Selection in big companies requires an aspirant to be proficient in coding as well as fluent in his words. The latter sometimes becomes a major anchor for various students with the ability to achieve high otherwise. This is a problem that our current interface looks at resolving. We are building an interface that helps users with a situation by the use of an AI that asks questions on the basis of a code which the aspirant has written, asked from a diverse pack of frequently asked coding questions. Then the AI asks the aspirant questions related to his code and some staple questions. At the end of this experience, the user receives his interview profile showing him his flaws in answering questions, his fluency, and his ability to handle the situation verbally. This can be done on various levels and be stored for future scrutiny by the user. We can also provide a growth curve that helps the aspirant to judge his progress.
We developed a AI interview emotion recognition platform to analysis the emotions of job candidates.
The tool can be accessed from the WebApp repository, by installing the requirements and launching WebApp/app.py.
Our aim is to develop a model able to provide a live sentiment analysis with a visual user interface.Therefore, we have decided to separate two types of inputs :
- Video input from a live webcam or stored from an MP4 or WAV file, from which we split the audio and the images
Video Analysis - Read More . . .
Audio Analysis - Read More . . .
To use the WebApp ( Server Side ):
- Clone the project locally
- Go in the WebApp folder
- Run
pip install -r requirements.txt - Launch
python app.py - Alternatively, skip all the steps by using
Install PyAudio Window , Mac
-
- CMD
pip install pipwin
- CMD
-
- CMd
pipwin install pyaudio
- CMd
-
brew install portaudio
To use the Application ( Client Side ):
- Go in the Application folder.
- Run
npm install -g && npm start - Launch
- Alternatively, skip all the steps by using
The web app has been Dockerized ( Application && WebApp Folder )
- First install Docker
- Second build the image, Run
docker-compose build - Then start/run the container, run
docker-compose up
Improve Model Accuracy : AI-Interview-ML
Figma UI Design : AI-Interview
- Take a look at the Existing Issues or create your own Issues!
- Wait for the Issue to be assigned to you.
- Fork the repository
- Have a look at Contibuting Guidelines
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