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Great, we are in ver2 and I'll try to

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be quick now and show you ver3.

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This is ver3 and the way I implemented

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this functionality, where I want to check if there

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is an address column in the DataFrame, is

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by adding a try and expect statement.

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So basically, if you can see the difference is,

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[No Audio]

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I get the file and then I try to read the file.

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[No Audio]

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Or this could also be outside of the

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try keyword, that wouldn't be a problem and

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gc outside of a try keyword as well.

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However, this is still better because, you may also want

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to check for users sending files that are not csv.

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So here we're checking actually that the

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user is submitting a csv file.

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However, that only means that the file has

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a .csv extension in the file name.

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However, that doesn't mean that

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the file is actually csv.

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So you may have, let's say an mp4

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file and you have changed that the extension

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to csv, but that's still an mp4 file.

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So what you want to do is, you want

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to include df, inside try and expect block.

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If Python is not being able to create a dataframe

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out of an mp4 file, then it will throw an error.

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So that's what actually happens

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if you pass an mp4 file, you wouldn't be

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able to read it as a dataframe.

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So yeah, you want to include that in here.

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Then you return

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[No Audio]

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the same, so I haven't changed anything here.

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You return the same template index.html and you send

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the html there and the button. Except, if there is

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an error, what you do is you return the index.html,

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but then instead of the HTML table, you want

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to return this message. And of course, you don't want

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to return any download button in there.

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And yeah, that should do it.

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And that's about ver3. So those were the differences.

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Now, version four, what I have there, well, if

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you see here, we got this geocoded.csv file.

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Now that's a string and that means that,

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a geocoded.csv file will be created for

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all the users, that will be submitting data.

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Now that may cause some problems, because if

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two users are submitting data at the same

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time, you may have some name clashes there.

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So what you could do here is you

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could use, a datetime module to generate

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unique names for every generated file.

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And that's what I did.

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So here is ver4.

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I imported the datetime module in here and

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then, I'm generating the geocoded.csv file in here.

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So df to_csv filename. And I made this

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filename global in here, because I also want to

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access it from the download function in here.

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So again, I want to generate that in here.

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I'm generating a filename.

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Then in the filename we have the

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upload string, which will point to the fall

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to the directory, where this file will be.

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Then we have the slash.

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And actually, this plus shouldn't be there.

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So just after the slash, we have the filename.

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And that would, you know, we have year, and then we

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have the month, and then the day, and then the

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hour, and the minutes and seconds and milliseconds, and then

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the .csv extension in the filename.

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So, yeah, this is quite unique for every

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user, because we have milliseconds in there.

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But let me show you how this looks like, actually.

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So, again, the user chooses a file, Submit.

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And when I press Submit, Python will generate that dataframe,

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and it will also generate this csv file.

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So in this line here. And you can now

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find that csv file in the uploads folder.

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So this was generated earlier, and this is the file.

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That's the file that we just generated.

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Then when the user presses Download, that

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file will be downloaded, but with your

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filename, which is this one here.

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And, yeah, what happens is that the download function

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will get the path of the file that, it

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has to download in the browser, will get the

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filename from this global variable.

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That's why I am passing this as a

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global variable, so that I can access the

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value of it, which is generated in here.

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I can access this value from another function.

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And, yeah, that's my version of the application.

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I know this is not like your version.

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I hope you were close, as much as possible. It would be great,

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if you did it better than me.

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In either case, I'm sure that, trying to solve

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this application should have improved your problem solving skills

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in Python, because that at least will position you,

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so will define your level, your Python level, so

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that you can fill the gaps, that you really

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think, are not your strongest points.

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And, yeah, that was about this lecture.

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And I'll see you.