All language subtitles for 010 Parsing Future Responses into a Model_en

In the last section, we spoke about how our jet function right here returns a future, we get back

a future because we have to wait for that request to be completed before we can actually access the

JSON data, the data that we get back.

So in order to work with the future that comes back, I'm going to use the async 08 syntax to do so.

I'll first mark the inclosing function as being async by putting the async keyword right after my argument

list.

Then in front of the git function, I will write out the await keyword, which tells Darte We need to

wait for the future that gets returned from the get function to be resolved before we can work with

the actual data.

So then after we await that, we're going to take whatever information we got back from the AP and assign

it to a new variable that we will call response.

And claps aside so we can see a little bit.

OK.

Now, one thing I want to point out here is, is that this response variable is not the actual JSON

data.

The response variable is an object that tells us a little bit about the entire response that came back

from that API.

So it contains status codes, headers, a lot of information about the actual request that or the response

that came back from the request that JSON data that we actually care about is nested inside of that

response object.

So now that we've at least got something that contains the Jason day that we care about, we can now

take that, Jason, we can decode it and then we can create a new image model instance from it.

So to get started with that, I'm going to first make sure that I import that image model class that

we created a couple of seconds ago.

Up at the top of the file, I'm going to import and remember, this is an important statement for a

file that you and I wrote, so we don't have to write out the package keyword or the package word here.

We just write out the path to the file that we want to get.

So we're currently inside of Apte Dart.

We're going to go into the models directory and then get the image underscore model, dot, dot, file.

So let's say models slash image.

Oops, image model that darts.

OK, so that works now down here.

Right after we take that response back, we're going to create a new image model instance, so say var

image model equals image model.

We want to take the JSON out of this response object and construct a new image model with it.

So I'm going to call the named constructor from Jason.

And I'm going to pass in that, Jason, from the response object, which is available on response dogsbody.

Now, before we leave it right here, we did miss one step.

Do you know what we missed?

Well, this right here is Jason Data.

It's still that raw, Jason.

So we first have to decode that, Jason, and we decode it by writing out Jason Dot Decode.

And we pass in the response body like so.

OK, now you'll notice that Jason is red right here, says Undefined, named Jason, that is because

we have not imported the package from the dark standard library that contains the JSON object.

So at the very top, I'm going to make sure that I also add on an import statement for darte colon convert.

The convert module that's part of the DART Senior Library is where we get that JSON object from.

So we now get back our response.

The actual raw JSON is available on response to body, we decode that, that gives us some pass JSON

and then we use the front JSON named constructor that we created to create a new image model instance.

And that's it.

That's pretty much it.

So we've now got our image model.

We've definitely made some progress.

But you'll notice that we're not really doing anything with image model just yet.

So let's take a quick pause right here and we'll talk about what we're going to now do that we have

this image model sitting around because obviously we want to eventually somehow get it on the screen

of our device.

It's a quick break and we'll see you in just a minute.