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diff --git a/2023/captions/emacsconf-2023-matplotllm--matplotllm-iterative-natural-language-data-visualization-in-orgbabel--abhinav-tushar--main--chapters.vtt b/2023/captions/emacsconf-2023-matplotllm--matplotllm-iterative-natural-language-data-visualization-in-orgbabel--abhinav-tushar--main--chapters.vtt new file mode 100644 index 00000000..80483399 --- /dev/null +++ b/2023/captions/emacsconf-2023-matplotllm--matplotllm-iterative-natural-language-data-visualization-in-orgbabel--abhinav-tushar--main--chapters.vtt @@ -0,0 +1,17 @@ +WEBVTT + + +00:00:00.000 --> 00:01:40.719 +Introduction + +00:01:40.720 --> 00:02:23.599 +What is an LLM? + +00:02:23.600 --> 00:05:11.700 +Using this library + +00:05:11.701 --> 00:08:00.159 +Further instructions + +00:08:00.160 --> 00:09:33.480 +Room for improvement diff --git a/2023/captions/emacsconf-2023-matplotllm--matplotllm-iterative-natural-language-data-visualization-in-orgbabel--abhinav-tushar--main.vtt b/2023/captions/emacsconf-2023-matplotllm--matplotllm-iterative-natural-language-data-visualization-in-orgbabel--abhinav-tushar--main.vtt new file mode 100644 index 00000000..a01ffd80 --- /dev/null +++ b/2023/captions/emacsconf-2023-matplotllm--matplotllm-iterative-natural-language-data-visualization-in-orgbabel--abhinav-tushar--main.vtt @@ -0,0 +1,602 @@ +WEBVTT captioned by sachac, checked by sachac + +NOTE Introduction + +00:00:00.000 --> 00:00:03.039 +Hi, my name is Abhinav and I'm going to talk about + +00:00:03.040 --> 00:00:06.199 +this tool that I've been working on called MatplotLLM. + +00:00:06.200 --> 00:00:09.519 +MatplotLLM is a natural language interface + +00:00:09.520 --> 00:00:12.479 +over matplotlib, which is a library I use a lot + +00:00:12.480 --> 00:00:14.439 +for making visualizations. + +00:00:14.440 --> 00:00:18.679 +It's a pretty common Python library used a lot everywhere + +00:00:18.680 --> 00:00:22.479 +where there's need of plotting and graphing. + +00:00:22.480 --> 00:00:25.359 +I usually use it in reports. + +00:00:25.360 --> 00:00:27.359 +Whenever I'm writing a report in org mode, + +00:00:27.360 --> 00:00:31.559 +I tend to write a code block which is in Python. + +00:00:31.560 --> 00:00:34.079 +And then that code block has usage of matplotlib + +00:00:34.080 --> 00:00:35.999 +to produce some reports. + +00:00:36.000 --> 00:00:38.319 +That works really well. + +00:00:38.320 --> 00:00:39.999 +But at times what happens is + +00:00:40.000 --> 00:00:43.959 +I have to make a very custom graph, let's say. + +00:00:43.960 --> 00:00:46.919 +And then while I'm writing a report, + +00:00:46.920 --> 00:00:50.679 +it's kind of a huge leap of abstraction + +00:00:50.680 --> 00:00:51.519 +when I'm working on text + +00:00:51.520 --> 00:00:54.879 +versus going into actual low-level matplotlib code + +00:00:54.880 --> 00:00:56.239 +to do that graphing. + +00:00:56.240 --> 00:00:59.679 +So that's something I don't want to do. + +00:00:59.680 --> 00:01:00.479 +Here's an example. + +00:01:00.480 --> 00:01:03.999 +This is a graph which is... I think it was made + +00:01:04.000 --> 00:01:05.839 +like five or six years back. + +00:01:05.840 --> 00:01:08.399 +And then there are some common things + +00:01:08.400 --> 00:01:09.959 +like scatter plot here, + +00:01:09.960 --> 00:01:12.239 +the dots that you can see here scattered. + +00:01:12.240 --> 00:01:16.279 +Then... But there are a few things which, to do them, + +00:01:16.280 --> 00:01:19.159 +to make them, you will actually have to go--at least me, + +00:01:19.160 --> 00:01:20.839 +I have to go to the documentation + +00:01:20.840 --> 00:01:24.119 +and figure out how to do it. Which is fine, + +00:01:24.120 --> 00:01:26.519 +but I don't want to do this, you know, + +00:01:26.520 --> 00:01:29.199 +spend so much time here, when I'm working on + +00:01:29.200 --> 00:01:32.319 +a tight deadline for a report. + +00:01:32.320 --> 00:01:33.919 +That's the motivation for this tool. + +00:01:33.920 --> 00:01:35.199 +This tool basically allows me + +00:01:35.200 --> 00:01:38.479 +to get rid of the complexity of the library + +00:01:38.480 --> 00:01:40.719 +by working via an LLM. + +NOTE What is an LLM? + +00:01:40.720 --> 00:01:43.399 +So an LLM is a large language model. + +00:01:43.400 --> 00:01:45.079 +These are models which are + +00:01:45.080 --> 00:01:49.399 +trained to produce text, generate text. + +00:01:49.400 --> 00:01:51.519 +And just by doing that, + +00:01:51.520 --> 00:01:55.079 +they actually end up learning a lot of common patterns. + +00:01:55.080 --> 00:01:56.799 +For example, if you ask a question, + +00:01:56.800 --> 00:01:58.919 +you can actually get a reasonable response. + +00:01:58.920 --> 00:02:00.759 +If you ask to write a code for something, + +00:02:00.760 --> 00:02:01.879 +you'll actually get code + +00:02:01.880 --> 00:02:04.759 +which can also be very reasonable. + +00:02:04.760 --> 00:02:06.599 +So this tool is basically a wrapper + +00:02:06.600 --> 00:02:10.999 +that uses an LLM. For the current version, + +00:02:11.000 --> 00:02:13.919 +we use GPT-4, which is OpenAI's model. + +00:02:13.920 --> 00:02:17.919 +It's not open in the sense of open source. + +00:02:17.920 --> 00:02:21.119 +So that's a problem that it has. + +00:02:21.120 --> 00:02:23.599 +But for this version, we are going to use that. + +NOTE Using this library + +00:02:23.600 --> 00:02:25.479 +Using this library is pretty simple. + +00:02:25.480 --> 00:02:27.399 +You basically require the library + +00:02:27.400 --> 00:02:30.719 +and then you set up your OpenAI API key here. + +00:02:30.720 --> 00:02:33.359 +Then you get a code block + +00:02:33.360 --> 00:02:35.759 +where you can specify the language as `matplotllm`. + +00:02:35.760 --> 00:02:38.279 +And then what you can do is, + +00:02:38.280 --> 00:02:40.799 +you can basically describe what you want + +00:02:40.800 --> 00:02:41.799 +in natural language. + +00:02:41.800 --> 00:02:45.279 +I'll take this example of this data set. + +00:02:45.280 --> 00:02:48.599 +It's called the Health and Wealth of Nations. + +00:02:48.600 --> 00:02:49.639 +I think that was + +00:02:49.640 --> 00:02:51.399 +the name of a visualization where it was used. + +00:02:51.400 --> 00:02:53.399 +This is basically life expectancy, + +00:02:53.400 --> 00:02:59.279 +GDP of various countries starting from 1800. + +00:02:59.280 --> 00:03:02.719 +I think it goes up to 2000 somewhere. + +00:03:02.720 --> 00:03:07.479 +So earlier, I would try to write code which reads this CSV + +00:03:07.480 --> 00:03:09.839 +and then does a lot of matplotlib stuff + +00:03:09.840 --> 00:03:11.679 +and then finally produces a graph. + +00:03:11.680 --> 00:03:13.879 +But with this tool, what I'll do is + +00:03:13.880 --> 00:03:17.679 +I'll just provide instructions in two forms. + +00:03:17.680 --> 00:03:18.879 +So the first thing I'll do is + +00:03:18.880 --> 00:03:21.359 +I'll just describe how the data looks like. + +00:03:21.360 --> 00:03:29.039 +So I'll say data is in a file called `data.csv`, + +00:03:29.040 --> 00:03:33.159 +which is this file, by the way, on the right. + +00:03:33.160 --> 00:03:39.799 +It looks like the following. + +00:03:39.800 --> 00:03:44.359 +I just pasted a few lines from the top, which is enough. + +00:03:44.360 --> 00:03:47.119 +Since it's a CSV, there's already a structure to it. + +00:03:47.120 --> 00:03:50.079 +But let's say if you have a log file + +00:03:50.080 --> 00:03:53.759 +where there's more complexities to be parsed and all, + +00:03:53.760 --> 00:03:55.039 +that also works out really well. + +00:03:55.040 --> 00:03:58.079 +You just have to describe how the data looks like + +00:03:58.080 --> 00:04:01.159 +and the system will figure out how to work with this. + +00:04:01.160 --> 00:04:06.404 +Now, let's do the plotting. So what I can do is... + +00:04:06.405 --> 00:04:09.559 +Let's start from a very basic plot + +00:04:09.560 --> 00:04:11.620 +between life expectancy and GDP per capita. + +00:04:11.621 --> 00:04:13.800 +I'll just do this. + +00:04:13.801 --> 00:04:17.280 +"Can you make a scatter plot + +00:04:17.281 --> 00:04:26.399 +for life expectancy and GDP per capita?" + +00:04:26.400 --> 00:04:29.639 +Now, you can see there are some typos, + +00:04:29.640 --> 00:04:31.719 +and probably there will be some grammatical mistakes + +00:04:31.720 --> 00:04:32.919 +also coming through. + +00:04:32.920 --> 00:04:37.119 +But that's all OK, because the models are supposed to + +00:04:37.120 --> 00:04:40.559 +handle those kinds of situations really well. + +00:04:40.560 --> 00:04:43.239 +So I send the request to the model. + +00:04:43.240 --> 00:04:47.119 +Since it's a large model--GPT-4 is really large-- + +00:04:47.120 --> 00:04:50.519 +it actually takes a lot of time to get the response back. + +00:04:50.520 --> 00:04:53.359 +So this specific response took 17 seconds, + +00:04:53.360 --> 00:04:54.239 +which is huge. + +00:04:54.240 --> 00:04:57.439 +It's not something you would expect + +00:04:57.440 --> 00:04:59.599 +in a local file running on a computer. + +00:04:59.600 --> 00:05:01.879 +But I've got what I wanted. Right. + +00:05:01.880 --> 00:05:04.119 +So there's a scatter plot here, as you can see below, + +00:05:04.120 --> 00:05:08.879 +which is plotting what I specified it to do, + +00:05:08.880 --> 00:05:11.700 +though it looks a little dense. + +NOTE Further instructions + +00:05:11.701 --> 00:05:12.640 +What I can do is + +00:05:12.641 --> 00:05:16.000 +I can provide further instructions as feedback. + +00:05:16.001 --> 00:05:18.400 +I try to feed back on this. So I can say, + +00:05:18.401 --> 00:05:30.599 +"Can you only show points where year is the multiple of 50?" + +00:05:30.600 --> 00:05:33.519 +So since it's starting from 1800, the data points, + +00:05:33.520 --> 00:05:34.719 +there are too many years, + +00:05:34.720 --> 00:05:37.239 +so I'll just try to thin them down a little. + +00:05:37.240 --> 00:05:40.199 +Now what's happening in the background + +00:05:40.200 --> 00:05:42.719 +is that everything below this last instruction + +00:05:42.720 --> 00:05:45.719 +is going out as the context to the model + +00:05:45.720 --> 00:05:47.399 +along with the code that it wrote till now. + +00:05:47.400 --> 00:05:50.079 +And then this instruction is added on top of it + +00:05:50.080 --> 00:05:53.079 +so that it basically modifies the code to make it work + +00:05:53.080 --> 00:05:55.079 +according to this instruction. + +00:05:55.080 --> 00:05:58.439 +As you can see now, the data points are much fewer. + +00:05:58.440 --> 00:06:01.519 +This is what I wanted also. + +00:06:01.520 --> 00:06:02.799 +Let's also do a few more things. + +00:06:02.800 --> 00:06:05.439 +I want to see the progression through time. + +00:06:05.440 --> 00:06:13.079 +So maybe I'll do something like, color more recent years + +00:06:13.080 --> 00:06:15.439 +with a darker shade of... + +00:06:15.440 --> 00:06:21.719 +Let's change the color map also. + +00:06:21.720 --> 00:06:24.159 +Now, this again goes back to the model. + +00:06:24.160 --> 00:06:26.799 +Again, everything below before this line + +00:06:26.800 --> 00:06:29.119 +is the context along with the current code, + +00:06:29.120 --> 00:06:31.799 +and then this instruction is going to the model + +00:06:31.800 --> 00:06:37.039 +to make the changes. So now this should happen, I guess. + +00:06:37.040 --> 00:06:41.319 +Once this happens. Yeah. So. OK. + +00:06:41.320 --> 00:06:44.599 +So we have this new color map, + +00:06:44.600 --> 00:06:46.599 +and there's also this change of color. + +00:06:46.600 --> 00:06:51.719 +And also there's this range of color from 1800 to 2000, + +00:06:51.720 --> 00:06:53.399 +which is a nice addition. + +00:06:53.400 --> 00:06:55.839 +Kind of smart. I didn't expect... + +00:06:55.840 --> 00:06:58.959 +I didn't exactly ask for it, but it's nice. + +00:06:58.960 --> 00:07:00.959 +So there's a couple more things. + +00:07:00.960 --> 00:07:07.759 +Let's make it more minimal. "Let's make it more minimal. + +00:07:07.760 --> 00:07:17.319 +Can you remove the bounding box?" + +00:07:17.320 --> 00:07:21.399 +Also, let's annotate a few points. + +00:07:21.400 --> 00:07:23.719 +So I want to annotate the point + +00:07:23.720 --> 00:07:25.839 +which has the highest GDP per capita. + +00:07:25.840 --> 00:07:33.599 +"Also annotate the point with highest GDP per capita + +00:07:33.600 --> 00:07:36.999 +with the country and year." + +00:07:37.000 --> 00:07:41.599 +So again, forget about the grammar. + +00:07:41.600 --> 00:07:43.599 +The language model works out well. + +00:07:43.600 --> 00:07:46.159 +Usually it takes care of + +00:07:46.160 --> 00:07:47.439 +all those complexities for you. + +00:07:47.440 --> 00:07:53.119 +This is what we have got after that. + +00:07:53.120 --> 00:07:55.719 +As you can see, there's the annotation, which is here. + +00:07:55.720 --> 00:07:56.679 +I think it's still overlapping, + +00:07:56.680 --> 00:07:58.559 +so probably it could be done better, + +00:07:58.560 --> 00:08:00.159 +but the box is removed. + +NOTE Room for improvement + +00:08:00.160 --> 00:08:03.359 +Now, as you can see, the system is... + +00:08:03.360 --> 00:08:04.879 +You will be able to see this + +00:08:04.880 --> 00:08:07.479 +that the system is not really robust. + +00:08:07.480 --> 00:08:10.079 +So the GitHub repository has some examples + +00:08:10.080 --> 00:08:12.119 +where it fails miserably, + +00:08:12.120 --> 00:08:13.679 +and you'll actually have to go into the code + +00:08:13.680 --> 00:08:14.999 +to figure out what's happening. + +00:08:15.000 --> 00:08:17.879 +But we do expect that to improve slowly, + +00:08:17.880 --> 00:08:21.039 +because the models are improving greatly in performance. + +00:08:21.040 --> 00:08:22.479 +This is a very general model. + +00:08:22.480 --> 00:08:24.479 +This is not even tuned for this use case. + +00:08:24.480 --> 00:08:26.639 +The other thing is that + +00:08:26.640 --> 00:08:29.639 +while I was trying to provide feedback, + +00:08:29.640 --> 00:08:32.199 +I was still using text here all the time, + +00:08:32.200 --> 00:08:34.559 +but it can be made more natural. + +00:08:34.560 --> 00:08:36.159 +So, for example, if I have to annotate + +00:08:36.160 --> 00:08:37.439 +this particular point, + +00:08:37.440 --> 00:08:42.239 +I actually can just point my cursor to it. + +00:08:42.240 --> 00:08:44.519 +Emacs has a way to figure out + +00:08:44.520 --> 00:08:45.799 +where your mouse pointer is. + +00:08:45.800 --> 00:08:49.620 +And with that, you can actually go back into the code + +00:08:49.621 --> 00:08:51.960 +and then see which primitive + +00:08:51.961 --> 00:08:54.480 +is being drawn here in Matplotlib. + +00:08:54.481 --> 00:08:55.719 +So that there is a way to do that. + +00:08:55.720 --> 00:08:58.439 +And then, if you do that, then it's really nice to + +00:08:58.440 --> 00:09:01.319 +just be able to say + +00:09:01.320 --> 00:09:04.279 +put your cursor here and then say something like, + +00:09:04.280 --> 00:09:04.999 +"Can you make this? + +00:09:05.000 --> 00:09:06.599 +Can you annotate this point?" + +00:09:06.600 --> 00:09:10.719 +Because text is, you know... There are limitations to text. + +00:09:10.720 --> 00:09:12.479 +And if you're producing an image, + +00:09:12.480 --> 00:09:13.959 +you should be able to do that, too. + +00:09:13.960 --> 00:09:16.399 +So I do expect that to happen soonish. + +00:09:16.400 --> 00:09:19.839 +If not, from the model side, the hack that I mentioned + +00:09:19.840 --> 00:09:21.359 +could be made to work. + +00:09:21.360 --> 00:09:24.439 +So that will come in in a later version, probably. + +00:09:24.440 --> 00:09:27.599 +Anyway, so that's the end of my talk. + +00:09:27.600 --> 00:09:29.759 +You can find more details in the repository link. + +00:09:29.760 --> 00:09:33.480 +Thank you for listening. Goodbye. |