Turning a whole textbook into Anki flashcards with one click (including images)
Summary
TLDRThe 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
📚 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.
🤖 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.
🛠 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.
Mindmap
Keywords
💡Custom GPTs
💡Medical Trainee
💡Flashcards
💡HTML Links
💡Anki
💡Radiology
💡Python
💡CSV File
💡Regular Expressions
💡JSON File
💡Spaced Repetition
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
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