🤖 Automated Technical Interviewer

A next-generation AI-powered platform for conducting automated, adaptive, and realistic technical interviews.

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📖 Table of Contents

• Overview

• Key Features

• System Architecture

• Tech Stack

• Getting Started

  • Prerequisites
  • Backend Setup
  • Frontend Setup

• Usage Guide

• API Documentation

• Project Structure

• Roadmap

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🔍 Overview

The Automated Technical Interviewer is designed to streamline the recruitment process by autonomously conducting initial technical screening rounds. Unlike standard quiz-based platforms, this system leverages Google's Gemini 1.5 Pro LLM to simulate a human interviewer. It reads resumes, asks context-aware questions, evaluates code in real-time, and even holds spoken conversations using speech synthesis and recognition.

✨ Key Features

Feature	Description
📄 Resume Analysis	Automatically parses PDF/Docx resumes to extract skills and projects, tailoring the interview context.
🧠 Adaptive AI Interviewer	Uses Gemini 1.5 Pro to generate dynamic follow-up questions based on candidate responses, not just static scripts.
💻 Interactive Coding Environment	Full-featured code editor with syntax highlighting and server-side execution (Sandbox).
🗣️ Voice-Enabled Interaction	Real-time Speech-to-Text (STT) and Text-to-Speech (TTS) for a natural conversational experience.
🛡️ Comprehensive Assessment	Covers MCQ (CS Fundamentals), DSA (Coding), and System Design.
📊 Automated Scoring & Feedback	Generates detailed performance reports immediately after the interview.

🏗 System Architecture

graph TD
    subgraph Frontend ["Frontend (React + Vite)"]
        UI[Web App]
        Audio[Audio Recorder/Player]
        Editor[Monaco Code Editor]
    end

    subgraph Backend ["Backend (FastAPI)"]
        API[FastAPI Router]
        Auth[Auth Service JWT]
        Flow[Interview State Machine]
        Sandbox[Code Executor]
        LLM_Service[LLM Service Wrapper]
    end

    subgraph External_Services ["External Services"]
        Gemini[Google Gemini 1.5 Pro API]
        DB[(SQLite / PostgreSQL)]
    end

    UI -->|HTTP/JSON| API
    UI -->|Audio Blob| API
    
    API --> Auth
    API --> Flow
    
    Flow --> LLM_Service
    Flow --> Sandbox
    Flow --> DB
    
    LLM_Service -->|Prompt/Context| Gemini
    Gemini -->|Response/Feedback| LLM_Service

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🛠 Tech Stack

Backend

• Framework: FastAPI (High-performance Async Python)
• Database: SQLite + aiosqlite (Dev) / PostgreSQL (Prod)
• AI Model: Google Gemini 1.5 Pro
• Speech: gTTS (Google Text-to-Speech)
• ORM: SQLAlchemy (Async)

Frontend

• Framework: React (Vite)
• Language: TypeScript
• Styling: Tailwind CSS + Lucide React (Icons)
• State: React Hooks + Context API
• HTTP Client: Axios

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🚀 Getting Started

Follow these steps to set up the project locally.

Prerequisites

• Python 3.10+
• Node.js 16+ & npm
• Google Cloud API Key (with access to Gemini API)

Backend Setup

1. Clone the repository:

   git clone https://github.com/yourusername/automated-technical-interviewer.git
   cd automated-technical-interviewer/backend

2. Create virtual environment:

   python -m venv venv
   
   # Windows
   venv\Scripts\activate
   
   # Mac/Linux
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt
   pip install aiosqlite  # Required for async SQLite

4. Configure Environment: Create a .env file in the backend/ directory:

   GEMINI_API_KEY=your_google_api_key_here
   DATABASE_URL=sqlite+aiosqlite:///./interview.db
   # REDIS_URL=redis://localhost:6379/0 (Optional)

5. Run the Server:

   uvicorn app.main:app --reload

   ✅ Server running at: http://localhost:8000
   📄 API Docs: http://localhost:8000/docs

Frontend Setup

1. Navigate to frontend directory:

   cd ../frontend

2. Install dependencies:

   npm install

3. Start Development Server:

   npm run dev

   ✅ App running at: http://localhost:5173

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📖 Usage Guide

1. Registration: Sign up as a new candidate on the landing page.
2. Resume Upload: Upload your resume (PDF/DOCX) to initialize the session.
3. Online Assessment (OA):
   • Complete the MCQ Round (CS Fundamentals).
   • Solve the Coding Challenge in the integrated editor.
4. Technical Rounds:
   • Round 1 (DSA): Explain your logic and optimize solutions.
   • Round 2 (System Design): Discuss architecture for scalable systems.
5. Completion: Review your automated feedback report (Coming Soon).

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🔌 API Documentation

Endpoint	Method	Description
/auth/login	POST	Authenticates user & returns JWT.
/candidates/upload_resume	POST	Uploads and parses resume file.
/interview/{id}/state	GET	Retrieves current round and session status.
/interview/{id}/chat	POST	Sends text message to AI interviewer.
/interview/{id}/speak	POST	Sends audio blob for voice interaction.
/interview/{id}/run_code	POST	Executes python code in sandbox.

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📂 Project Structure

├── backend/
│   ├── app/
│   │   ├── routers/        # API route handlers
│   │   ├── services/       # Core business logic (LLM, Flow, Code Exec)
│   │   ├── models.py       # Database schemas
│   │   └── main.py         # Application entry point
│   ├── requirements.txt
│   └── .env
│
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   │   └── interview/  # Interview specific UI components

│   │   ├── pages/          # Full page layouts
│   │   └── context/        # Global state
│   └── package.json

🗺 Roadmap

• WebSocket Integration: For lower latency voice and chat streaming.
• Proctoring: Webcam integration for identity verification.
• Multi-Language Support: Support for Java/C++ in code execution.
• Admin Dashboard: For recruiters to review candidate sessions.

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Built with ❤️ by the Automated Technical Interviewer Team.