path: root/2023/captions/emacsconf-2023-matplotllm--matplotllm-iterative-natural-language-data-visualization-in-orgbabel--abhinav-tushar--main.vtt

WEBVTT captioned by sachac, checked by sachac

NOTE Introduction

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Hi, my name is Abhinav and I'm going to talk about

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this tool that I've been working on called MatplotLLM.

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MatplotLLM is a natural language interface

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over matplotlib, which is a library I use a lot

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for making visualizations.

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It's a pretty common Python library used a lot everywhere

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where there's need of plotting and graphing.

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I usually use it in reports.

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Whenever I'm writing a report in org mode,

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I tend to write a code block which is in Python.

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And then that code block has usage of matplotlib

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to produce some reports.

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That works really well.

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But at times what happens is

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I have to make a very custom graph, let's say.

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And then while I'm writing a report,

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it's kind of a huge leap of abstraction

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when I'm working on text

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versus going into actual low-level matplotlib code

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to do that graphing.

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So that's something I don't want to do.

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Here's an example.

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This is a graph which is... I think it was made

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like five or six years back.

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And then there are some common things

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like scatter plot here,

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the dots that you can see here scattered.

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Then... But there are a few things which, to do them,

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to make them, you will actually have to go--at least me,

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I have to go to the documentation

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and figure out how to do it. Which is fine,

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but I don't want to do this, you know,

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spend so much time here, when I'm working on

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a tight deadline for a report.

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That's the motivation for this tool.

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This tool basically allows me

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to get rid of the complexity of the library

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by working via an LLM.

NOTE What is an LLM?

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So an LLM is a large language model.

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These are models which are

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trained to produce text, generate text.

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And just by doing that,

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they actually end up learning a lot of common patterns.

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For example, if you ask a question,

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you can actually get a reasonable response.

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If you ask to write a code for something,

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you'll actually get code

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which can also be very reasonable.

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So this tool is basically a wrapper

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that uses an LLM. For the current version,

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we use GPT-4, which is OpenAI's model.

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It's not open in the sense of open source.

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So that's a problem that it has.

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But for this version, we are going to use that.

NOTE Using this library

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Using this library is pretty simple.

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You basically require the library

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and then you set up your OpenAI API key here.

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Then you get a code block

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where you can specify the language as `matplotllm`.

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And then what you can do is,

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you can basically describe what you want

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in natural language.

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I'll take this example of this data set.

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It's called the Health and Wealth of Nations.

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I think that was

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the name of a visualization where it was used.

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This is basically life expectancy,

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GDP of various countries starting from 1800.

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I think it goes up to 2000 somewhere.

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So earlier, I would try to write code which reads this CSV

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and then does a lot of matplotlib stuff

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and then finally produces a graph.

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But with this tool, what I'll do is

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I'll just provide instructions in two forms.

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So the first thing I'll do is

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I'll just describe how the data looks like.

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So I'll say data is in a file called `data.csv`,

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which is this file, by the way, on the right.

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It looks like the following.

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I just pasted a few lines from the top, which is enough.

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Since it's a CSV, there's already a structure to it.

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But let's say if you have a log file

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where there's more complexities to be parsed and all,

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that also works out really well.

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You just have to describe how the data looks like

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and the system will figure out how to work with this.

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Now, let's do the plotting. So what I can do is...

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Let's start from a very basic plot

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between life expectancy and GDP per capita.

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I'll just do this.

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"Can you make a scatter plot

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for life expectancy and GDP per capita?"

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Now, you can see there are some typos,

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and probably there will be some grammatical mistakes

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also coming through.

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But that's all OK, because the models are supposed to

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handle those kinds of situations really well.

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So I send the request to the model.

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Since it's a large model--GPT-4 is really large--

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it actually takes a lot of time to get the response back.

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So this specific response took 17 seconds,

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which is huge.

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It's not something you would expect

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in a local file running on a computer.

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But I've got what I wanted. Right.

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So there's a scatter plot here, as you can see below,

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which is plotting what I specified it to do,

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though it looks a little dense.

NOTE Further instructions

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What I can do is

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I can provide further instructions as feedback.

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I try to feed back on this. So I can say,

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"Can you only show points where year is the multiple of 50?"

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So since it's starting from 1800, the data points,

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there are too many years,

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so I'll just try to thin them down a little.

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Now what's happening in the background

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is that everything below this last instruction

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is going out as the context to the model

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along with the code that it wrote till now.

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And then this instruction is added on top of it

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so that it basically modifies the code to make it work

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according to this instruction.

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As you can see now, the data points are much fewer.

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This is what I wanted also.

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Let's also do a few more things.

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I want to see the progression through time.

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So maybe I'll do something like, color more recent years

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with a darker shade of...

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Let's change the color map also.

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Now, this again goes back to the model.

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Again, everything below before this line

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is the context along with the current code,

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and then this instruction is going to the model

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to make the changes. So now this should happen, I guess.

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Once this happens. Yeah. So. OK.

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So we have this new color map,

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and there's also this change of color.

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And also there's this range of color from 1800 to 2000,

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which is a nice addition.

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Kind of smart. I didn't expect...

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I didn't exactly ask for it, but it's nice.

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So there's a couple more things.

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Let's make it more minimal. "Let's make it more minimal.

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Can you remove the bounding box?"

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Also, let's annotate a few points.

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So I want to annotate the point

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which has the highest GDP per capita.

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"Also annotate the point with highest GDP per capita

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with the country and year."

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So again, forget about the grammar.

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The language model works out well.

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Usually it takes care of

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all those complexities for you.

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This is what we have got after that.

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As you can see, there's the annotation, which is here.

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I think it's still overlapping,

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so probably it could be done better,

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but the box is removed.

NOTE Room for improvement

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Now, as you can see, the system is...

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You will be able to see this

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that the system is not really robust.

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So the GitHub repository has some examples

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where it fails miserably,

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and you'll actually have to go into the code

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to figure out what's happening.

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But we do expect that to improve slowly,

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because the models are improving greatly in performance.

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This is a very general model.

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This is not even tuned for this use case.

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The other thing is that

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while I was trying to provide feedback,

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I was still using text here all the time,

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but it can be made more natural.

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So, for example, if I have to annotate

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this particular point,

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I actually can just point my cursor to it.

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Emacs has a way to figure out

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where your mouse pointer is.

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And with that, you can actually go back into the code

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and then see which primitive

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is being drawn here in Matplotlib.

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So that there is a way to do that.

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And then, if you do that, then it's really nice to

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just be able to say

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put your cursor here and then say something like,

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"Can you make this?

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Can you annotate this point?"

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Because text is, you know... There are limitations to text.

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And if you're producing an image,

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you should be able to do that, too.

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So I do expect that to happen soonish.

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If not, from the model side, the hack that I mentioned

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could be made to work.

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So that will come in in a later version, probably.

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Anyway, so that's the end of my talk.

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You can find more details in the repository link.

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Thank you for listening. Goodbye.