https://github.com/how-to-train-your-ai-dragon/automated-interview-filtering
Automated Interview Filtering System
https://github.com/how-to-train-your-ai-dragon/automated-interview-filtering
deepface gradio langchain llamaparse python
Last synced: 6 months ago
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Automated Interview Filtering System
- Host: GitHub
- URL: https://github.com/how-to-train-your-ai-dragon/automated-interview-filtering
- Owner: How-to-Train-Your-AI-Dragon
- License: mit
- Created: 2024-11-24T16:34:22.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-02-27T16:11:25.000Z (7 months ago)
- Last Synced: 2025-02-27T22:52:43.294Z (7 months ago)
- Topics: deepface, gradio, langchain, llamaparse, python
- Language: Python
- Homepage: https://huggingface.co/spaces/RainPoo/Automated-Interview-Filtering
- Size: 644 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
---
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
1. Clone the repository:
```bash
git clone https://github.com/How-to-Train-Your-AI-Dragon/Automated-Interview-Filtering.git
cd automated-interview-filtering
```
2. Create and activate virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Set up environment variables:
```bash
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:
1. Install black and pre-commit:
```bash
pip install black pre-commit
```
2. Run pre-commit hooks:
```bash
pre-commit install
```
3. Run Black manually:
```bash
black .
```
4. Configure VS Code (optional):
```json
{
"python.formatting.provider": "black",
"editor.formatOnSave": true
}
```
Refer to the [Black documentation](https://black.readthedocs.io/en/stable/) for more information. Reference from the article [here](https://dev.to/emmo00/how-to-setup-black-and-pre-commit-in-python-for-auto-text-formatting-on-commit-4kka)
### Git Workflow
#### Creating a New Branch
```bash
# Update main branch
git checkout main
git pull origin main
# Create new feature branch
git checkout -b feature/your-feature-name
```
#### Making Changes
```bash
# 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
```bash
python -m src.presentation.gradio.interface
```
The interface will be available at `http://localhost:7860`
#### Running Tests
```bash
# Run all tests
pytest -v tests/
# Run specific test file
pytest -v tests/unit/test_interview_analyzer.py
# Run with coverage
pytest --cov=src tests/
```
## Configuration
### Environment Variables
```env
OPENAI_API_KEY=your_key_here
GOOGLE_SPEECH_KEY=your_key_here
LLAMAPARSE_API_KEY=your_key_here
FIREFLY_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
1. Clone the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request
### Commit Message Format
```
[]:
[]
[]
```
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`](LICENSE) for more information.