Hey everyone, my name is Mukundan Sankar. I'm the host of the Data, AI, Productivity and Business
with a little personality podcast. We're going to dive into something really exciting. It's another
AI-powered solution and this time I'm applying it to PDFs. Okay, so let's dive into something that
I think is going to resonate with anyone who works with data and AI, PDFs. They're the lifeblood of
our field, but who actually has the time to read all of them? That's where this podcast gets really
interesting. It's an AI-powered tool that I built here that basically turbocharges your PDF game.
It can extract key information, turn it into audio and even generate Q&A. That's not all. It uses some
familiar tools in the data science world like PyPDF2 and Hugging Face. So I'm curious about
how much of a pain point PDFs are in your work. For me, it's a major one. In data science and AI,
staying current means sifting through a mountain of PDFs. And it's not just about keeping up.
Sometimes I really need to dig into a specific paper for a project. Even skimming for key
takeaways can take forever, especially with all the technical jargon. That's where I believe this
tool can be a game changer. Now let me break it down for you how this works. If I start with the
first step, it uses PyPDF2, which I spoke about. PyPDF2, let me talk about that briefly. So PyPDF2
helps you to extract all the key text from the PDF that you uploaded. A lot of people might be
familiar with this PyPDF2, especially if you're in the Python world and the data science world. So
it's a, and those of you who aren't, it's a Python-based library specifically designed for
working with PDFs. But the real magic happens in the next step. Summarization. The tool that I built,
it uses a summarizer from Hugging Face. And for those who aren't familiar with Hugging Face,
it is a platform with a ton of pre-trained AI models. So you can just use any AI model, like
text to video, text to audio, and all that would be available there. And this summarizer that I
built, yeah, you can obviously generate summary too. Summarizer for this particular use case,
it condenses those pages of information into concise digestible chunks. So imagine being able
to grasp the core concepts of a complex research paper without getting lost in the details.
And for those of you who are always on the go, get this. The tool can convert those summaries
into audio files using Google Text to Speech. That's amazing, right? Have you ever used Google
Text to Speech? It's again another Python-based library, but like as the name suggests, it
converts text to audio. And think about how you can use text to audio for your use case, for
anything, right? Like you're on the go, maybe you're driving somewhere and you don't have access to
maybe like your phone or you just like have access to a Bluetooth or something, right?
You are listening to something on the podcast and this podcast is something that you created.
That's where Google Text to Speech comes in. So basically anything that you've written,
you're converting that into audio. So you are listening essentially a podcast made by you
while working out. Another company that does this these days is Google again on their Notebook LM
product. So they are converting your regular text to a podcast. But yeah, this is a bit different,
but this is something that you built yourself. So there's like a different pleasure in that.
So just coming back to this, I think the possibilities are pretty exciting with this tool.
It's like having your PDF on the go, but you are listening to it. Like I said,
it gets even more interesting. Another function which I really want to talk about
is hugging faces, question and answer features. So basically it is used to generate questions
and answers from the PDF content. Now imagine preparing for an important meeting or presentation
and you could have AI highlight the key points and even anticipate the questions
your audience might ask. Now what's fascinating here is that it goes beyond just summarizing
the information. It's creating a more interactive way to engage with the content.
Now I built out a front end application using Streamlit, which is a Python based library,
but it will help you to see the content in a more interactive way. So I'm going to go ahead and
show you how it works, but it will help you to see data on a website. So it's like basically
you're creating a website and Python by itself wouldn't have been able to do it, I think.
And Streamlit really makes that process easy. So anybody who doesn't know how to do front end
programming can use Streamlit library from Python to make an application. So that's what I did in
I created an application which is asking the user to upload a PDF and generate a summary,
generate questions and answers and generate even audio. What is audio you might ask?
So what I spoke about before was the text to speech feature, right?
With the text to speech feature, it is converting the highlights of this text into an audio.
And this can obviously extend to other things like basically you can just convert the whole
text into audio. So I just didn't choose to do that, but I think if you wanted that could
be done as well. So all you have to do is just change a few bits of code and the blog post,
which actually has the step-by-step approach will be linked in the show notes. So do keep an eye on
that. So yeah, just think about this, like this PDF is not just about saving time. It can help you
absorb complex information more effectively, right? Imagine being able to quickly grasp the
key takeaways from a dozen research papers on a new technique. So you're learning something.
The other day I was learning about LLM quantization or something. And see, I mean,
I don't even know that topic yet. I don't know how to pronounce it, but I was trying to learn this,
get a summary of this using my tool. And it did provide like a quick summary. So it was nice,
obviously, I think notebook LLM is greater, but I think this is something that
you can build yourself and that gives me the pleasure of using my tool.
I can obviously use more improvements. And I would say just play around with the tool and
think what else you can do it to make it better. And if you have any suggestions,
feel free to send them my way. So it's important to definitely acknowledge that no technology is
perfect. And we know that AI is just a tool, right? It has its limitations, but it's exciting to see
how AI is evolving so rapidly. And it's helping tackling some of the biggest challenges we are
facing, right? Information overload. So obviously, like I said, this is just another AI application
and I'm hoping to keep generating more AI content, like more how tos, like how to do this using AI
and how to do that. So that's something that I get excited to do. Now, you can also think about
how you can do like an AI powered analysis on websites, right? And I just spoke about PDFs,
so we can extend it to websites, blog posts, and even books. It could completely revolutionize
how we learn and stay informed. So how could this tool fit into the workflow of someone in our field?
First and foremost, like I said, it's like a massive time saver. Now, instead of spending hours
on researching every paper from start to end, I could just use this tool quickly to extract
the key findings, identify the common themes, and even generate potential questions to guide
a deeper dive into specific aspects of the research. And that's a mouthful. It's just,
I want to be able to save time, right? And it's just not about research, like I said,
you're preparing for work, presentations, or a new visualization technique. And this tool could
quickly summarize the key points from several articles and white papers and even generate
potential questions that the audience might ask. Plus, it could be a life saver when I need to
quickly get up to speed on a new technology, right? Like if you want to learn something quickly,
a quick summary, yeah, just made your life very much easy. So I just always go for
optimize whenever I can. So I'm always trying to use technology, especially AI these days,
to make my life easier. And I hope you're also thinking on the similar lines.
So just imagine not just your PDF experience is turbocharged, getting key information,
and you're also getting it in audio, you're asking the questions and answers and getting
answers from it all by just using Python based libraries and AI. So this working through
PDFs is like a huge pain point and tools like this would definitely make life easier.
So staying current is definitely something we need to do in the data science and AI field.
So instead of just like going through so much PDF, just make your life easy and summarize them and
build out an AI power tool that really does everything for you.
So where could this go? I would say no tool is perfect. Like AI has its limitations.
That said, it's exciting to see how AI evolves into some of our, you know, evolves to tackle some of
our biggest challenges. And finally, just would like to say as AI continues to advance,
we'll even see more sophisticated tools emerge that will also that will transform how we interact
with information. It's an exciting time to be in this field. So question number one,
so question for you, what PDF would you run through this tool? How would you use all the time that
you saved? For those of us always under pressure to stay ahead, this tool could be a game changer.
But as with any powerful technology, it's essential to use it properly and responsibly.
And it's while it is really super tempting to see AI as a silver bullet, I do approach it as a
compliment to my own expertise. Ultimately, the balance lies in leveraging AI's power with
while keeping your critical thinking skills sharp. And AI challenges us to become even
better thinkers and learners, helping us engage with information actively, question it, and use
it to strengthen our understanding. So I hope you got some value out of this podcast today and I
hope to catch you in the next one. This is Mukundan Sankar. Thank you and I will see you in the next
episode.