AI-Powered Interview API

A comprehensive interview system with Google Gemini AI integration for question customization, code execution, and intelligent feedback generation.

Features

• AI-Powered Questions: Customized interview questions based on job context and candidate profile
• Code Execution: Execute and validate code submissions against test cases
• Intelligent Hints: AI-generated hints for technical problems
• Technical Feedback: Job-context aware feedback with hireability scoring
• Multi-Language Support: Python, JavaScript
• MongoDB Integration: Persistent session and question storage

Prerequisites

• Go 1.19+
• MongoDB (local or Atlas)
• Google Gemini API key

Setup

1. Install dependencies:

   go mod tidy

2. Set up environment variables:

   # Create .env file with your credentials
   MONGODB_URI=mongodb://localhost:27017/stormhacks
   GEMINI_API_KEY=your_gemini_api_key_here

3. Run the server:

   go run main.go

4. Access the API:

   • Web interface: http://localhost:8080
   • Health check: http://localhost:8080/health

API Endpoints

• POST /api/interview/session - Create interview session
• GET /api/interview-questions - Get AI-customized questions
• POST /api/interview/feedback - Generate interview feedback
• GET /api/technical-question - Get technical questions by difficulty
• POST /api/hint - Generate AI hints
• POST /api/execute-code - Execute and validate code
• POST /api/technical-feedback - Generate technical feedback

Quick Start

1. Create a session:

   curl -X POST http://localhost:8080/api/interview/session \
     -H "Content-Type: application/json" \
     -d '{"parsedResumeText": "...", "jobTitle": "Software Engineer", "jobInfo": "..."}'

2. Get questions:

   curl "http://localhost:8080/api/interview-questions?sessionId=YOUR_SESSION_ID"

3. Execute code:

   curl -X POST http://localhost:8080/api/execute-code \
     -H "Content-Type: application/json" \
     -d '{"questionId": "...", "code": "def solution(): ...", "language": "python"}'

Project Structure

stormhacks-BE/
├── handlers/          # HTTP request handlers
├── services/          # Business logic and AI integration
├── repositories/      # Database operations
├── models/           # Data structures
├── types/            # Request/response types
├── prompts/          # AI prompt templates
├── database/         # MongoDB connection and migrations
└── main.go           # Server entry point

Technologies

• Backend: Go with HTTP handlers
• Database: MongoDB with BSON
• AI: Google Gemini API
• Code Execution: go-piston library
• Architecture: Clean layered architecture (handlers → services → repositories)