Interview Simulator

Interview simulator that uses ChatGPT, Whisper, and Google Text-to-Speech to provide a realistic interview experience. This project requires Docker.

Slides for Project

Quickstart for Teaching Team:

1. Clone the repo

git clone https://gitfront.io/r/user-3333581/8WWEbmiQd3o5/interview-simulator.git

# this is the github link, but I made this repo private, so it wont work unless you have access
git clone https://github.com/theuerc/interview_simulator

2. Run the following commands in the root directory of the repo:

# make the database file
touch dev.db
# copy the environment file
cp .env.example .env
# open the .env file
nano .env

The .env file should include a path to a json file with your Google credentials and an OpenAI API key. The bracketed sections need to be replaced:

# Environment variable overrides for local development
FLASK_APP=autoapp.py
FLASK_DEBUG=1
FLASK_ENV=development
DATABASE_URL=sqlite:////tmp/dev.db
GUNICORN_WORKERS=1
LOG_LEVEL=debug
SECRET_KEY=not-so-secret
# In production, set to a higher number, like 31556926
SEND_FILE_MAX_AGE_DEFAULT=0
# API keys for ChatGPT, Whisper, and Google
OPENAI_API_KEY=[OPENAI_API_KEY]
GOOGLE_APPLICATION_CREDENTIALS=[PATH/TO/GOOGLE/KEY.JSON]

3. Once the dev.db file is created, google credentials are uploaded, and all of the required information is entered in the .env file, run the following commands:

docker-compose build flask-dev
docker-compose run --rm manage db upgrade
docker-compose up flask-dev

Then go to http://localhost:8080/

At this point a sqlite database should be created. You can check this by running the following command in the root directory, or you can just move to the next step:

sqlite3 dev.db
sqlite> .tables
alembic_version  roles            user_files       users 
sqlite> .exit

4. Make a dummy account on the website. These credentials might work:

username: asdf
email: [email protected]
password: asdfasdf

5. Add a resume and transcript. These were made with ChatGPT:

Resume:

JONATHAN MYERS
Data Analyst
Harvard University | Class of 2019
Email: [email protected] | Phone: (123) 456-7890

EDUCATION
Harvard University, Cambridge, MA
Bachelor of Science in Statistics, May 2019
Relevant coursework: Data Analysis and Statistical Inference, Regression Analysis, Applied Time Series Analysis, Data Mining and Machine Learning, Bayesian Statistics.

EXPERIENCE
Data Analyst, ABC Corporation, Boston, MA
June 2019 - Present

Conducted data analysis and visualization to support strategic decision-making across the organization
Developed and implemented predictive models to identify patterns and trends in customer behavior
Created reports and dashboards to communicate insights to executive team and stakeholders
Collaborated with cross-functional teams to identify opportunities for process improvement and efficiency gains
Data Science Intern, XYZ Company, Cambridge, MA
May 2018 - August 2018

Conducted exploratory data analysis and modeling to support product development initiatives
Developed predictive models to forecast sales and customer demand
Created data visualizations and dashboards to communicate insights to stakeholders
Conducted research to identify best practices and emerging trends in data science and analytics
SKILLS

Proficient in programming languages such as Python, R, and SQL
Experience with data analysis and visualization tools such as Tableau and Power BI
Knowledge of statistical modeling techniques and machine learning algorithms
Strong analytical and problem-solving skills
Excellent communication and collaboration skills
AWARDS AND HONORS

Dean's List, Harvard College, 2015-2019
National Merit Scholarship Finalist, 2015
Harvard College Research Program Grant Recipient, 2017
REFERENCES
Available upon request.

Job Description

Data Scientist with Bioethics Background

SciCorp is a rapidly growing biotechnology company that focuses on developing innovative solutions to improve human health. We are seeking a highly motivated data scientist with a strong background in bioethics to join our team. The successful candidate will play a key role in developing and implementing data-driven solutions that address ethical issues in biotechnology research and development.

Responsibilities:

Design and implement data collection and analysis strategies to address ethical issues related to biotechnology research and development
Develop predictive models to identify potential ethical issues and their impacts on the company's research and development initiatives
Conduct ethical reviews of research proposals, including analyzing potential risks and benefits to human health, and proposing ethical solutions
Collaborate with cross-functional teams to design and implement ethical guidelines and best practices for research and development initiatives
Stay up-to-date with emerging trends and developments in bioethics and integrate this knowledge into the company's ethical policies and practices
Communicate complex ethical issues and analyses to both technical and non-technical stakeholders, including regulatory bodies and the public
Qualifications:

PhD in bioethics, philosophy, or a related field
Strong background in biotechnology research and development
Experience in data science, including data collection, analysis, and modeling
Knowledge of statistical analysis tools and programming languages such as Python or R
Familiarity with regulatory frameworks related to biotechnology research and development, such as IRB and FDA regulations
Excellent analytical and problem-solving skills
Strong communication and collaboration skills
Ability to work independently and as part of a team in a fast-paced environment
We offer competitive compensation packages, flexible work schedules, and opportunities for growth and development. If you are passionate about using data science to address ethical challenges in biotechnology research and development, we encourage you to apply.

To apply, please submit your CV, cover letter, and any relevant work samples to [insert email address].

6. Go to the interview page and click on the "Start Interview" button. It will take about 10 seconds to load, and to reply to the interviewer you need to have the microphone in your browser enabled. ChatGPT responses will generate with constructive feedback after it transcribes your voice.

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The next section is the original README.md file from the cookiecutter repo that I used for this project.

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Docker Quickstart

This app can be run completely using Docker and docker-compose. Using Docker is recommended, as it guarantees the application is run using compatible versions of Python and Node.

There are three main services:

To run the development version of the app

docker-compose up flask-dev

To run the production version of the app

docker-compose up flask-prod

The list of environment: variables in the docker-compose.yml file takes precedence over any variables specified in .env.

To run any commands using the Flask CLI

docker-compose run --rm manage <<COMMAND>>

Therefore, to initialize a database you would run

docker-compose run --rm manage db init
docker-compose run --rm manage db migrate
docker-compose run --rm manage db upgrade

A docker volume node-modules is created to store NPM packages and is reused across the dev and prod versions of the application. For the purposes of DB testing with sqlite, the file dev.db is mounted to all containers. This volume mount should be removed from docker-compose.yml if a production DB server is used.

Go to http://localhost:8080. You will see a pretty welcome screen.

Running locally

Run the following commands to bootstrap your environment if you are unable to run the application using Docker

cd interview_simulator
pipenv install --dev
pipenv shell
npm install
npm run-script build
npm start  # run the webpack dev server and flask server using concurrently

Go to http://localhost:5000. You will see a pretty welcome screen.

Database Initialization (locally)

Once you have installed your DBMS, run the following to create your app's database tables and perform the initial migration

flask db init
flask db migrate
flask db upgrade

Deployment

When using Docker, reasonable production defaults are set in docker-compose.yml

FLASK_ENV=production
FLASK_DEBUG=0

Therefore, starting the app in "production" mode is as simple as

docker-compose up flask-prod

If running without Docker

export FLASK_ENV=production
export FLASK_DEBUG=0
export DATABASE_URL="<YOUR DATABASE URL>"
npm run build   # build assets with webpack
flask run       # start the flask server

Shell

To open the interactive shell, run

docker-compose run --rm manage db shell
flask shell # If running locally without Docker

By default, you will have access to the flask app.

Running Tests/Linter

To run all tests, run

docker-compose run --rm manage test
flask test # If running locally without Docker

To run the linter, run

docker-compose run --rm manage lint
flask lint # If running locally without Docker

The lint command will attempt to fix any linting/style errors in the code. If you only want to know if the code will pass CI and do not wish for the linter to make changes, add the --check argument.

Migrations

Whenever a database migration needs to be made. Run the following commands

docker-compose run --rm manage db migrate
flask db migrate # If running locally without Docker

This will generate a new migration script. Then run

docker-compose run --rm manage db upgrade
flask db upgrade # If running locally without Docker

To apply the migration.

For a full migration command reference, run docker-compose run --rm manage db --help.

If you will deploy your application remotely (e.g on Heroku) you should add the migrations folder to version control. You can do this after flask db migrate by running the following commands

git add migrations/*
git commit -m "Add migrations"

Make sure folder migrations/versions is not empty.

Asset Management

Files placed inside the assets directory and its subdirectories (excluding js and css) will be copied by webpack's file-loader into the static/build directory. In production, the plugin Flask-Static-Digest zips the webpack content and tags them with a MD5 hash. As a result, you must use the static_url_for function when including static content, as it resolves the correct file name, including the MD5 hash. For example

<link rel="shortcut icon" href="{{static_url_for('static', filename='build/favicon.ico') }}">

If all of your static files are managed this way, then their filenames will change whenever their contents do, and you can ask Flask to tell web browsers that they should cache all your assets forever by including the following line in .env:

SEND_FILE_MAX_AGE_DEFAULT=31556926  # one year

Heroku

Before deploying to Heroku you should be familiar with the basic concepts of Git and Heroku.

Remember to add migrations to your repository. Please check Migrations_ section.

Since the filesystem on Heroku is ephemeral, non-version controlled files (like a SQLite database) will be lost at least once every 24 hours. Therefore, a persistent, standalone database like PostgreSQL is recommended. This application will work with any database backend that is compatible with SQLAlchemy, but we provide specific instructions for Postgres, (including the required library psycopg2-binary).

Note: psycopg2-binary package is a practical choice for development and testing but in production it is advised to use the package built from sources. Read more in the psycopg2 documentation.

If you keep your project on GitHub you can use 'Deploy to Heroku' button thanks to which the deployment can be done in web browser with minimal configuration required. The configuration used by the button is stored in app.json file.

Deployment by using Heroku CLI:

• Create Heroku App. You can leave your app name, change it, or leave it blank (random name will be generated)

  heroku create interview_simulator

• Add buildpacks

  heroku buildpacks:add --index=1 heroku/nodejs
  heroku buildpacks:add --index=1 heroku/python

• Add database addon which creates a persistent PostgresSQL database. These instructions assume you're using the free hobby-dev plan. This command also sets a DATABASE_URL environmental variable that your app will use to communicate with the DB.

  heroku addons:create heroku-postgresql:hobby-dev --version=11

• Set environmental variables (change SECRET_KEY value)

  heroku config:set SECRET_KEY=not-so-secret
  heroku config:set FLASK_APP=autoapp.py
  heroku config:set SEND_FILE_MAX_AGE_DEFAULT=31556926

• Please check .env.example to see which environmental variables are used in the project and also need to be set. The exception is DATABASE_URL, which Heroku sets automatically.

• Deploy on Heroku by pushing to the heroku branch

  git push heroku main