AI Recruiter

Live Link - https://ai-recruiter-tau.vercel.app/

AI Recruiter is an intelligent platform that streamlines and enhances the hiring process by leveraging AI technologies to conduct mock interviews, create customized interviews for recruiters, and facilitate real-time video interviews with automated scoring and feedback.

AI Recruiter has two main components:

• Frontend: Built using React and Three.js, the frontend provides an interactive and visually engaging user interface.
• Backend: Powered by FastAPI, the backend handles data processing, interview session management, AI-driven question generation, audio transcription, and feedback analysis.

• Mock Interview:

  • Allows candidates to quickly prepare for interviews by entering a job role.
  • Automatically generates interview questions for the specified role.

• Recruiter Dashboard:

  • Uploading a Job Description (JD) file.
  • Uploading a candidate's resume.
  • Adding custom interview questions.
  • Automatically generates questions based on the JD and resume.

• Video Interview:

  • Enables real-time interviews where candidates respond to AI-generated questions.
  • Automatically transcribes and analyzes audio responses.
  • Provides scores and feedback for:
    • Technical Proficiency
    • Communication Skills
    • Cultural Fit

• Job Description Management:

Upload and process JDs to extract role-specific details like title, company, and requirements.

• Resume Management:

Upload and process resumes to extract candidate information, skills, and experience.

• Interview Session Management:

  • Combine JD and resume to create interview sessions.
  • Generate AI-powered questions tailored to the JD and resume.
  • Edit, update, and confirm questions before starting the interview.

• Mock Interview Generation:

Dynamically generate a JD and interview questions for candidates without requiring any uploads.

• Audio Transcription:

Converts candidate audio responses into text using OpenAI Whisper API.

• AI Feedback and Scoring:

  • Analyzes transcriptions to provide scores and feedback on:
  • Relevance to the question.
  • Clarity of response.
  • Technical accuracy.
  • Generates improvement suggestions and highlights strengths.

• Text-to-Speech:

Responds to candidates with audio feedback using ElevenLabs API.

• React: Component-based architecture for building the user interface.
• Three.js: 3D animations and visual effects for the landing animation and background.
• CSS: Styling for components and layouts.

• FastAPI: Framework for building the RESTful API.
• SQLAlchemy: ORM for database operations.
• OpenAI Whisper API: Audio-to-text transcription.
• OpenAI GPT-like models: AI-powered question generation and feedback.
• ElevenLabs API: Text-to-speech functionality for audio responses.

• SQLite: Used as the default database (can be swapped for PostgreSQL, MySQL, etc.).

• Navigate to the Mock Interview tab in the app.
• Enter a job role and click Create Mock Interview.
• The backend generates a dynamic JD and interview questions for the role.

• Navigate to the Recruiter Dashboard tab.
• Upload: - Job Description file. - Candidate Resume file. - Customize or confirm AI-generated interview questions.
• A unique Interview ID is generated.

• Navigate to the Video Interview tab.
• Enter the Interview ID generated from the Recruiter Dashboard or Mock Interview.
• Answer questions in real-time, and the backend:
• Transcribes audio.
• Scores and analyzes responses.
• Provides feedback.

1. Node.js and npm (for the frontend).
2. Python 3.8+ (for the backend).
3. Environment Variables:

• DATABASE_URL: Database connection string (default: SQLite).
• OPENAI_API_KEY: OpenAI API key for transcription and question generation.
• GROQ_API_KEY: API key for LLM-based question generation.
• ELEVENLABS_API_KEY: ElevenLabs API key for text-to-speech functionality.

1. Clone the repository:

git clone https://github.com/kodegod/ai-recruiter.git

2. Install dependencies:

npm install

3. Set up environment variable

REACT_APP_API_URL = http://localhost:8000

4. Start the development server:

npm start

The app will be available at http://localhost:3000.

1. Install dependencies:

pip install -r requirements.txt

2. Set up environment variables:

Create a .env file in the backend directory:

OPENAI_API_KEY=<your_openai_api_key>
GROQ_API_KEY=<your_groq_api_key>
ELEVENLABS_API_KEY=<your_elevenlabs_api_key>

3. Start the server:

uvicorn main:app --reload