WEBVTT 0 00:00.150 --> 00:04.500 Hey there. Welcome to day 39 of 100 Days of Code. 1 00:04.980 --> 00:05.400 Now, 2 00:05.400 --> 00:10.260 today we've got a two part series and this is the part 3 00:10.260 --> 00:12.360 one of your capstone project. 4 00:12.840 --> 00:15.450 So we've been learning about APIs for quite a while now, 5 00:15.660 --> 00:20.660 and we're going to be using a combination of different APIs to create a cheap 6 00:21.780 --> 00:24.720 flight finder. Part one, 7 00:25.020 --> 00:29.610 our program is going to find amazing flight deals just for ourselves. 8 00:29.910 --> 00:30.930 And in part two, 9 00:31.200 --> 00:36.200 we turn this project into a fully-fledged product where we can start signing up 10 00:36.780 --> 00:41.190 users to use our service. So I don't know about you, 11 00:41.220 --> 00:44.400 but I am a travel aficionado. 12 00:44.430 --> 00:46.890 I absolutely love to travel. 13 00:47.400 --> 00:51.030 I'm the sort of person who can't stay stationary in one location. 14 00:51.510 --> 00:56.190 And one of the reasons why I teach Programming online is to be able to travel 15 00:56.250 --> 00:58.020 and work from different places. 16 00:58.500 --> 01:02.910 Now I'm not that picky about where I go. I think everywhere I go, 17 01:02.910 --> 01:06.690 there's beautiful people who can teach me a lot of things. 18 01:07.290 --> 01:12.290 So instead of planning a trip where I have one destination and I plan out the 19 01:12.570 --> 01:16.350 time and date, I actually just look for a good deal. 20 01:16.710 --> 01:18.660 So when I can get a flight 21 01:18.660 --> 01:22.830 that's really cheap and it's to a location that I want to visit, 22 01:23.190 --> 01:27.930 then I pretty much just go for it. So for example, 23 01:28.440 --> 01:32.820 whereas a flight to New Zealand normally costs something like 800 pounds, 24 01:33.240 --> 01:36.900 I managed to get a flight for just a 350 pounds, 25 01:37.170 --> 01:39.030 and it was such a good deal. 26 01:39.060 --> 01:43.170 It even included a stopover in Beijing where I was able to get some tasty duck 27 01:43.710 --> 01:48.210 before I went onto the next 12-hour flight. Another case, um, 28 01:48.240 --> 01:52.950 was Japan where the flight normally costs around 500, 29 01:53.160 --> 01:58.140 550. And I managed to find a flight that was just a 250 pounds return. 30 01:58.680 --> 02:02.880 And the savings on the flight meant that I got to eat some extra tasty sushi. 31 02:03.420 --> 02:07.860 The way that these deals come about is imagine if you go onto a flight search 32 02:07.860 --> 02:11.100 website and you look for flights, 33 02:11.160 --> 02:12.990 anytime in the next six months, 34 02:13.290 --> 02:16.590 every day for lots of different locations. 35 02:16.980 --> 02:19.230 Then you will see that at some point, 36 02:19.230 --> 02:23.490 one of the flight prices will come up and it's much lower than what you expect 37 02:23.490 --> 02:27.600 it to be. And that's how you get a good deal. But of course, 38 02:27.750 --> 02:30.420 we're too busy to do that every single day manually. 39 02:30.810 --> 02:34.320 So here's how my program works. First, 40 02:34.350 --> 02:39.350 we have a Google sheet which keeps track of the locations that we want to visit 41 02:39.750 --> 02:44.520 and a price cutoff. So a historical low price. 42 02:44.850 --> 02:45.683 For example, 43 02:45.690 --> 02:50.690 maybe I want to go to Kerala and visit Kotchi and eat some tasty South Indian 44 02:51.390 --> 02:52.470 food. Well, 45 02:52.500 --> 02:57.060 maybe I would set the price at 350 pounds return from London. 46 02:57.750 --> 03:02.560 So we take this data from our Google sheet with lots of different locations 47 03:02.620 --> 03:07.180 and their lowest prices and we feed that into a flight search API, 48 03:07.570 --> 03:11.260 which is going to run every day, searching through all of the locations 49 03:11.530 --> 03:14.080 looking for the cheapest flight in the next six months. 50 03:14.650 --> 03:19.420 When it comes up with a hit and it finds a flight that's actually cheaper than 51 03:19.420 --> 03:20.800 our predefined price, 52 03:21.100 --> 03:26.100 then it's going to send that date and price via our Twilio SMS module to our 53 03:26.320 --> 03:29.110 mobile phone so that we can book it right there 54 03:29.110 --> 03:33.160 and then. That's the theory of it, but let's see it in action. 55 03:33.700 --> 03:38.700 So here I've got the code for my personal flight club and I'm going to run it 56 03:39.490 --> 03:44.490 and we're going to watch my phone and wait to see if there were any good deals 57 03:45.730 --> 03:46.900 that they found today. 58 03:47.800 --> 03:52.800 And we get a text message from our Twilio account and there we have it. Today's 59 03:54.580 --> 03:56.800 message says low price alert, 60 03:57.010 --> 04:02.010 only 41 pounds to fly from London Stansted to Berlin from the 25th of August to 61 04:03.970 --> 04:06.040 the 10th of September. 62 04:06.670 --> 04:11.670 That was triggered because it looked at my spreadsheet of flight prices and it 63 04:11.860 --> 04:14.770 found that out of all of these locations, 64 04:15.220 --> 04:20.140 the only flight that it found which was cheaper than my lowest price was 65 04:20.170 --> 04:22.600 for Berlin. It was actually only one pound cheaper. 66 04:23.440 --> 04:27.190 So now that I've got my message, I can go ahead and book my trip to Berlin. 67 04:28.720 --> 04:31.990 But before I do that, we're going to complete this capstone project. 68 04:32.410 --> 04:36.670 So head over to the next lesson and let's get started building this project.