Turning a whole textbook into Anki flashcards with one click (including images)

Summary

The video script narrates a medical trainee's quest to automate the conversion of textbooks into Anki flashcards for efficient study. Initially attempting to use custom GPTs for image and text extraction, the creator faced limitations and turned to Python with the help of a friend. After overcoming several coding challenges, including image extraction, text formatting, and regular expressions, the project culminated in a streamlined app that transforms entire textbooks into flashcards with a single click, enhancing study efficiency for medical students.

Takeaways

• 🎥 The project started with an aim to automate the creation of flashcards from medical textbooks using custom GPTs.
• 🕵️‍♂️ The creator discovered a method to import images into flashcards using HTML links, potentially streamlining the process.
• 🚫 Despite initial optimism, the use of custom GPTs proved inefficient for processing large volumes of textbook content at once.
• 🌟 The 'Holy Grail' goal was to convert an entire textbook into flashcards with a single click, enhancing study efficiency.
• 📚 Textbooks in Radiology, with their standard format, were identified as ideal candidates for this automated conversion process.
• 🛠️ The creator faced several challenges, including image extraction issues and the need for text formatting consistency across books.
• 🔄 Regular expressions became a crucial tool for targeting specific text and segmenting documents for the flashcard creation.
• 🤖 Chat GPT was utilized to assist in coding the project, despite the creator's initial lack of Python knowledge.
• 📝 The project involved creating scripts for image extraction, text extraction, and CSV file conversion for Anki flashcard import.
• 🔧 The creator had to refine the script to handle multiple cases per page and differences in textbook formatting within a series.
• 💡 A breakthrough came with breaking down problems into basic parts, creating a uniform tagging system, and developing a common script pathway for processing different books.

Q & A

What was the initial goal of the project described in the script?

The initial goal was to create a custom GPT that could systematically process casebooks from Radiology textbooks, extracting text and images, and then organizing the data to create Anki flashcards.

Why was automating the image import into flashcards considered a significant breakthrough?

Automating the image import into flashcards was considered significant because it eliminated the need for manual copying and pasting of images, greatly reducing the time and effort required to create flashcards from textbooks.

What challenges did the creator face when trying to extract images from the textbook?

The creator faced challenges such as the image extractor script failing to recognize every case in the textbook, resulting in incomplete image extraction, and images having an inverted color scheme with no apparent pattern.

How did the creator address the issue of the image extractor script not recognizing every case?

The creator modified the script to ignore case numbers due to inconsistent formatting, which resolved the issue for most cases, except when two cases were presented on a single page.

What role did regular expressions play in the text extraction process?

Regular expressions were used to target specific text segments and ignore others, allowing the creator to extract only the desired portions of the text for the flashcards based on the consistent formatting of the casebooks.

Why was creating a JSON file with an inventory of images important for the text extractor script?

The JSON file with an inventory of images was important because it allowed the text extractor script to know how many HTML image links needed to be created for each case, ensuring that the number of images matched the text content.

What was the purpose of developing a uniform tagging system for the extracted information?

The uniform tagging system made it easier to manipulate and process the text consistently, reducing reliance on consistent formatting between different books in the series and allowing for easier application of the script to new textbooks.

How did the creator handle the issue of multiple cases presented on a single page?

The creator updated the image naming convention and included new logic in the image extractor script to determine when and how to separate pictures when they are presented in a multi-case format.

What was the final output of the project after addressing the various challenges?

The final output was a functional app that could process an entire textbook and turn it into flashcards with a single click, significantly streamlining the process of creating study materials from Radiology casebooks.

What did the creator learn about the limitations of their initial script and how did they plan to overcome them?

The creator realized that their initial script couldn't handle multiple cases on a single page and wasn't easily adaptable to different textbooks in the series. They planned to overcome these limitations by breaking down the problems into basic parts, addressing them individually, and then packaging the solutions into a common format that could be applied to any book in the series.

Outlines

00:00

📚 Automating Flashcard Creation with Custom GPTs

The narrator, a medical trainee, embarked on a project to automate the creation of Anki flashcards from textbooks, aiming to save time and streamline the process. Initially, they attempted to use custom GPTs to extract text and images from radiology case review books, which are highly recommended and organized in a standard format. The goal was to leverage HTML links to automatically import images into flashcards. Despite the challenges, including the limitations of the custom GPTs and the need to manually handle images, they discovered a potential solution using Python, a language they were unfamiliar with but sought help from a friend to learn.

05:03

🤖 Python Scripting to Extract and Organize Textbook Data

After realizing that chat GPT was not the right tool for the task, the narrator turned to Python, a programming language well-suited for data extraction. They downloaded a code editor, installed Python, and utilized the PyMuPDF library to extract images and text from PDFs. Facing several issues, such as image recognition problems, color inversion, and text extraction specificity, they employed regular expressions to target the desired text segments. They also developed a system to count images per case and generate HTML image links for flashcards. The process involved creating a CSV file from the extracted data, which could then be uploaded to Anki, resulting in functional flashcards from an entire textbook with minimal manual effort.

10:03

🛠 Refining the Script for General Applicability

Despite the initial success, the narrator identified limitations in their script, such as handling multiple cases on a single page and the need for modifications when incorporating new textbooks from the series. They decided to break down the problem into smaller parts, addressing them individually before integrating them into a common format. This approach allowed for a more general solution that could be applied across different books in the series. They developed a uniform tagging system for consistent text manipulation, updated the image naming convention, and created an app that could process an entire textbook into flashcards with a single click, significantly reducing the effort required for each new book.

Keywords

💡Custom GPTs

Custom GPTs refer to customized versions of Generative Pre-trained Transformers, a type of AI language model that can be fine-tuned for specific tasks. In the video, the creator is attempting to use these models to automate the process of turning medical textbooks into flashcards, which is central to the video's theme of leveraging technology for efficiency.

💡Medical Trainee

A medical trainee is a person undergoing medical education or training, often in a residency program. The script mentions the creator's perspective as a medical trainee, emphasizing the limited free time they have and the desire to use it effectively, which is the driving force behind the project described in the video.

💡Flashcards

Flashcards are a study aid consisting of cards with information on them, typically used for memorization and review. In this context, the creator aims to automate the creation of digital flashcards from medical textbooks, which is a key part of the project being discussed.

💡HTML Links

HTML links are used to connect different pages or resources on the web. The video describes a method of using HTML links to automatically import images into flashcards, which is a significant part of the technical solution the creator is exploring.

💡Anki

Anki is a popular open-source flashcard program using spaced repetition to help users memorize information more efficiently. The creator's goal is to integrate Anki with their custom flashcard creation process, which is a central theme in the video.

💡Radiology

Radiology is a medical specialty that uses imaging techniques to diagnose and treat diseases. The video mentions Radiology textbooks, which are the specific type of textbooks the creator is working with, and these are used as the basis for the flashcard content.

💡Python

Python is a widely-used high-level programming language known for its readability and versatility. The creator eventually decides to use Python to build an application for extracting images and text from PDFs, which is a pivotal moment in the video as it shifts from using AI to coding.

💡PDF

PDF stands for Portable Document Format, a file format used to present documents in a manner independent of application software, hardware, and operating systems. The script discusses extracting images and text from PDF textbooks, which is the primary task the creator is automating.

💡CSV File

A CSV file, or Comma-Separated Values file, is a type of file used to store tabular data, such as a spreadsheet or database. The creator's project involves converting the extracted data into a CSV file format that can be uploaded into Anki.

💡Regular Expressions

Regular expressions are a powerful tool used in programming for matching patterns in strings of text. In the script, the creator uses regular expressions to target and extract specific text from the medical textbooks for the flashcards.

💡JSON File

JSON, or JavaScript Object Notation, is a lightweight data-interchange format that is easy for humans to read and write and for machines to parse and generate. The video describes using a JSON file to store and access the count of images associated with each diagnosis.

💡Spaced Repetition

Spaced repetition is a learning technique that involves increasing intervals of time between subsequent review of previously learned material to exploit the psychological spacing effect. This concept is integral to how Anki operates and is mentioned in the context of the benefits of using Anki for studying.

Highlights

Project goal to automate the creation of Anki flashcards from medical textbooks using images and text.

Discovery of HTML links to automate image import into flashcards, eliminating manual copy-pasting.

Challenge of processing multiple cases from radiology textbooks into a standardized Anki format.

Innovative approach to use Python for data extraction and manipulation instead of relying on GPT.

Introduction of PyMuPDF, a Python library for extracting images and text from PDFs.

Struggle with image extraction due to inconsistent case number formatting in the textbook.

Solution to ignore case numbers and handle multiple cases on a single page.

Issue with inverted color scheme in extracted images and the decision to fix it post-extraction.

Utilization of regular expressions to target and segment specific text for flashcards.

Development of a method to count images per case for accurate HTML link generation.

Designing Anki note types to balance information availability and user experience.

Implementation of an image gallery feature in Anki for case review.

Inclusion of dropdown menus for questions and answers to enhance study flexibility.

Challenge of making the script adaptable to different textbooks in the series.

Epiphany to break down problems into basic parts for individual addressing.

Creation of a uniform tagging system to standardize text extraction across books.

Refinement of the image naming convention for consistency across different books.

Development of logic to handle multi-case image separation in the extractor script.

Packaging of individual scripts into a single app for one-click textbook to flashcard conversion.

Recognition of script limitations and the need for further refinement for broader applicability.

Transcripts

[Music]

[Music]

I started out this project trying to

film a video about the new custom gpts

that came out a couple weeks ago I'm

always trying to find ways to leverage a

a to be able to reclaim the few precious

hours of free time I have as a medical

trainee so that I can spend time doing

things that actually matter to me like

spending time with my

family I have a goal of turning all of

the textbooks that are recommended by my

residency into eny flashcards for me and

my co-residents one of the most

timeconsuming parts of this endeavor is

taking the images from a textbook and

pasting them into the flash cards that I

create and I recently discovered that

there's a way that you can automatically

import images into flash cards by using

HTML links as long as you're linking the

file name exactly as it exists in the

collection. media folder you can

automatically populate your flashcards

with images that means no need to

manually copy and paste images ever

again which would be freaking huge if I

could figure this out I would be getting

that much closer to being able to turn

an entire textbook into flashcards with

a single click which is like the Holy

Grail of this quest to ankify everything

now obviously this wouldn't necessarily

work with every textbook and there's a

lot of textbooks you really wouldn't

want to do this with because they

contain a lot of garbage you don't want

in your flash cards but in Radiology we

have these case review textbooks that

are the perfect candidate for something

like this and they come very highly

recommended by our attendings because as

they love to say the best way to see

more cases is by taking a stab at some

of these unknown cases from these case

books these books are organized in a

very standard format where the image or

multiple images are presented along with

a list of questions and then on the

second page you have a diagnosis the

answers to the questions and some extra

comments about the condition in

question and they all follow this format

exactly almost exactly my goal is to

make it so that we can study the content

from these textbooks while also

benefiting from the convenience and the

space repetition that Anki offers so my

plan was to create a custom GPT that

would work through one of these

casebooks systematically to extract the

text place the closed deletions and

would also link the appropriate images

but it turns out that even with the

custom gpts uh the best I could hope for

would be to process these cases one to

three cases at a time and I would still

have to screenshot and save all of these

images from the textbook which is way

too cumbersome and honestly a far cry

from the oneclick Holy Grail that I had

envisioned well that project got put on

hold for a couple weeks because I

rotated onto nights and I worked 19 days

in a

row like And subscribe so so I can quit

this stupid job anyways while I was

actively dissociating on these long

night shifts I had the realization that

maybe this was not the right task for

chat GPT I'm not really asking the

chatbot to do any highle processing I'm

just asking it to extract text and

images and then organize that data in a

specific way which at its core is really

just data transfer there's got to be a

simpler way to do this Well turns out

this is the sort of thing that people

have been using python to do for a long

time and unfortunately

I don't know anything about python but I

do know somebody who does and he's my

best friend

so let's give him a call all right I may

have abused this relationship in the

past so I really don't know how this is

going to

[Music]

go hello hey um I have something I need

your help with how much does it pay what

did you have in mind it's a python

project I wanted to make an app that

extracts images and text from a PDF

textbook and puts it all into a CSV file

that I can upload into Ani and create

flashcards from it

so how much does it pay just kidding

that was uh not a real person that was

chat

GPT it is my best friend

though and I have abused it so after a

quick conversation with the chat bot I

figured out that I needed to download a

code editor the latest version of python

and get access to pimu PDF which is a

python library that helps you extract

images and text from PDF documents what

is a python Library honestly I'm not

sure it sounds a lot like a book of

magic spells it's got a bunch of

pre-coded functions that you can use to

build out your scripts like it makes it

easier to do the magic stuff without

having to come up with the spells from

scratch every time armed with my book of

spells I was ready to start building my

app the game plan was as follows step

one test out the image extractor to see

if what I wanted to do was even possible

step two design the eny not type so that

I know how my final CSV would need to be

structured and so I have a good end

point in mind and because I was using

chat GPT I had to get a working

prototype as quickly as possible so that

if I was going to fail I was going to

fail quickly before I had invested a

bunch of time into this project

obviously I worked with chat GPT to Code

100% of this project because I thought

python was a snake a couple weeks ago or

maybe a reference to my favorite movie

while I was growing up so I created a

custom GPT and gave it all the

background information about what we

would be trying to do as well as

reminded it that it needs to treat me

like a total Noob because that's what I

am the first thing I did was test a

script that extracted images from the

textbook for a given page range which

seemed to work fine I realized I was

going to have to figure out a clever way

of sorting and naming these images so

that they were all associated with the

appropriate case but before I could even

even really finish that thought I had

encountered my first problem the image

extractor script was failing to

recognize every case in the textbook and

thus failing to extract all the images

from the text this was happening because

of the way the case numbers were

formatted there were a bunch of spaces

in between each number and I wasn't

going to end up using the case numbers

anyways so I just told the script to

completely ignore the case numbers and

this seemed to fix the issue well it

fixed the issue except for when there

were two cases presented at once on a

single page

which I'll get to that later the second

major issue I had was that a lot of the

images I was extracting looked like they

had been inverted the color scheme was

inverted and there was no Rhyme or

Reason to it and so I figured it would

be easier to fix this in eny after I had

imported all the cards and stuff so I

figured I would just table this for now

and deal with it later the third problem

I had was getting the text extractor

script to ignore certain parts of the

text and only grab the portions of the

text that I I really wanted to be in the

flashcards this is where I learned a lot

about regular Expressions which would

soon become the entire backbone of this

script a regular expression is a way of

targeting specific text as well as

segmenting your document I was able to

use the consistent formatting of these

cases to my advantage and Target only

the specific text that I wanted this way

it ignored all the other texts that I

really didn't care for and didn't really

need the fourth major issue that I

encountered was that I needed an

automated way to count how many images

were in each case and be able to

communicate at that to the text

extractor script that I had written so

that it could generate the appropriate

number of HTML image links within each

case chat GPT suggested having the image

extractor script count all the images

and then associate them with the

diagnosis and then put all that

information into a Json file which the

text extractor script could then access

and then know how many HTML links it

needed to create for each case which

works perfectly I could tell I was

getting really close to having a fully

functional script so I extracted a few

few practice cases in order to get my

eny note type totally dialed in I didn't

want people to feel really overwhelmed

with all the information that each card

would contain but I still wanted it to

be readily available if they wanted to

reference it so they didn't have to go

back and look it up in the book I knew

that I definitely needed some sort of

image gallery to be able to review all

of the images so that when you looked at

a case that had a bunch of images it

wasn't super annoying to review this is

also where I fixed the issue I was

having with a lot of the images

inverting the colors randomly I just

went ahead and added an inversion button

so that you could flip the color

inversion on the images that you wanted

to it's actually useful to have the

ability to invert images and Radiology

so it's kind of a two for one tool I

decided that the questions and answers

should appear in drop down menus to give

people the option to study them that way

they wouldn't clutter up the flash card

if they weren't in use and it made the

most sense to have the diagnosis

contained within the Clos deletion since

that was the most important information

that I was testing anyways some other

issues that I faced while I was trying

to fine-tune this enky not type was I

had to figure out how to add extra

fields which ended up being pretty easy

I just had to look in the eny manual

there's basically no limit to the number

of fields you can create which is pretty

cool another issue I had was getting the

images to actually show up within the

flashcards turns out you have to have

this little button checked here this

allows there to be HTML within the

fields in the flash card another issue I

was having was getting the images to

show up within the image gallery I fixed

this by assigning a class group within

the CSV file so that the JavaScript

Gallery actually

recognize the images as an object it

could pull in and after about a week of

working on this project for a couple

hours in the morning before heading to

work I had two functional scripts the

image extractor script pulled all the

images out of the textbook named them

appropriately and also generated a Json

file with an inventory of how many

images went with each diagnosis that

could be accessed by my text extractor

script the text extractor script was

very impressive in my humble opinion it

extracted the diagnosis and placed

closed letion around them Ely organized

the questions and answers as well as

generated the exact number of image

links that matched the file names

perfectly it corrected the formatting

issues and made sure everything was

neatly separated so that it would line

up with the fields in my eny note type I

then created a simple script that would

convert the output from the text

extractor script into a CSV file which I

could automatically upload into Anki and

after that I had perfectly functioning

flashcards which I had created from an

entire textbook in just a couple of

clicks

well almost an entire textbook book my

script still had some major flaws

unfortunately one issue with my script

was that it really couldn't handle the

instances where there were multiple

cases presented on a single page now

this really wasn't that big of an issue

in this textbook because there were only

eight instances of that or 16 cases of

that but in some of the other books in

the series almost every single page has

multiple cases on it so it would be a

major issue moving forward also even

though these textbooks are formatted

very similarly

they're not exactly the same ideally

this script would be able to be used on

all of the books in the series but the

way it's written currently I'd have to

make significant modifications each time

I Incorporated a new textbook from the

series for example none of the other

textbooks include the diagnosis colon

text in any of the

cases which is kind of an important part

of the script currently I created

something really cool that feels like a

real life cheat code but but it loses

its utility almost immediately if it's

not more generally applicable and able

to be used with all the other books in

the series so I decided I couldn't stop

there over the next couple days I would

read the official python standard

Library um in whatever downtime I had at

work throughout the day I would think of

potential solutions to the problems that

I was encountering and also trying to

anticipate any other problems I might

encounter as I tried to fix my script

and eventually I had an epiphany I

didn't have to create a script that

would solve all my problems at once I'm

way too much of a noob to be able to

write something as sophisticated as that

so instead I decided I would try to

break down my problems into the most

basic parts possible then I could

address them individually and then

package up all the pieces into a common

format that was much easier to work with

and that would allow me to get the

results I was after I took this New

Perspective and a fresh box of Diet Coke

and Mountain Dew and got back to

work

[Music]

I took a closer look at all the books in

this case review series and identified

what all the common elements were these

were the best targets for my regular

Expressions I developed a uniform

tagging system that I could Ed to label

each piece of information that I was

extracting it made it much easier to

manipulate and process the text in a

more consistent way and in a way that

didn't rely so heavily on consistent

formatting between books within the

series I changed how the scripts split

the text from each case which resolved

the multi-case issue that I was having

as far as the text was concerned I

updated the image naming convention to

make it more simple and straightforward

and so that it would apply across the

board to all the different books in the

series I also included New Logic that

helps the image extractor script

determine when and how to separate

pictures when they're presented in a

multi-case format by doing all this I've

established the trunk of my main script

pathway now anytime I work with a new

book I don't have to take it all the way

to the finished product I just have to

modify the data enough that it feeds

into this common pathway this will

significantly decrease the amount of

work that I have to do each time I

introduce a new book from the series and

finally I packaged all of these

different scripts into a simple app

which made it so I can process an entire

textbook and turn it into flashcards

with just a single

click

he