metadata

title: Automated Interview Filtering
emoji: 🐉
colorFrom: red
colorTo: purple
sdk: gradio
sdk_version: 5.6.0
app_file: src/app.py
pinned: true

Automated Interview Filtering

Overview

The Automated Interview Filtering System is an AI-powered tool that helps HR professionals streamline their interview process through automated analysis of video interviews, resumes, and candidate responses. The system leverages multiple AI technologies including emotion detection, speech-to-text conversion, and natural language processing to provide comprehensive candidate assessments.

Architecture

The project follows a clean, layered monolith architecture:

src/
├── domain/      # Core business logic and entities
├── service/     # Use cases and business rules
├── utils/       # Helper functions and utilities
└── app.py       # Frontend interface
tests/
└── integration/ # Integration tests

Key Components

Domain Layer: Contains business entities, value objects, and enums
Service Layer: Core business logic and use cases
- LangChain for LLM integration
- DeepFace for emotion analysis
- Google Speech-to-Text for audio transcription
- LlamaParse for resume parsing
Frontend Interface: Gradio-based user interface
Utils: Helper functions and utilities

Technologies Used

Frontend: Gradio
AI/ML:
- LangChain for LLM operations
- DeepFace for facial emotion analysis
- Google Speech-to-Text API
- LlamaParse for resume parsing
Development Tools:
- Python 3.9+
- Black for code formatting
- pytest for testing
- pre-commit hooks

Prerequisites

Python 3.9 or higher
pip package manager
Git

Installation

Clone the repository:

git clone https://github.com/How-to-Train-Your-AI-Dragon/Automated-Interview-Filtering.git
cd automated-interview-filtering

Create and activate virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Set up environment variables:

cp .env.example .env
# Edit .env with your API keys and configurations

Development Setup

Code Formatting

We use Black for code formatting. To set up:

Install black and pre-commit:

pip install black pre-commit

Run pre-commit hooks:

pre-commit install

Run Black manually:

black .

Configure VS Code (optional):

{
    "python.formatting.provider": "black",
    "editor.formatOnSave": true
}

Refer to the Black documentation for more information. Reference from the article here

Git Workflow

Creating a New Branch

# Update main branch
git checkout main
git pull origin main

# Create new feature branch
git checkout -b feature/your-feature-name

Making Changes

# Stage changes
git add .

# Commit changes
git commit -m "feat: your descriptive commit message"

# Push to remote
git push origin feature/your-feature-name

Running the Application

Starting Gradio Interface

python -m src.presentation.gradio.interface

The interface will be available at http://localhost:7860

Running Tests

# Run all tests
pytest

# Run specific test file
pytest tests/unit/test_interview_analyzer.py

# Run with coverage
pytest --cov=src tests/

Configuration

Environment Variables

OPENAI_API_KEY=your_key_here
GOOGLE_SPEECH_KEY=your_key_here
LLAMAPARSE_API_KEY=your_key_here

Supported File Formats

Video: MP4, AVI, MOV, WMV
Resume: PDF, DOCX, DOC, TXT

Error Handling

The system implements comprehensive error handling for:

Invalid file formats
API failures
Resource limitations
Processing errors

Contributing

Clone the repository
Create your feature branch
Commit your changes
Push to the branch
Create a Pull Request

Commit Message Format

[<type>]: <subject>

[<body>]

[<footer>]

Types:

feat: New feature
fix: Bug fix
docs: Documentation
style: Formatting
refactor: Code restructuring

License

This project is licensed under the MIT License. See LICENSE for more information.