DepressionAI | Alexa skill for people suffering with depression

by Jflick58 Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | DepressionAI Summary

DepressionAI is a Python library. DepressionAI has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

According to Psychology Today, 71% of people who make their bed in the morning report feeling happy. This was the inspiration behind DepressionAI. The aim behind this skill is to encourage people to perform daily activities that become very difficult when one is depressed. The skill detects positive and negative moods. If the user is having a bad day, it asks them a series of questions about what they have done that day (e.g. "Have you gotten out of bed?") and if they haven't, it encourages them to do so. This tool does not provide medical advice, and is for informational and educational purposes only, and is not a substitute for professional medical advice, treatment or diagnosis. Call your doctor to receive medical advice. If you think you may have a medical emergency, please dial your local emergency response phone number.

Support

Quality

Security

License

Reuse

Support

DepressionAI has a low active ecosystem.

It has 93 star(s) with 24 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 6 have been closed. On average issues are closed in 0 days. There are 46 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of DepressionAI is current.

Quality

DepressionAI has 0 bugs and 0 code smells.

Security

DepressionAI has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

DepressionAI code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

DepressionAI is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

DepressionAI releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

DepressionAI saves you 1119 person hours of effort in developing the same functionality from scratch.

It has 2530 lines of code, 135 functions and 10 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed DepressionAI and discovered the below as its top functions. This is intended to give you an instant insight into DepressionAI implemented functionality, and help decide if they suit your requirements.

Handle question
Return a list of possible problems
Evaluate answers
Start a new session
Generate a welcome message
Re - prompt the user
Try to find a therapist
Try to get the address from Amazonalexa
Ask the user
Generate a random reason
Handles question
Creates an example
Generate a question question
Ask suggested suggestion

Get all kandi verified functions for this library.

DepressionAI Key Features

No Key Features are available at this moment for DepressionAI.

DepressionAI Examples and Code Snippets

No Code Snippets are available at this moment for DepressionAI.

Community Discussions

No Community Discussions are available at this moment for DepressionAI.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install DepressionAI

You can download it from GitHub.
You can use DepressionAI like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.