video create

Video upload

Normal

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Erikka Innes

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Tutorials

JavaScript

Python

Hello Pythonistas! Today let's learn how to upload a video using Python's Flask and Dropzone.js. 
In this tutorial, we'll use Python (for a version where you use our new api.video client, please see: ) and Dropzone.js. 

Flask is a WSGI web application framework. It makes getting started with building a web application quick and easy. It's based on the Werkzeug WSGI toolkit and the Jinja2 template engine. It's very popular for creating backends for webpages, and that's what I'll be using it for today.

Dropzone.js is an open source JavaScript library that lets you create drag n' drop file uploads with image previews (when available). It doesn't have dependencies on other libraries, and it does a lot of work for you so you can focus on styling and configuration. I'll use a very simple format in this tutorial, but you will be able to style it however you want. 

## Prerequisites 

For this project you'll need: 

* Python installed
* Virtual environment for your project (optional)
* Flask installed
* Project files - https://github.com/apivideo/python-examples/tree/main/videos/Flask%20with%20Dropzone 
* Api.video account - if you do do have one, you can [create one for free](https://dashboard.api.video/register)

## Set up 
To get started, you'll need to do these things: 

1. Grab the code from [Flask and Dropzone.js](https://github.com/apivideo/python-examples/tree/main/videos/Flask%20with%20Dropzone).

2. In my version of the project, Dropzone.js is attached using CDNs. You can also reference these using the static folder and placing dropzone.js and dropzone.css there. Just change the references in **index.html** if you want to do this instead. 

## What's in index.HTML

1. Let's start by walking through the HTML file **index.html**. It's in the **templates** folder. 

2. First add the scripts and stylesheets you'll need for the project. I'm using Dropzone with bootstrap. So I've added the scripts and stylesheets for that.

3. Next let's skip ahead to the body of the HTML code and look at the way the form is set up. I place the form between the body tags, and I'm using a simple form, so it looks like this:

4. I add a form action to show where I want the information to be sent to. In this case I'm using "/" as the route. Next, I add the class **dropzone**. This tells Dropzone where to add the drag n' drop zone. In the form I add an ID, because I'll have to make some changes to how Dropzone is set up, and I specify that it's going to be a POST request to "/". Along with sending files, I want to send the API key, so I add a field for that, and I want everything to send when I press **Submit** so I add a button for that. 

5. Now let's take a look at customizing the dropzone. By default, Dropzone wants to upload your file the second you drop it in the zone. I don't want that to happen, because I'll get an error. The file (or files) need to be sent with my API key in order to be posted on the api.video servers. So the first thing I'm going to do is set up to configure some Dropzone options. In order to reference my Dropzone, I use the ID written in camelcase. The zone ID I chose is **my-dropzone** so that becomes **myDropzone**. I chose to add the script directly to the HTML in the head tags, but you can reference it from a separate file if you prefer.  

6. To prevent Dropzone from trying to upload files right away, I set **autoProcessQueue** to **false**. I want users to be able to upload multiple files, so I put **true** for uploadMultiple. **parallelUploads** I set to 10 max and I set the total number of files you can add at one time (**maxFiles**) to 10.
 
7. Next I set up the submit button. The code here tells Dropzone to process the files after the user hits **Submit**. And that's it for the script! Let's set up the backend next.

## What's in app.py
Here's what's set up for the backend: 


1. I start with imports for features of Flask, the os library, and the requests library.
 
2. Add limits to what kind of files a user can upload with the line: ```app.config['UPLOAD_EXTENSIONS'] = ['.mov', '.mp4', '.m4v', '.jpm', '.jp2', '.3gp', '.3g2', '.mkv', '.mpg', '.ogv', '.webm', '.wmv' ]``` 

3. This demo only handles files up to 199 MiB so let's add an error handler route (@app.errorhandler(413)). 

4. When you start up the app, I want you to see the **index.html** template. So that template is returned for @app.route('/').

5. To process the file and API key, they must be retrieved from request.files and request.form. request.files can contain more than one file, so I'm assigning the files to my_files. 

6. Assign the API key from the form to api_key. 

7. You'll recognize the set up after this from previous tutorials - it's setting up to retrieve an access token with the API key, then creating the headers with the token so everything is ready to start posting files. 

8. Because my_files could have one or many files and I want to be sure to process them all, I'm using a for loop. The code here creates a video container to upload to and adds the title. It retrieves the video_id so a video can be uploaded to the container. Then it sets up the request and posts the video into the container. It will loop through every video and send it, so long as the video is under 200 MiB. 



That's it! You now have a way to upload videos using Dropzone and Flask without using the Python client. In the next tutorial, we'll upload using the Python client. The client will allow us to automatically handle chunked uploads and handle videos of any size. 
Don't hesitate to reach us on [our Community](https://community.api.video/)!

### Resources 

To build this tutorial, I used this documentation, and it might help you too: 

1. [Handling File Uploads with Flask](https://blog.miguelgrinberg.com/post/handling-file-uploads-with-flask) - A great tutorial that shows how to set up with a basic Dropzone. 
2. [Dropzonejs.com](https://www.dropzonejs.com/) - Details about how to customize Dropzone. 

Use Flask with Dropzone.js to upload videos under 200 MiB

Use Flask with Dropzone.js to upload videos under 128MB

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Use Flask with Dropzone.js to upload videos under 200 MiB (no client)

Delegated Upload

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Doug Twitter

Doug Sillars

The first demo we published on a.video was a demo on how to upload a video (check it out yourself at [https://upload.a.video](https://upload.a.video). This is a great little demo that will take your video, break it into smaller chunks (1 MB in this case), and upload them into api.video. Once the uploads are complete, you get a link to the url where the video will soon be available for playback. You can read the [post describing how we built the app](https://api.video/blog/tutorials/uploading-large-files-with-javascript/).

## More than one video

What if you'd like to upload a few videos? You could use this interface multiple times, but that would be slow and tedious.  

To resolve this, I've created a new version of the uploader at [https://upload.a.video/many.html](https://upload.a.video/many.html) that allows you to upload more than one video at a time. It only took about 5 lines of added code to make the change from one video to many.

Let's walk through what I did to make this happen.

### Input

First, we need to allow the form to accept multiple files.  This is a one word change:

```
	<input type="file" id="video-file" style='display:none'>
```

to

```
	<input type="file" id="video-file" style='display:none' multiple>
```

This basically tells the browser that users can select more than one file at a time. With this line added, you can select multiple adjacent files while holding the shift key, or multiple non-adjacent files holding the command (on a Mac) key.

### Uploading multiple files

In upload a video, only one video could be uploaded at a time. When you add files via a form, they are placed in an array, so once the button to upload was clicked, we defined the video and the video name based on the file in index 0 of the array:


```

			const file = input.files[0];
			const filename = input.files[0].name;
			
			<upload stuff>

```

There's just one file, so grab the file in index 0, grab the filename and upload the file to api.video.  Extending this to more than one file is easy: create a loop over the array of files (incrementing index i), and upload each file in the array by referencing file i in the array:

```
  const numberofFiles = input.files.length;
	for( i=0;i< numberofFiles; i++){
			//do the upload for each file.

			const file = input.files[i];
			const filename = input.files[i].name;
			<upload stuff>
			
	}

```


### Make the UI look nice

So that was probably the 5 lines of code I promised, (6 if you count the comment).  But the UI was weird, since the notifications and text were updating and overlapping for each file being uploaded - and you couldn't read anything as the text was changing so quickly.  So, let's fix that.  I changed 3 lines of code here:

First: the upload information is changed to show information for each video by appending the HTML and adding the video number: 

```
			document.getElementById("video-information").innerHTML += "Video "+ i +" will be uploaded in " + numberofChunks + " chunks.<br/>"

```


Second: as the videos upload, so the upload information should update for each video:

```
document.getElementById("chunk-information").innerHTML = "Video: " +i+" Chunk # " + chunkCounter + " is " + percentComplete + "% uploaded. Total uploaded: " + totalPercentComplete +"%";
```

This one just overwrites itself all the time.  I could probably make this look nicer, but for a demo, it's ok.

When the upload is completed, we add the completed upload notice, along with the url where the playback can be watched:

```
	document.getElementById("video-information").innerHTML += "Video " +i+" is uploaded! Watch the video <a href=\'" + playerUrl +"\' target=\'_blank\'>here</a><br/>" ;

```

All three of these lines were already in the code, I just updated them to fit the multiple upload function of the application. That said, with a grand sum of 8 lines of uploaded code, we've modified [https://upload.a.video](https://upload.a.video) to [https://upload.a.video/many.html](https://upload.a.video/many.html), allowing for more than one video to be uploaded at once.

## Try it out

With ~10 lines of modified code, I've taken a page built for a single video uploaded a page that can now handle many videos, and show the links to the videos when they are completed.

That said, since this is a live demo, I have placed a limit of 3 files per upload, just to keep my account from filling up with videos.  These videos will also be stored in the sandbox: so watermarked and deleted after 24 hours.

```
var maxUpload = Math.min(numberofFiles, 3);

		for( i=0;i< maxUpload; i++){
			<file selection & upload stuff>
		}
```

The code is live at [https://upload.a.video](https://upload.a.video), and available on [Github](https://github.com/dougsillars/blobUpload/blob/master/many.html). Give it a try, and let us know what you think!

Upload many files at once with HTML and JavaScript

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Authenticate

If you have a large video you need to upload, sometimes the server you're uploading to will not accept it in one request. Different servers have different limits. For api.video, you can upload a video file up to a size of 199 MiB before your file is considered too big. Once your video crosses this limit, to upload it you'll need to break it into chunks and send it that way. You send each chunk and the server reassembles it for you. 

In this tutorial, we'll walk through a simple example of how to do a large video upload.

<center><iframe src="https://giphy.com/embed/jVeI3SzbxKGKQ" width="480" height="480" frameBorder="0" class="giphy-embed" allowFullScreen></iframe><p><a href="https://giphy.com/gifs/vhs-vcr-jVeI3SzbxKGKQ">This looks like it would turn into a really big video file.</a></p><br></center>

## Prerequisites
* [Account](https://dashboard.api.video) with api.video 
* api.video API key 
* You'll need access to Python and you'll need to install the requests library if you don't already have it added

And if you'd rather watch this tutorial than read it, check out our video tutorial here: 

## What You'll Be Doing 
This is a quick summary of what you're going to do to upload your large video. 

1. You'll authenticate with api.video using your API key and retrieve a token that lasts one hour that you can use to access all of api.video's other endpoints.
2. You'll create a video container for your video and retrieve the videoId. 
3. You'll use the videoId to create the endpoint you'll send your video to.
4. You'll break up your video file into chunks, then upload each chunk of the video file.

## Troubleshooting / What to Watch Out For 
If you decide to build your own uploader, a few things will help you out: 

* Remember that you must open the file as binary and work with it as binary. It's easy to forget and open it without adding 'rb' or depending on what you're doing, 'wb'.
* Headers are very important. The wrong header will prevent you from sending your content. 
* When using the Content-Range header, you must make sure you are listing the correct set of bytes being sent. If you are off by even one byte, it won't work. You will get weird errors like: 
```UnicodeDecodeError: 'utf-8' codec can't decode byte 0xd1 in position 52: invalid continuation byte``` And the solution is often a simple fix - correct how you're listing your bytes in the header.
* When you send bytes in the Content-Range header, it's inclusive. If you want to send the first 100 bytes, you write it as 0-99. This can get confusing when you're breaking your file into chunks because you can say 'grab the first 100 bytes' and the computer knows to grab 0-99. When you say 'grab the next 100 bytes,' the computer would grab 100-199. However, it's easy to write a dynamic header where you make mistakes like listing 0-100 instead of 0-99, or starting over on the same byte, for example 0-100, then 100-200. All these kinds of mistakes will cause a decode or encoding error. If you choose to build your own uploader instead of using this sample, keep this in mind. 
* If you label what type of file you're sending, you may cause unexpected issues. This sample works by opening, reading, and sending your video file as chunks of binary data. When it arrives, it's encoded into HLS for you, no matter what kind of video it started out as. 

## Code Sample 

```
## How to upload a large video that is over 199 MiB to api.video. (Though this script will also work for videos under 200 MiB if you want to test it out.)

import requests
import os 

## Set up variables for endpoints (we will create the third URL programmatically later)
auth_url = "https://ws.api.video/auth/api-key"
create_url = "https://ws.api.video/videos"

## Set up headers and payload for first authentication request
headers = {
    "Accept": "application/json",
    "Content-Type": "application/json"
}

payload = {
    "apiKey": "your API key here"
}

## Send the first authentication request to get a token. The token can be used for one hour with the rest of the API endpoints.
response = requests.request("POST", auth_url, json=payload, headers=headers)
response = response.json()
token = response.get("access_token")

## Set up headers for authentication - the rest of the endpoints use Bearer authentication.

auth_string = "Bearer " + token

headers2 = {
    "Accept": "application/json",
    "Content-Type": "application/json",
    "Authorization": auth_string
}

## Create the video container payload, you can add more parameters if you like, check out the docs at https://docs.api.video
payload2 = {
    "title": "Demo Vid from my Computer",
    "description": "Video upload test."
}

## Send the request to create the container, and retrieve the videoId from the response.
response = requests.request("POST", create_url, json=payload2, headers=headers2)
response = response.json()
videoId = response["videoId"]

## Create endpoint to upload video to - you have to add the videoId into the URL
upload_url = create_url + "/" + videoId + "/source"

## Set up the chunk size. This is how much you want to read from the file every time you grab a new chunk of your file to read.
## If you're doing a big upload, the recommendation is 50 - 80 MB (50000000-80000000 bytes). It's listed at 6MB (6000000 bytes) because 
## then you can try this sample code with a small file just to see how it will work.  The minimum size for a chunk is 5 MB.

CHUNK_SIZE = 6000000

## This is our chunk reader. This is what gets the next chunk of data ready to send.
def read_in_chunks(file_object, CHUNK_SIZE):
    while True:
        data = file_object.read(CHUNK_SIZE)
        if not data:
            break
        yield data

## Upload your file by breaking it into chunks and sending each piece 
def upload(file, url):
    content_name = str(file)
    content_path = os.path.abspath(file)
    content_size = os.stat(content_path).st_size

    print(content_name, content_path, content_size)

    f = open(content_path, "rb")

    index = 0
    offset = 0
    headers = {}

    for chunk in read_in_chunks(f, CHUNK_SIZE):
        offset = index + len(chunk)
        headers['Content-Range'] = 'bytes %s-%s/%s' % (index, offset -1, content_size)
        headers['Authorization'] = auth_string
        index = offset
        try:

            file = {"file": chunk}
            r = requests.post(url, files=file, headers=headers)
            print(r.json())
            print("r: %s, Content-Range: %s" % (r, headers['Content-Range']))
        except Exception as e:
            print(e)

## Add a path to the file you want to upload, and away we go! 
upload('your-giant-file-here.mp4', upload_url)
```

## Code Walkthrough

Wanna know how it works in a little more detail? Great, here we go! The first couple of sections might look like some of the other blog posts, scroll down for a discussion about the new stuff if that's the case. 

You'll start by adding the requests and os libraries, so you can easily make HTTP requests.

```
import requests
import os
```

Next, let's assign a variable to each endpoint we'll use, they'll be a little easier to work with. We're going to assign two of them here, and we'll use the second one later to build the third one: 
* [Authentication](https://docs.api.video/reference/basic-authentication) endpoint - This is where we send our API key to exchange it for a token. It's **auth_url** in the code sample.
* [Videos](https://docs.api.video/reference/api/Videos#create-a-video-object) endpoint - Specifically we'll use the feature on this endpoint that's for creating a video. It's **create_url** in the code sample.
* [Video Upload](https://docs.api.video/reference/api/Videos#upload-a-video) endpoint - It looks a lot like the other endpoint. It's create_url + "/videoId" + "/source". We'll construct this after we get a videoId to work with. 

```
auth_url = "https://ws.api.video/auth/api-key"
create_url = "https://ws.api.video/videos"
```

### Authentication - Get Your Token
We'll prepare our headers and payload for authentication, then send a POST request to api.video. With the api.video API you start authentication by sending a request using your API key to retrieve a token. You can use your sandbox or your production key, the backend will figure out which you are using and handle it accordingly. If you choose to use your sandbox credentials for this walkthrough, your video will be uploaded with the watermark 'FOR DEVELOPMENT PURPOSES ONLY.': 

```
headers = {
    "Accept": "application/json",
    "Content-Type": "application/json"
}

payload = {
    "apiKey": "your API key here"
}
response = requests.request("POST", auth_url, json=payload, headers=headers)
```

Now we need to get the token from our response, so we can authenticate with the rest of api.video's endpoints when we want to do something. To get the token, we'll convert the response to JSON, then retrieve the token out of the JSON dictionary. 

```
response = response.json()
token = response["access_token"]
```

### Set up a Video Container 
Using the token, we'll get the headers and payload ready for our next request, which will be to ask api.video to create a video container for us.

```
## Set up headers for authentication
headers_bearer = {
    "Accept": "application/json",
    "Content-Type": "application/json",
    "Authorization": auth_string
}

## Create a video container
payload2 = {
    "title": "Demo Vid from my Computer",
    "description": "Video upload of Big Buck Bunny to demo how to do an upload from a folder on your computer."
}
```

api.video endpoints use bearer authentication. Add the authorization header for bearer authentication to your list of headers, and then for the payload we keep it simple, just a title and description. But what about the path to your file? Don't worry we're just creating a container right now. When you upload from your computer, you create a container first, and do your upload second. 

### Send Your Request and Grab the VideoId
For the next part of our walkthrough, we'll send our request, convert the response to JSON, and retrieve the videoId. We need the videoId so we can tell api.video what container we want to upload our video to. 

```
response = requests.request("POST", create_url, json=payload2, headers=headers_bearer)
response = response.json()
videoId = response["videoId"]
```

### Create the Endpoint for Uploading Your Video
Set up the endpoint you'll upload your video to, which is composed of `https://ws.api.video/videos` + `/` + `videoId` + `/source`. 

### Build a Data Chunk Reader
To break your data into chunks, you're going to want to decide how big each chunk should be, and then from an open file keep grabbing the next chunk of data to read. We'll set the chunk size at 1000000 bytes. This is fairly small, in case you wanted to try the sample with a smaller video file to see how it works. If you plan to start using this data chunk reader, make the chunk size larger. A good size is about 50-80 MB or about 50000000-80000000 bytes. 

This function assumes you've already opened your file elsewhere, and it's using **yield**. What this does, is return a generator. Every time you use this function as part of a for loop, it will run until it hits yield, then return the value. Yield 'remembers' where you left off, so each time you loop through, it will return a new chunk of data. 

```
CHUNK_SIZE = 1000000

def read_in_chunks(file_object, CHUNK_SIZE):
    while True:
        data = file_object.read(CHUNK_SIZE)
        if not data:
            break
        yield data
```

### Start Your Large Video Upload 
<center><iframe src="https://giphy.com/embed/TI3IErToEDAERYK7LL" width="480" height="480" frameBorder="0" class="giphy-embed" allowFullScreen></iframe><p><a href="https://giphy.com/gifs/TalentSmiths-loading-talentsmiths-janetsmiths-TI3IErToEDAERYK7LL"></a></p><br></center>
Here's our next function. This one is the upload function, and it contains our chunk reader. What we do here, is use the os library to grab some file details, so we know how big the file we're going to send is, the path to get to it, and the name of the file. 

We open our file, and NOTE...we are opening our file in binary. This is important to remember, since opening a video file in other formats will probably not work. 

Next we set up our index, that's going to represent what point we're at in the file. We also set up the offset, which represents how far forward to move the index after we send each chunk. 

Then we start reading chunks of data. For each chunk of data, we prepare the Content-Range header so we are correctly listing what part of the file we are sending in bytes. To send our data chunk, we need to put it into a one item file dictionary. We do that, and then for readability assign it to a variable. Then we send our request! This will loop through until we're out of data chunks (the last chunk will probably be smaller than the others). 

```
def upload(file, url):
    content_name = str(file)
    content_path = os.path.abspath(file)
    content_size = os.stat(content_path).st_size

    print(content_name, content_path, content_size)

    f = open(content_path, "rb")

    index = 0
    offset = 0
    headers = {}

    for chunk in read_in_chunks(f, CHUNK_SIZE):
        offset = index + len(chunk)
        headers['Content-Range'] = 'bytes %s-%s/%s' % (index, offset -1, content_size)
        headers['Authorization'] = auth_string
        index = offset
        try:

            file = {"file": chunk}
            r = requests.post(url, files=file, headers=headers)
            print(r.json())
            print("r: %s, Content-Range: %s" % (r, headers['Content-Range']))
        except Exception as e:
            print(e)
```

You don't need any other headers. 

You will know you are successful after you send the last chunk. It's possible to receive a -200- response for each chunk, then have it not work out at the very end, so make sure to watch your video upload the first time through. Success is denoted by the response to the last chunk, where you will receive something that looks like this: 

```
{
 'videoId': 'viyourvideoIdz', 
 'title': 'Demo Vid from my Computer', 
 'description': 'Video upload test.', 
 'public': True, 
 'panoramic': False, 
 'mp4Support': True, 
 'publishedAt': '2021-02-08T06:14:53+00:00', 
 'createdAt': '2021-02-08T06:14:53+00:00', 
 'updatedAt': '2021-02-08T06:14:53+00:00', 
 'tags': [], 
 'metadata': [], 
 'source': {
	 'type': 'upload', 
	 'uri': '/videos/vviyourvideoIdz/source'
	 }, 
 'assets': {
	 'iframe': '<iframe src="https://embed.api.video/vod/viyourvideoIdz" width="100%" height="100%" frameborder="0" scrolling="no" allowfullscreen="true"></iframe>', 
	 'player': 'https://embed.api.video/vod/viyourvideoIdz', 
	 'hls': 'https://cdn.api.video/vod/viyourvideoIdz/hls/manifest.m3u8',    
	 'thumbnail': 'https://cdn.api.video/vod/viyourvideoIdz/thumbnail.jpg'
	 }
}
r: <Response [201]>, Content-Range: bytes 4000000-4824145/4824146
```

Check your video's status in [your dashboard](https://dashboard.api.video/). If you have a very large video file, wait a few minutes while api.video finishes processing it. When processing is finished, you'll be able to see a thumbnail for your video and play it from the dashboard. 

If your video upload is unsuccessful, you won't receive the detailed message listed above. You might receive an additional 200 response, or a 400 response for each chunk, depending on how much went wrong with your upload. 
In the dashboard, you may see your video uploaded, but it stays stuck on processing. If this happens, review your code, or contact us with your issue in the [community forum](https://community.api.video/). 



Upload a big video file using Python

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If you have some videos on your computer that you need to upload to api.video, you're in luck - today's tutorial will show you how to do just that using Python. Your video must be under 200 MiB to use this method. The code sample is available on github in the [api.video python-examples repository](https://github.com/apivideo/python-examples). Let's get started! 

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## Prerequisites
* An [api.video account](https://dashboard.api.video/register) 
* api.video API key 
* You'll need access to Python and you'll need to install the requests library if you don't already have it added

## What You'll Be Doing
This is a quick summary of what you're going to do to upload your video. 

1. You'll authenticate with api.video using your API key and retrieve a token that lasts one hour that you can use to access all of api.video's other endpoints.
2. You'll create a video container for your video and retrieve the videoId. 
3. You'll use the videoId to upload your video from your computer to api.video. You can only upload a video to the videoId once. If you decide you wanted to upload a different video, you'll have to go through the create container and upload video steps again. You don't need to repeat the part where you get a new token unless your token expires (so if you take an hour long break and come back, you'll need to redo that step then). 

## Code Sample 

```
## How to upload a video under 200 MiB using the api.video API

import requests

## Set up variables for endpoints
auth_url = "https://ws.api.video/auth/api-key"
create_url = "https://ws.api.video/videos"

headers = {
    "Accept": "application/json",
    "Content-Type": "application/json"
}

payload = {
    "apiKey": "your API key here"
}

response = requests.request("POST", auth_url, json=payload, headers=headers)
response = response.json()
token = response.get("access_token")

auth_string = "Bearer " + token


## Set up headers for authentication
headers_bearer = {
    "Accept": "application/json",
    "Content-Type": "application/json",
    "Authorization": auth_string
}

## Create a video container
payload2 = {
    "title": "Demo Vid from my Computer",
    "description": "Video upload of Big Buck Bunny to demo how to do an upload from a folder on your computer."
}

response = requests.request("POST", create_url, json=payload2, headers=headers_bearer)
response = response.json()
videoId = response["videoId"]

## Create endpoint to upload video to
upload_url = upload_url + "/" + videoId + "/source"

## Create upload video headers 
headers_upload = {
    "Accept": "application/vnd.api.video+json",
    "Authorization": auth_string
}

file = {"file": open("local/path/to/myvideo.flv", "rb")}
response = requests.request("POST", upload_url, files=file, headers=headers_upload)
json_response = response.json()
print(json_response)
```

## Code Walkthrough

You'll start by adding the requests library, so you can easily make HTTP requests.

<center><iframe src="https://giphy.com/embed/YGlRW1Am9q7e0" width="320" height="480" frameBorder="0" class="giphy-embed" allowFullScreen></iframe><p><a href="https://giphy.com/gifs/books-libraries-shelves-YGlRW1Am9q7e0">How people added libraries before computers did it for you.</a></p><br></center>

```
import requests
```

Next, let's assign a variable to each endpoint we'll use, they'll be a little easier to work with. We're going to assign two of them here, and we'll use the second one later to build the third one: 
* [Authentication](https://docs.api.video/reference/basic-authentication) endpoint - This is where we send our API key to exchange it for a token. It's **auth_url** in the code sample.
* [Videos](https://docs.api.video/reference/api/Videos#create-a-video-object) endpoint - Specifically we'll use the feature on this endpoint that's for creating a video. It's **create_url** in the code sample.
* [Video Upload](https://docs.api.video/reference/api/Videos#upload-a-video) endpoint - It looks a lot like the other endpoint. It's create_url + "/videoId" + "/source". We'll construct this after we get a videoId to work with. 

```
auth_url = "https://ws.api.video/auth/api-key"
create_url = "https://ws.api.video/videos"
```

### Authentication - Get Your Token
We'll prepare our headers and payload for authentication, then send a POST request to api.video. With the api.video API you start authentication by sending a request using your API key to retrieve a token. You can use your sandbox or your production key, the backend will figure out which you are using and handle it accordingly. If you choose to use your sandbox credentials for this walkthrough, your video will be uploaded with the watermark 'FOR DEVELOPMENT PURPOSES ONLY.': 

```
headers = {
    "Accept": "application/json",
    "Content-Type": "application/json"
}

payload = {
    "apiKey": "your API key here"
}
response = requests.request("POST", auth_url, json=payload, headers=headers)
```

Now we need to get the token from our response, so we can authenticate with the rest of api.video's endpoints when we want to do something. To get the token, we'll convert the response to JSON, then retrieve the token out of the JSON dictionary. 

```
response = response.json()
token = response.get("access_token")
```

### Set up a Video Container 
Using the token, we'll get the headers and payload ready for our next request, which will be to ask api.video to create a video container for us.

```
## Set up headers for authentication
headers_bearer = {
    "Accept": "application/json",
    "Content-Type": "application/json",
    "Authorization": auth_string
}

## Create a video container
payload2 = {
    "title": "Demo Vid from my Computer",
    "description": "Video upload of Big Buck Bunny to demo how to do an upload from a folder on your computer."
}
```

api.video endpoints use bearer authentication. Add the authorization header for bearer authentication to your list of headers, and then for the payload we keep it simple, just a title and description. But what about the path to your file? Don't worry we're just creating a container right now. When you upload from your computer, you create a container first, and do your upload second. 

### Send Your Request and Grab the VideoId
For the next part of our walkthrough, we'll send our request, convert the response to JSON, and retrieve the videoId. We need the videoId so we can tell api.video what container we want to upload our video to. 

```
response = requests.request("POST", create_url, json=payload2, headers=headers_bearer)
response = response.json()
videoId = response["videoId"]
```

### Create the Endpoint for Uploading Your Video
Set up the endpoint you'll upload your video to, which is composed of `https://ws.api.video/videos` + `/` + `videoId` + `/source`. You also need to add the headers. For Python, you don't want to use the "Content-Type": `multipart/form-data` header you see listed if you try the API explorer. Because you are doing an upload with only one part, it won't work. Omit this in your headers. Set things up like this: 

```
headers_upload = {
    "Accept": "application/vnd.api.video+json",
    "Authorization": auth_string
}
```

For the payload, you're sending a video file, so you'll have to open the file, attach it to a dictionary, and for readability, we assign the dictionary to a variable. We use "rb" because we want to open the file as read-only binary format. Check it out: 

```
file = {"file": open("sample-flv-file.flv", "rb")}
```

We can assign our variable in our request to upload our video, and then process and print the request response:

```
response = requests.request("POST", upload_url, files=file, headers=headers_upload)
json_response = response.json()
print(json_response)
```

The response provides a lot of detail about your video:

```
{
	'videoId': 'video ID here', 
	'title': 'Demo Vid from my Computer', 
	'description': 'Video Upload From My Computer', 
	'public': True, 
	'panoramic': False, 
	'mp4Support': True, 
	'publishedAt': '2021-02-05T04:25:02+00:00', 
	'createdAt': '2021-02-05T04:25:02+00:00', 
	'updatedAt': '2021-02-05T04:25:02+00:00', 
	'tags': [], 
	'metadata': [], 
	'source': {
		'type': 'upload', 
		'uri': '/videos/video_ID_here/source'
	 }, 
	'assets': {
		'iframe': '<iframe src="https://embed.api.video/vod/video_ID_here" width="100%" height="100%" frameborder="0" scrolling="no" allowfullscreen="true"></iframe>', 
		'player': 'https://embed.api.video/vod/video_ID_here', 
		'hls': 'https://cdn.api.video/vod/video_ID_here/hls/manifest.m3u8',
		'thumbnail': 'https://cdn.api.video/vod/video_ID_here/thumbnail.jpg'
 }
}
```

* **videoId** - the unique identifier for your new video. Before it was just referencing a container, now it's referencing a container that holds your video!
* **title** - the title you provided for your video when you made the container
* **description** - a description of what the video contains
* **public** - by default, your video is public 
* **panoramic** - by default, your video isn't listed as being a 360° (panoramic is the term api.video uses for 360° or immersive video)
* **mp4Support** - whether you have an mp4 version of your video available or not. By default you do. If you turn this off, your video will not be downloadable from a player and there won't be a link to an mp4 version of your file.
* **publishedAt** - the time your video was published, in ISO-8601 format.
* **createdAt** - the time your video was created, in ISO-8601 format.
* **updatedAt** - the time your video was updated in ISO-8601 format.
* **tags** - you can add individual key words to categorize your videos and make them easier to search if you have a lot of them.
* **metadata** - you can add a dictionary of key value pairs that categorize your videos for searches.
* **source** - this lists the video type, which is upload, and the link you add to the api endpoint to get to your video. 
* **assets** - this is a list of links you can use to set up different services with your video. You can embed an iframe using the iframe code, use the player link to add the file to a player, get the manifest file if you need the source for live streaming, and api.video selects a thumbnail for you automatically. 

You can update all of this information should you choose to. That's it for uploading a video from your computer using Python! 

If you have any questions or suggestions, please share them on our [community forum](https://community.api.video/) and if you still haven't created your api.video account, you can do that [here](https://dashboard.api.video/register). Happy building! 

Upload a video with api.video’s API (Python)

Photo by <a href="https://www.pexels.com/photo/silver-imac-displaying-collage-photos-1779487/">Designecologist</a> from Pexels

Upload a video from your computer with the api.video API (Python)

Private Videos

NodeJS

Where I live in the UK, there are miles and miles of footpaths - publically accessible trails that cross private property.  There's an expectation that those that use the public footpaths will respect the land/plants/crops of the private owners, in exchange for access to the beautiful countryside.

## Public access to api.video

api.video has a concept of [delegated tokens](https://api.video/blog/tutorials/delegated-uploads) which works like a public key - granting anyone with the key access to upload videos into your account. Now, much like the public footpaths, the usage of the public key is expected to be reasonable, and not overused.

But...it is the internet. While most of the users will behave and use the delegated upload appropriately, there might be a bad egg or two that cause issues. To help mitigate public token upload abuse, we have added [revocation](https://docs.api.video/reference/api/Upload-Tokens#delete-an-upload-token) of delegated tokens. We also have added a 'time to live' where you can provide an expiration time (in seconds) to your token.

So we've wrapped some security around the major vector to delegated token abuse. Even with this change, we have not alleviated all the questions from our customers who are grappling with ideas of privacy with a public token.  In this post, we'll show how to address the most common concerns we've heard from our users. 






## Public, but Private

*"Can I have a 'one use' delegated token?*

This is a fabulous idea - it completely removes the issue of delegated token abuse - the token "self destructs" upon video upload.



thumbnail_single-use.jpeg

single-use.jpeg

*"Can I make a delegated upload private by default?"*


A [private video](https://api.video/blog/tutorials/tutorial-private-videos) has a unique token in the url that allows **one view** of the video.  Each time the video is served, a new URL must be requested from api.video. This prevents the general public from seeing the video, and allows you to decide on the access.  By default, all videos are public.

Sometimes, as we walk the footpaths, we come across a path that is marked as private - the landowner does not want the public in that area.  

Setting a public, delegated token to private videos is like the "No Trespassing sign" on the footpath.

medium_notrespassingbydjuradjvujcic.jpg

small_notrespassingbydjuradjvujcic.jpg

thumbnail_notrespassingbydjuradjvujcic.jpg

notrespassingbydjuradjvujcic.jpg



*"Can I disable mp4 download link by default?"*

By default, the ```mp4Support``` parameter is set to ```true```, allowing the download of a private video.  By setting this to false, you can prevent the download from occurring.  Another "keep out" sign on the public path, if you will.


medium_forbidden.jpg

small_forbidden.jpg

thumbnail_forbidden.jpg

forbidden.jpg

 
### Private access, but with the public token??

Can we make the video private and disable the mp4 *while* using the public delegated token?  You can!  

Can we create a single-use delegated token - one per video upload?  Yes, we can (to a degree).  But I think this approach is close enought to a unique upload token to be secure and protect your account from bad actors on the internet.


## Demo Time

We've created a demo showing a public/private upload.  You can try it out yourself at [privatelyupload.a.video](https://privatelyupload.a.video/).


When you visit this page, you see a form for video upload.  I am writing this post in mid-December, so will use a trailer from a classic Christmas movie in my upload:

online form for video upload. We'll be uploading the Die Hard trailer.

large_screenshot-2020-12-17-at-15-46-49.png

medium_screenshot-2020-12-17-at-15-46-49.png

small_screenshot-2020-12-17-at-15-46-49.png

thumbnail_screenshot-2020-12-17-at-15-46-49.png

screenshot-2020-12-17-at-15-46-49.png

When I begin the upload (by selecting the video), I create an XMLHTTPRequest to the server with the video name, description, and if the video should be public/private, support/not support mp4:

### Set video privacy

This request is sent to the "createVideo" endpoint on my server.  The Node server extracts the JSON from the body, and creates a video container at api.video.  When we create the container, we can set the privacy settings for the video to be uploaded - namely if it should support mp4 download, and if the video should be public or private.

>This solves 2 of the questions asked above - 'can a delegated upload be set to private?' and 'Can I disable mp4 support?'.

```javascript
	if(req.body.public === "false"){
		public=false;
	}
	var mp4  = true;
	if(req.body.mp4Support === "false"){
		mp4=false;
	}
	var title = req.body.title;
	var descr = req.body.description;

	client = new apiVideo.Client({ apiKey: apiVideoKey});	
	
	
	let result = client.videos.create(title, {	"title": title, "mp4Support": mp4,
		"public": public, 
		"description": descr,					
	});

```

The response from the create video has a videoId that we will use as the container for the video we are about to upload.  This container is already built with the privacy settings requested on the webpage!

### One use upload token

Next, we'll create a delegated token with a TTL of 90 seconds.  Note - the TTL support is not yet in the Node SDK (it launched last week!), so we make HTTP requests to the API endpoint.

1. [Authenticate our apiKey](https://api.video/blog/tutorials/authentication-tutorial). This will return an authentication key in a second request for the token.  2. [Create a delegated token](https://docs.api.video/reference/api/Upload-Tokens) with the desired TTL (set to 90s in the code).  The response will give us the delegated token, and when it expires ("expiresAt").

```
var authOptions = {
			method: 'POST',
			url: 'https://ws.api.video/auth/api-key',
			headers: {
				accept: 'application/json'
				
			},
			json: {"apiKey":apiVideoKey}

		}
		console.log(authOptions);	
		request(authOptions, function (error, response, body) {
			if (error) throw new Error(error);
			//this will give me the api key
			
			var authToken = body.access_token;
			console.log(authToken);
			//now use this to generate a delegated toke with a ttl of 90s
			var tokenTTL = 90;
			var tokenOptions = {
				method: 'POST',
				url: 'https://ws.api.video/upload-tokens',
				headers: {
					accept: 'application/json',
					authorization: 'Bearer ' +authToken
				},
				json: {"ttl":tokenTTL}
	
			}
			request(tokenOptions, function (error, response, body) {
				if (error) throw new Error(error);
				var delegatedToken = body.token;
				var tokenExpiry = body.expiresAt;
				console.log("new token", delegatedToken);
				console.log("new token expires", tokenExpiry);
				var tokenVideoIdJson = {"token": delegatedToken,
										"expires":tokenExpiry,
										"videoId": videoId};
				res.setHeader('Content-Type', 'application/json');
				res.end(JSON.stringify(tokenVideoIdJson));

			});
```

Once these two requests have been made, we then send back a JSON response to the browser with the ```videoId```, and the delegated token information.

### Token Expiration

The token expires in 90 seconds - how can we ensure that the video will upload completely in 90s? The answer is that we cannot.  However, we'll use a trick.  If we break the video to be uploaded into small segments, we just need to ensure that the first *segment* of the video is uploaded in 90s. The chunkSize is set to 6 MB - so the first 6 MB must be uploaded in 90s.  

> We're balancing your customers' upload speed with the expiry of the upload token.  If 6 MB in 90s is causing failures - you can lengthen the token expiry - the minimum upload size is 5 MB.  
> If you think this is too generous - lower the tokenTTL. 


### Video Upload

>NOTE: Since this post was written, we've published a library to simplify JavaScript upload of videos [read the blog post to learn more](https://api.video/blog/tutorials/new-video-uploader-javascript-library).


Now we can come back to the JavaScript on the page, and the ```qReq.onload``` that I omitted above:



First we grab the parameters sent from the Node server. For the purposes of the demo, we display the delegated tokenId and the time the token expires (in GMT) on the page.

We then create the delegated video upload url by appending the token to the base url. We determine the number of chunks to upload, and the initial byterange (0-chunkSize) and send the videoId and the videoFile to the createChunk function.

The createChunk function slices up the video from 0 to chunkEnd-1 (the file Slice API lists the first byte IN the slice, and the first byte NOT in the slice). *It's easy to get an off by one error here!*

```
			function createChunk(videoId, start, end){
				chunkCounter++;
				console.log("created chunk: ", chunkCounter);
				chunkEnd = Math.min(start + chunkSize , file.size );
				const chunk = file.slice(start, chunkEnd);
				console.log("i created a chunk of video" + start + "-" + chunkEnd + "minus 1	");
				const chunkForm = new FormData();
				console.log("createChunk videoId",videoId +start+end);
				if(videoId.length >0){
					//we have a videoId
					chunkForm.append('videoId', videoId);
					console.log("added videoId");	
					
				}
				chunkForm.append('file', chunk, filename);
				console.log("added file");

				
				//created the chunk, now upload iit
				uploadChunk(chunkForm, start, chunkEnd);
			}
```

Then the api calls the uploadChunk function that sets the byterange header (off by one warning!) and uploads the video.  It also updates the upload progress.  When the segment is completed, the counter is incremented, and calls the createChunk function again:

```
function uploadChunk(chunkForm, start, chunkEnd){
				var oReq = new XMLHttpRequest();
				oReq.upload.addEventListener("progress", updateProgress);	
				oReq.open("POST", url, true);
				var blobEnd = chunkEnd-1;
				var contentRange = "bytes "+ start+"-"+ blobEnd+"/"+file.size;
				oReq.setRequestHeader("Content-Range",contentRange);
				console.log("Content-Range", contentRange);
				function updateProgress (oEvent) {
					if (oEvent.lengthComputable) {  
					var percentComplete = Math.round(oEvent.loaded / oEvent.total * 100);
					
					var totalPercentComplete = Math.round((chunkCounter -1)/numberofChunks*100 +percentComplete/numberofChunks);
					document.getElementById("chunk-information").innerHTML = "Chunk # " + chunkCounter + " is " + percentComplete + "% uploaded. Total uploaded: " + totalPercentComplete +"%";
				//	console.log (percentComplete);
					// ...
				} else {
					console.log ("not computable");
					// Unable to compute progress information since the total size is unknown
				}
				}
				oReq.onload = function (oEvent) {
							// Uploaded.
								console.log("uploaded chunk" );
								console.log("oReq.response", oReq.response);
								var resp = JSON.parse(oReq.response)
								videoId = resp.videoId;
								//playerUrl = resp.assets.player;
								console.log("videoId",videoId);
								
								//now we have the video ID - loop through and add the remaining chunks
								//we start one chunk in, as we have uploaded the first one.
								//next chunk starts at + chunkSize from start
								start += chunkSize;	
								//if start is smaller than file size - we have more to still upload
								if(start<file.size){
									//create the new chunk
									createChunk(videoId, start);
								}
								else{
									//the video is fully uploaded. there will now be a url in the response
									playerUrl = resp.assets.player;
									console.log("all uploaded! Watch here: ",playerUrl ) ;
									document.getElementById("video-information").innerHTML = "all uploaded! Watch the video <a href=\'" + playerUrl +"\' target=\'_blank\'>here</a>" ;

								}
								
				};
				oReq.send(chunkForm);
		
				
				
			}
```


These two functions dance back and forth - creating slices and uploading them - until the entire video is uploaded.  When the video is fully uploaded, api.video begins assembling the video, and encoding the file.  The response conatains the player URL for the video - which we put on the page for the viewer to see. If the video is set to private - it will be a private URL with a token - only good for one view.

## Private and secure video upload

I think we've done it!  A single use public token for video upload, and the ability to protect the video **before** it is uploaded.

Now you can be sure that your users will properly "stay on the path" with their video uploads.

large_img20200822121759.jpg

medium_img20200822121759.jpg

small_img20200822121759.jpg

thumbnail_img20200822121759.jpg

img20200822121759.jpg

 The code is availabe on [Github](https://github.com/dougsillars/privatedelegatedtoken) if you'd like to take a deeper dive, or create your own version.  And don't forget to try the demo [privatelyupload.a.video](https://privatelyupload.a.video/).

Was this tutorial/demo app useful to you?  Do you have other examples of how to use api.video that you'd live to see? Let us know in our [community](https://community.api.video) forum. 

Private video upload with a public token

medium_footpath.jpg

small_footpath.jpg

thumbnail_footpath.jpg

footpath.jpg

curl

api.video can help you simplify your video hosting and [delivery](https://api.video/product/live-stream/). As such, one of the first things you'll want to do is to upload videos and get them loaded into the service. Once you upload the video, we'll take care of transcoding the video into streams for delivery to your users.

The easiest way to upload videos into api.video is using our API, and we'll walk through the steps to use the API and get your video online in this tutorial.

## Using cURL

This tutorial uses curl and HTTP requests to authenticate your session, create a video, and then upload the video. 

This example works for smaller videos, but as your video gets larger, you will want to break the video into smaller bites (bytes?) to facilitate upload.  Here's how do to that using the [command line](https://api.video/blog/tutorials/video-upload-tutorial-large-videos/). 

### Other languages?

We have many tutorials on video upload.  Here are a few links to help you:

* [JavaScript](https://api.video/blog/tutorials/uploading-large-files-with-javascript/).
* [Python](https://api.video/blog/tutorials/upload-a-video-from-your-computer-with-the-api-video-api-python/)
* [PHP](https://api.video/blog/tutorials/helpful-php-scripts-pt-2-upload-videos-from-an-ftp-server/)

We have several [Clients](https://docs.api.video/) for additional platforms that will help you get started uploading videos quickly.

You can also check out the API reference documentation for uploading a video here: 
* [Create a video](https://docs.api.video/reference/api/Videos#create-a-video-object)
* [Upload a video](https://docs.api.video/reference/api/Videos#upload-a-video)

### Step 1
You need an access_token in order to create and upload a video.

You will need your API key to obtain the access_token. You can find your API key on [your dashboard](https://dashboard.api.video/overview).

This tutorial uses the sandbox endpoint. To upload your video in production, simply replace all ```sandbox.api.video``` instances with ```ws.api.video```.

First, call the authentication endpoint and get your access token.

**Example - Curl Request**

### Step 2
We will use the create video API to create and upload the video.

The ```Authorization: Bearer``` header uses the access_token from the first step. Hint: Make sure to not remove the end ' when pasting in your token.
The ```title``` parameter is required, so you must add a name to your video.
If your video is hosted on a server, you can use ```"source":"url"``` in the JSON to add the video.
If your video is on your local machine, we'll need to do one additional step.

This example also includes the video description.
The response contains links to the video location, and generates a unique identifier ```videoId```.

**Curl Request**

This is a title

### Step 3: Upload the video file.

> In this step, we use the upload endpoint of the API.  You can also use [delegated tokens](https://docs.api.video/reference/api/Videos) for the upload. The advantage of delegated tokens is that these are safe to use on a webpage (whereas your authorisation token is not), and the videos are directly uploaded to api.video.  You can learn more about using [delegated tokens](https://api.video/blog/product-updates/video-access-management-how-to-create-deliver-and-manage-private-videos-and-what/) for this same experience.

In Step 2, we saw how to link to a video already hosted. If the video is not hosted, and must be uploaded, we will call the video upload API:

When the upload is complete, a JSON response similar to that of step 2 is returned.

That's it - if you access the assets links in the response JSON - you can begin sharing the video you just created. Note: The mp4 URL is only added to the API response once the mp4 video has been encoded, which may take a few additional seconds.

## Conclusion

In this post, we learned about authentication of our account with our [api.video](https://api.video) token, creating a video container, and uploading a video into that container. Once your video is uploaded, it is ready to be shared with your audience!

If you have any questions or suggestions, please share them on our [commmunity forum](https://community.api.video/). To start building now, singup for an api.video account [here](https://dashboard.api.video/register). Thanks for reading! 

Video upload with cURL

Some videos are large, and when creating and uploading a large video to api.video, there can be transport issues, causing the upload to fail.

When uploading large videos, they must be broken into 100MB chunks to ensure delivery.  While these also may fail, the amount of time lost is minimised, and the segement can be retried.

api.video will reassemble the video segments on the server, and create streams for your users to consume.  
 
NOTE: This tutorial walks through the process of partial video uploads using curl.  The api.video [API Clients](https://docs.api.video/) handle large video uploads automatically, allowing you to skip this process. While this is a great walkthrough of what the clients and SDKs do behind the scenes, we strongly reccomend that you use them, as getting the Content-Range headers correct (in step 4) is tricky.


This tutorial uses curl and HTTP requests to authenticate your session, create a video, and then upload the video.

Another great way to split up large files is to use JavaScript and Blob.slice. We have a [second tutorial](https://api.video/blog/tutorials/uploading-large-files-with-javascript/) for that technique.

Due to limits in how HTTP handles large file uploads, there is an upload limit of 199 MiB per video segment.

However, you can still upload videos over 199 MiB, it just needs to be split into separate chunks (under 200 MiB), and each chunk uploaded separately.

This process works for videos that are less than 200 MiB as well, but has one additional step.


## Step 1

You need an access_token in order to create and upload a video.

You will need your API key to obtain the access_token.  You can find your API key on [your dashboard](https://dashboard.api.video/overview). 

This tutorial uses the sandbox endpoint. To upload your video in production, simply replace all `sandbox.api.video` instances with `ws.api.video`.

First, call the authentication endpoint and get your access token.


### Example - Curl Request

### Step 2 

1. We will use the [create](https://docs.api.video/reference/api/Videos#create-a-video-object) API to create a video container.
2. There are many parameters you can use when you create (and upload) a video, but the only required parameter is `title` (the name of your video).
3. The response contains links to the video location, and generates a unique identifier named `videoId`.

#### Curl Request

### Step 3 - Splitting the video
You have created a video container, but have not yet uploaded the video sources.  As mentioned, a video over 199 MiB must be split into smaller chunks.

#### Mac
split is a command line tool built into MacOS.  

1. The first argument is the chunk size (here 100MB)
2. The second argument is path to the source file
3. The third argument is a pattern name for the chunked files
 
This command creates 3 files:

#### Linux
You can use the split utility to split the video into smaller chunks (max 199 MiB).

1. The first argument is the chunk size (here 100MB)
2. The second argument is path to the source file
3. The third argument is a pattern name for the chunked files

This command generated 3 chunk files:

The last chunk is generally smaller than the other ones.

#### Windows
There are a number of free tools that can split a file into chunks.

### Step 4: Uploading video chunks

We will use the [source](https://docs.api.video/reference/api/Videos#post_videos-videoid-source) API to add the source files.  Note that the url also contains the `videoId` parameter obtained in the json response from step 2.

To upload by range, you must add the following header to the request:

`Content-Range: bytes <from>-<to>/<total_size>`

- `from` is the **0-indexed** start position  in the file
- `to` is the **0-indexed** end position in the file
- `total_size` is the size of the source file. It must be identical across requests.


#### CURL requests


**Request/response 1:**

And there you go. Your large video should be uploaded and ready to go. If you have any questions or suggestions, please share them on our [community forum](https://community.api.video). You can [start building](https://dashboard.api.video/register) now!

Video upload (large videos) with cURL

Announcements

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Cédric Montet

Company News

AI, models, assistants, agents and prompts sound and look magical on social networks. But from the countless conversations we have had with startups and Enterprise customers over the last months, we have realized that the gap between existing video workflow and concrete use cases is real.

&nbsp;

Whether it’s getting transcripts in seconds, changing the language of your video or being able to search for items in your content – the benefits, value or potential savings you see and can get with AI are obvious.

&nbsp;

This is why we are thrilled to unveil how we are bringing AI into video, where we are heading and how you can get more value from it!

## What does the future of video streaming look like?

AI will completely upend video consumption as we know it today. We envision a world where video is not a content piece anymore which you can only start and stop – because AI will make videos discoverable and universally understandable. More than ever it resonates fully with our core mission – connecting people through their cameras.

&nbsp;

Since Day 1, we are building api.video to make your use cases, first, a reality and, then, a success.

&nbsp;

Back in the days, the fragmented video streaming market, the number of services you had to aggregate, the lack of innovation by the incumbents was preventing you from joining our video-first world, while your efforts and budgets were consumed by maintaining mashups instead of focusing on what matters. 

&nbsp;

This is why we have built the video backbone and seeing that, now, you host 2.5 millions videos on api.video and deliver dozens of millions videos every month demonstrate why simplicity, exceptional performances and an E2E approach were key.


&nbsp;

What’s happening today with AI, the efforts it requires to benchmark, setup and customize LLMs, to run and maintain them, the Infrastructure you have to build to execute them before getting any value, remind us of the state of the streaming market when we had launched api.video.

&nbsp;

Helping you to benefit from the AI revolution by abstracting all the underlying complexity became obvious and a natural next step in our evolution.

&nbsp;

To get there and to bring you there, here is what we are building, api.video’s very first – **Video Model Hub**.

&nbsp;

Let’s start with the root principles encompassing our work on this Hub:

&nbsp;

- **Open-source models over private ones.** We are long time believers and contributors of the open source community and we have chosen to run only models that are available publicly.

- **Your data is… your data.** You already trust us to host and deliver sensitive content,and this is exactly why we won’t train our own models or sell the data generated from your videos to anyone.

- **Privacy at the core.** We host and execute the models inside our own infrastructure. Your data remains inside the api.video infrastructure. Your videos or data is not sent to external partner, SaaS providers like OpenAI, or others.

- **We believe that every organization will eventually build their own models to leverage their unique data sets**. Next year, alongside the ability to run public and open source models on api.video, we will offer you the ability to host your own.

- **One API to rule them all.** As we have already replaced a dozen of services and vendors under GET/videos to make your videos available worldwide in seconds, we are abstracting all the complex work of executing LLMs behind clear and straightforward endpoints.

&nbsp;

At a glance, api.video brings to you today the power of AI by:

- Leveraging its years of experience in video management and building infrastructure, with GPUs.
- Combining with the most advanced LLMs available on the market.

so that you get more value from your videos in a few lines of code!

&nbsp;

## Here are the first three new features from the Video Model Hub

### 1. Summarization of long-form videos

Upload a video in English, Spanish, French, Portuguese or Chinese and get a text summary of 120 characters with the 3 key takeaways discussed in the video (within seconds!). Ideal to index or preview long form recorded meetings or courses!

&nbsp;

_The video below gives you a glimpse into AI summarization._


### 2. Transcription of your video in seconds

Get a transcript (in .VTTformat) from your uploaded video with the timecodes and offer the captions to your audiences. The best part? The api.video player supports VTT files so there’s nothing new that you need to build on your side!
This also makes your content more inclusive by making it accessible to people with hearing impairments. 


### 3. Translation of subtitles in 5 different languages (and more to come!)

Is your video in English? You can get Spanish and French subtitles. In Chinese? You will get English subtitles and three more languages. In short, this is the perfect solution to spread your message to your international audience!

&nbsp;

_Check out the video below for a glimpse into AI translation._ 


_Try it for yourself now by uploading a video in your [sandbox account](https://dashboard.api.video/overview)._ 

## What’s next

From video classification, alteration, generation, audio alteration, metadata generation, we have identified and plan to implement more than 200 additional use cases and features within the upcoming months!

&nbsp;

From next year, we will let you bring your own models on the infrastructure to get even better and more personalized outcomes.

&nbsp;

This is a long-journey and we’re only getting started. The whole team is thrilled to be at the forefront of this next chapter!


AI  >< Video: The Video Model Hub unveiled

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Arushi Gupta

Product updates

Our mission at api.video has always been to turn your use cases into successful realities. We built the video backbone with this goal in mind, and today, you host 2.5 million videos and deliver tens of millions more!

&nbsp;

Helping you harness the power of AI, while simplifying the problems that come along with, was the next direction to take. 

&nbsp;

To achieve this and to support your journey, we bring to you –  **AI features for your videos.**



## What are the AI video features?

### 1. AI video transcription & captions

With AI transcription, you can convert spoken words in your videos into written text, providing an accurate, searchable record of your content. This means you can easily create subtitles, enhancing accessibility for the hearing impaired or getting a written record of your meetings or events. 






### 2. AI video translation

With AI translation, you can translate your videos into multiple languages, ensuring that viewers from different linguistic backgrounds can understand and engage with your content. This can be useful if you’re hosting an international virtual event, creating multilingual educational materials, or expanding your marketing reach.

&nbsp;

Translation will be available for 5 languages – French, English, Spanish, Portuguese and Chinese.


### 3. AI video summarization

Using AI summarization, you can distill lengthy content into concise, digestible summaries, allowing viewers to quickly grasp the key points without watching the entire video with AI video summarization. This can prove invaluable for educational lectures, business presentations, and lengthy seminars where time is of the essence.

## Do you really need this?

Well, the potential [uses of our AI features](https://api.video/use-cases/generative-ai/) are limitless! From e-learning and e-commerce to online streaming, these [AI features](https://api.video/product/ai-features/) are going to be the difference between ordinary and excellent. What can you do really?

&nbsp;

- Summarize educational lectures for a quick review
- Translate virtual events into multiple languages for global reach
- Transcribe video interviews for easy reference
- And so much more!

## More than just encoding, hosting, and delivery

We’ve gone beyond the basics of video hosting and delivery to give you features that are a step up from the ordinary. Now, not only can you ensure that your customers get video delivery at minimal cost, but also that they get the flexibility to offer content  in their preferred language and in a crisp manner. 

&nbsp;

Additionally, when the features are in production, you may also get the option of adding them via your API. This means that you get complete power to add the features you like without having to go through the dashboard.

## Your videos stay private to you

Your data is yours, and we respect that. You already trust us to host and deliver your most sensitive content, which is why we will never train our own models or sell data generated from your videos. Privacy is at the core of everything we do, and that’s why we host and execute the models within our own infrastructure. Your data stays entirely within the api.video ecosystem and is never sent to external partners, SaaS providers like OpenAI or others.

&nbsp;

_Our CEO and founder, Cedric Montet, speaks more about privacy being our topmost concern in his latest [blog](https://api.video/blog/company/video-model-hub-ai/)._

## Give it a try!

See a glimpse how these AI features look like with a free sandbox account! [Upload a video now in your dashboard](https://dashboard.api.video/overview) and witness AI make a difference to your videos. As this is a Beta release, any feedback, questions, doubts on the features are welcome and much appreciated.

Create a sandbox account now 

Lights, Camera, AI Action! Discover our AI features

Introducing AI features to compliment your videos

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AI >< Video: The Video Model Hub unveiled

How to integrate AI facial expression detection to control your video – Javascript 

5 AI tools that will save you 10+ hours on video production

Lights, Camera, AI Action! 

Video trends

In a world where time is money and creativity reigns supreme, AI tools that convert text to video are game-changers! These AI-powered wonders are designed to save your day—and countless hours. Join us as we unveil the top 5 AI video tools that can dramatically cut down your video creation and editing time. From transforming text into captivating visuals to adding that perfect touch of magic to your edits, these tools are set to become your new best friends in the digital playground!

## **1. CapCut**

At number 1, we have Capcut. 

&nbsp;

[CapCut](https://www.capcut.com/) surged in popularity due to its user-friendly interface and powerful editing features, making it a favorite among video creators. Its integration with TikTok and social media platforms is also reason for its widespread adoption.

&nbsp;

The best part about using CapCut is that most of these important features are available for free, which kicks out budget-constraints out of the window. CapCut uses AI to empower users to create stunning videos without needing any fancy editing experience. With mobile and desktop apps, as well as a web version, CapCut can easily be used on the go. 

&nbsp;

CapCut offers a range of AI-driven features that are very, very impressive. With 'Smart Cuts & Trims', CapCut's AI automatically detects shaky parts, long pauses, or unnecessary bits, suggesting cuts to tighten up your video and eliminate boring footage. 'Effortless Audio Magic' helps isolate voices and enhance audio quality, making your message crystal clear even if there’s noisy background interference. 'Instant Captions' auto-generate captions, making your videos more accessible and saving you time and effort. Moreover, if your filming didn't capture the perfect lighting, 'Light Like a Pro' allows CapCut’s AI to adjust lighting in your videos post-production, creating the ideal mood.

What I most love about CapCut AI video generator is its AI-driven background removal feature which eliminates the need for a green screen, allowing me to replace any background with an image or color of my choice.

&nbsp;

Something to note about Capcut is that the free version includes watermarks on exported videos. which can be removed only with a paid subscription. This may potentially pose a barrier for you if you’re a budget-conscious user.

## **2. Runway**

[Runway](https://runwayml.com/) can be called a virtual sorcerer's wand for video editing! It has the potential to be an entire AI video production studio, so it can basically create an entire movie without filming a single frame — if you have the time, that is. Runway can generate videos and animations from simple text prompts, create animations from still images, generate lip-syncing avatars based on a recording of your voice, and even allow you to train your own AI models. 

&nbsp;

Runway might be a little intimidating in the beginning. That’s because it has an array of features that take time to understand, execute and implement, and that could be slightly time-taking and if I can say, complex.

Another aspect to note is that while Runway offers a range of pricing plans, the advanced features that truly set it apart are locked behind higher-tier subscriptions, making it less accessible for small businesses or individual creators on a tight budget.

## **3. Invideo AI**

[Invideo AI](https://invideo.io/ai/) is an excellent tool for generating a starting point for your videos. It helps you to quickly create a basic structure for your videos, which can then be further customized and polished. The  features allow you to input simple prompts, from which the tool generates an initial draft that includes relevant footage, transitions, and effects. 

&nbsp;

A few standout features of Invideo AI are an extensive stock media library that offers a range of options to choose from, AI-powered script generation which creates a complete script for your videos and, voiceover addition. Their AI voiceover generator is particularly good because it has an option to add your own voice which it then uses to narrate scripts in. The narration is also decent, with multiple accents and focussing well on the feeling, intent, and emotion behind each word.

Overall, the tool is great for YouTubers and people who want to start off their video making journey with minimal effort. 

&nbsp;

One of the shortcomings of Invideo AI is its dependency on a stable internet connection, as the tool is cloud-based. This can be a significant limitation for users in areas with unreliable internet access. I also noticed that rendering videos on Invideo AI can sometimes be slow, especially for longer or high-resolution projects, so keep enough time in hand before you expect a final video. 

## **4. Wondershare Filmora**

[Wondershare Filmora](https://filmora.wondershare.net/de/ad/videobearbeitungsprogramm-brand.html) has been a staple in the video editing world for over 15 years. Despite its long history, it's continuously evolving, with a suite of AI features that enhance video quality without requiring professional training or extensive time investments. 

&nbsp;

Filmora is a popular choice among full-time influencers and video editors for advanced-level video editing. It is also a preference because the tool keeps evolving based on new technological developments, and therefore is easily able to meet the demanding standards of modern digital media.

&nbsp;

Some of Filmora’s features include 'AI smart cutout', which allows you to cut objects out of scenes simply by painting their area on the screen, and 'AI audio denoise', which helps control background noise. 'AI audio stretch' lets you extend music to match scene transitions, while AI motion tracking enables face blurring for privacy or fun effects. They also have AI remove background which allows you to create imaginative scenes without hefty budgets. 

Wondershare also provides detailed explanations of these features on their website, so if you ever face a problem using any of them, be sure to check their dedicated pages. 


&nbsp;

However, do note that Filmora's performance can be sluggish when handling large files or high-resolution videos, leading to slower rendering times and potential crashes at times.

## **5. Canva Magic Studio**

Easy to use. Neat interface. Quick results. 

&nbsp;

Those were the three things I liked best about [Canva Magic Studio](https://www.canva.com/magic/). 

&nbsp;

Canva Magic Studio, though currently in a very basic state, looks very promising as an AI video tool. Having been a fan of Canva for infographics, presentations and social posts since the past few years, I have full confidence that their AI video tool will evolve too. 

&nbsp;

As of now, Canva Magic Studio gives you amazing results when it comes to video quality and is also very quick to generate the final video. The good video quality comes from its integration with [Runway](https://zapier.com/blog/best-ai-video-generator/#runway) (our #2 in the list).

&nbsp;

To show you an example, I used the prompt: "A cute rabbit watching a video on a laptop”. And the [result](https://www.canva.com/design/DAGGCqwwf4A/GS7q42J5C9nx26gxJGR9lA/watch) was surprisingly very apt!

Canva Magic Studio is a great option if you want to bulk-create your social media posts – create a chunk of Instagram reels or Facebook ads in a go or create similar kinds of videos using a single template. The template options Canva offers have a wide range and are decent for beginners to start with.

## Sora – Open AI’s video generator

Open AI sparked a wave of excitement around AI with ChatGPT, demonstrating its incredible potential in natural language processing. Now, it's taking things a step further with [Sora, an innovative AI](https://openai.com/index/sora/) video creator.

Open AI recently shared a series of tweets showing the potential of Sora and I must say, I am amazed. Be it [several giant mammoths](https://x.com/OpenAI/status/1758192960116785459) rushing through a snowy meadow or a [fluffy monster](https://x.com/OpenAI/status/1758192964222988506) behind a melted candle, the kind of videos Sora will be able to create is astounding! It looks like it will be able to take detailed prompts into consideration, just like it did with ChatGPT, to create exactly what the user needs.

&nbsp;

As Sora continues to evolve and improve, it will undoubtedly shape the future of video production, showing way to new forms of storytelling and engagement in the years to come.

## AI is great – but it cannot replace you

While these AI video tools can ease the load for you and save some time, they cannot really take the place of your creativity, intuition, and emotional intelligence. In the end, it is your perspective, personal touch, and experiences that shape the videos you create. 

&nbsp;

_If you’re finding a place to store, host and deliver your [AI generated videos](https://api.video/use-cases/generative-ai/), api.video could be just what you’re looking for. With quick video upload options, flexible hosting capabilities, ability to add watermarks and keep videos private, you can choose api.video for all your video needs. If you would like to test out our features, [sign-up](https://dashboard.api.video/register) for a sandbox account now and you wound’t be disappointed._

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5 ways the human eye impacts how we do video compression

7 eye-opening stats that prove the impact of product videos in e-commerce

5 ways to utilize product videos to boost your e-commerce sales

Yohann Martzolff

React

In today's tech-driven world, enhancing user interaction and creating immersive experiences is crucial. One exciting way to achieve this is by integrating AI facial expression detection into your video content.  In this blog, we'll guide you through the steps to <u>integrate AI facial expression detection to control your videos using React</u>. 

&nbsp;

Let's start.

&nbsp;

_*To learn how to do the integration with Javascript, head to this [blog](https://api.video/blog/tutorials/AI-facial-expression-detection-javascript/).*_

## 1. Create a minimal React app

As the first step, we need to create a new React app. Run the following command for the same. 

Now add the MediaPipe and the api.video React player packages

## 2. Add a video player

You’re going to use the official [api.video’s React player](https://github.com/apivideo/api.video-react-player) in this application, but feel free to use your favorite one and adapt the callbacks to it.

&nbsp;

Replace the code inside `App.tsx` with the following.
The `playerRef` allows us to control the [api.video player](https://api.video/product/player/).

Run your application with the npm run start command and go to `http://localhost:3000/`, you should see a video that you can play, pause or mute. 

## 3. Record your facial expressions

To control the player with your facial expressions, you need to stream your face through your webcam and pass this streamed data to the trained machine-learning model.

&nbsp;

Add a `<video />` HTML tag to `App.tsx`. 
Its `webcamRef` allows the video element to be targeted.
Apply a `display: "none"` inline style to hide the element, and an `autoPlay` attribute so the webcam stream time is updated. 

&nbsp;

**Note:** You can remove the `display: "none"` style and customize your CSS, it doesn’t matter.

## 4. Load the machine-learning model

Before we can use the machine-learning model, we must wait for it to finish loading. These models can be large and take a moment to get everything needed to run.

&nbsp;

Put this logic inside a `useEffect` and store the `faceLandmarker` class as a React stateful value.

## 5. Start detecting the webcam stream

Once the machine-learning model has been loaded and the `faceLandmarker` class has been instantiated, you can start detecting the stream.

&nbsp;

Create a new `useEffect`

Run your application with the `npm run start` command, go to `http://localhost:3000/`, and open your console. You should see a lot of console logs appearing each millisecond.

&nbsp;

Each one of them logs an array of objects of type:

## 6. Use the facial expression detection results

You now have access to a list of facial expression detection results. Use them to do whatever you want and in this case, to control your video player.

Let’s assume that you want to play the video when you smile, and pause it when you do a kiss.

Check the facial expression detection results on each frame and play or pause your video depending on the score of the wanted result by replacing your console.log with the following.

Once again, run your application with the `npm run start` command, go to `http://localhost:3000/`, and try to smile at your webcam. It should trigger the player’s `play` callback and play the video. Then, try to do a kiss ( 😗 ) in front of your webcam, and the video should pause.

## 7. Display the results

It can be helpful to have a visual representation of the detection results. Display these results as a list and highlight the detected facial expressions by adding a `<ul />` HTML tag to your JSX and creating a new ref to target it.

Add the code to update the HTML according to the results.

Now, you should see a list of results displayed in your application, with a green highlight on the ones with more than 60% confidence.

## 8. Code samples

Check the full `App.tsx` code below.

We hope this blog helped you understand the coding behind integrating AI facial expression detection to your videos.

 &nbsp;

_For any questions or doubts you may have, chat with us on our [website](https://api.video/contact/). If you would like to try out api.video for your videos, [sign-up](https://dashboard.api.video/register) for a free sandbox account here._

How to build AI facial expression detection into your videos with React

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