IntervAI

AI-powered interview question generator — built with Gemini, FastAPI, and vanilla HTML.

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What it does

IntervAI takes a job title and instantly generates 3 thoughtful, role-specific interview questions using Google's Gemini AI. Questions vary by type behavioural, situational, and role-specific knowledge so you walk into every interview prepared.

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Tech Stack

Layer	Technology
Frontend	HTML, CSS, Vanilla JS
Backend	Python, FastAPI
AI	Google Gemini (google-generativeai)
Server	Uvicorn
Deployment	Vercel (frontend) · Railway (backend)

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Project Structure

IntervAI/
├── backend/
│   ├── chat.py          # FastAPI app + Gemini integration
│   ├── requirements.txt # Python dependencies
│   └── .env             # API keys (never commit this)
├── frontend/
│   └── index.html       # Full frontend — single file
└── README.md

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Getting Started

Prerequisites

• Python 3.10+
• A Google Gemini API key — get one free at ai.google.dev

1. Clone the repo

git clone https://github.com/Di-nobi/IntervAI.git
cd IntervAI

2. Set up the virtual environment

python3 -m venv venv
source venv/bin/activate 
venv\Scripts\activate

3. Install dependencies

pip install -r backend/requirements.txt

4. Configure environment variables

Create a .env file inside the backend/ folder:

touch backend/.env

Add your Gemini API key:

API_KEY=your-gemini-api-key-here

5. Run the backend

uvicorn backend.chat:app --reload

The API will be live at http://localhost:8000.

6. Open the frontend

Open frontend/index.html directly in your browser. That's it — no build step needed.

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API Reference

POST /generate

Generates 3 interview questions for a given job title.

Request body:

{
  "jobTitle": "Customer Success Manager"
}

Response:

{
  "questions": [
    "How do you handle a situation where a customer is at risk of churning?",
    "Walk me through how you prioritise accounts when your portfolio is large.",
    "What metrics do you use to define and measure customer success?"
  ]
}

GET /health

Returns the health status of the API.

{ "status": "ok" }

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Deployment

Backend → Railway

1. Push your code to GitHub
2. Go to railway.app and create a new project from your repo
3. Set the root directory to backend/
4. Add your API_KEY under Variables
5. Set the start command to:

   uvicorn chat:app --host 0.0.0.0 --port $PORT
   

Frontend → Vercel

1. Go to vercel.com and import your GitHub repo
2. Set the root directory to frontend/
3. Update BACKEND_URL in index.html to your Railway URL:

   const BACKEND_URL = "https://intervai-production.up.railway.app";

4. Deploy — Vercel handles the rest.

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Environment Variables

Variable	Description
API_KEY	Your Google Gemini API key

⚠️ Never commit your .env file. It's already in .gitignore.

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Built by Di-nobi · Powered by Gemini