👩‍💻 InterviewCoach

Do you ever struggle with getting the right answer in your interviews? Get amazing and quick feedback on your interview response with "InterviewCoach"! The InterviewCoach is designed to help you improve your behavioral and technical interview responses.

Ways to use InterviewCoach

👉 Deployed on AWS EC2: https://bit.ly/interviewcoach-v1

Tech Stack

• Frontend: React + Vite + Tailwind CSS
• Backend: Python + FastAPI
• AI: OpenAI GPT-4o-mini
• Deployment: Docker + Kubernetes + AWS EKS

Running Locally

Prerequisites

• Python 3.11+
• Node.js 20+
• OpenAI API key

Setup Steps

1. Clone the repository

   git clone https://github.com/TangoMango223/InterviewCoachAI.git
   cd interviewcoach

2. Set up environment variables

   # Create .env file in the root directory
   echo "OPENAI_API_KEY=your-api-key-here" > .env

3. Build the frontend

   cd web
   npm install
   npm run build
   cd ..

   This builds the React app and copies it to app/static/

4. Set up Python environment

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r app/requirements.txt

5. Run the backend server

   cd app
   uvicorn main:app --host 0.0.0.0 --port 8080

6. Access the app

   Open your browser and go to: http://localhost:8080

Running with Docker

Option 1: Pull Pre-built Image from Docker Hub (Easiest)

Available on Docker Hub: https://hub.docker.com/r/tangomango223/interviewcoach

# Pull the image
docker pull tangomango223/interviewcoach:v1.0.1

# Run the container
docker run -p 8080:8080 -e OPENAI_API_KEY=your-api-key-here tangomango223/interviewcoach:v1.0.1

Then open your browser and go to: http://localhost:8080

Option 2: Build Locally

• This solution requires installing and running Docker on your machine!

1. Build the Docker image

   docker build -t interviewcoach .

2. Run the container

   docker run -p 8080:8080 --env-file .env interviewcoach

3. Access the app

   Open your browser and go to: http://localhost:8080

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Publishing Your Docker Image

To Docker Hub:

1. Login to Docker Hub

   docker login

2. Tag and push your image

   docker tag interviewcoach <your-dockerhub-username>/interviewcoach:latest
   docker push <your-dockerhub-username>/interviewcoach:latest

To AWS ECR Public:

1. Create a public repository (one-time setup)

   aws ecr-public create-repository \
     --repository-name interviewcoach \
     --region us-east-1

2. Authenticate and push

   aws ecr-public get-login-password --region us-east-1 | \
     docker login --username AWS --password-stdin public.ecr.aws
   
   docker tag interviewcoach public.ecr.aws/<your-alias>/interviewcoach:latest
   docker push public.ecr.aws/<your-alias>/interviewcoach:latest

Project Structure

interviewcoach/
├── app/                  # Backend (FastAPI)
│   ├── main.py          # API endpoints
│   ├── prompt.py        # AI prompt configuration
│   ├── requirements.txt # Python dependencies
│   └── static/          # Built frontend (auto-generated)
├── web/                 # Frontend (React)
│   ├── src/
│   │   ├── App.jsx      # Main React component
│   │   └── styles.css   # Tailwind styles
│   └── package.json
├── Dockerfile           # Multi-stage Docker build
├── .env                 # Environment variables (not in git)
└── .gitignore

Development

• Frontend development server: cd web && npm run dev
• Backend development: cd app && uvicorn main:app --reload

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Built for learning deployment concepts: Docker → Kubernetes → Helm → AWS EKS