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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: 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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