired and succeed at some of the top companies in the world.

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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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