License: MIT
Python 3.7|3.6|3.5|3.4
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PLAYEMO

Playemo was built from the ground-up with Flask, a python tool that makes it easy for developers to build APIs.

Use Cases

Is Python your language of choice? If so, we have a [fully-supported Python API client] that makes working with the playemo API an easy task!

There are many reasons to use the playemo API. The most common use case is to predict the emotion of a person from a single photograph.
However, this can also be used as a facial detection engine which returns a cropped out image of the face detected in a single photograph.!

Authorization

All API requests require the use of an API key

To authenticate an API request, you should provide your the api_key=[API_KEY] as a GET parameter to authorize yourself to the API. But note that this is likely to leave traces in things like your history, if accessing the API through a browser.

GET /?api_key=12345678901234567890123456789012
Parameter Type Description
api_key string Required. Your Playemo API key

Responses

Many API endpoints return the JSON representation of the resources created or edited. However, if an invalid request is submitted, or some other error occurs, Playemo returns a JSON response in the following format:

{
  "error" : string,
  "success" : bool,
  "result"    : string
}

The error attribute contains a message commonly used to indicate errors or, in the case of deleting a resource, success that the resource was properly deleted.

The success attribute describes if the transaction was successful or not.

The result attribute contains any other metadata associated with the response. This will be an escaped string containing JSON data.

Status Codes

Playemo returns the following status codes in its API:

Status Code Description
200 OK
201 CREATED
400 BAD REQUEST
404 NOT FOUND
500 INTERNAL SERVER ERROR

Links

Please don’t hesitate to file an issue if you see anything missing.

Screenshots

Home Page

Available Commands

In the project directory, you can run:
python--version" : "check python version",

Since tensorflow supports python 3.7,3.6,3.5 or 3.4, i would advice you have python 3.6 installed on your machine.

pip install -r requirements.txt" : "required libaries installed",

This will install the the neccesarry libaries needed to run the application on your machine.

python app.py" : "python-scripts start",

The app is built using Flask so this command Runs the app in Development mode. Open http://localhost:5000 to view it in the browser. The page will reload if you make edits.
You will also see any lint errors in the console.

Built With

  • Python
  • Flask
  • Mtcnn
  • TensorFlow
  • Keras
  • CSS
  • HTML

Future Updates

  • A playlist recommendation system based on Emotion predicted

Author

DERHNYEL

? Support

Contributions, issues, and feature requests are welcome!

Give a ⭐️ if you like this project!

GitHub

View Github