Resume Agent Template Engine

A powerful template engine for generating professional resumes and cover letters using LaTeX.

Features

• Multiple Template Support: Choose from a variety of professionally designed templates
• JSON & YAML Support: Input data in either JSON or YAML format for flexibility
• Dynamic Content Generation: Automatically generate content based on user input
• LaTeX-based Templates: High-quality, customizable templates
• RESTful API: Easy integration with other applications
• Template Preview: Preview templates before generating the final document
• Cover Letter Support: Generate matching cover letters for your resume
• Customizable Sections: Add, remove, or modify sections as needed
• CLI Tool: Command-line interface for batch processing and automation

Project Structure

resume-agent-template-engine/
├── src/
│   └── resume_agent_template_engine/
│       ├── core/
│       │   ├── resume_template_editing.py
│       │   └── template_utils.py
│       ├── templates/
│       │   ├── resume/
│       │   │   └── [template_name]/
│       │   │       ├── template.tex
│       │   │       ├── helper.py
│       │   │       ├── preview.png
│       │   │       └── README.md
│       │   └── cover_letter/
│       │       └── [template_name]/
│       │           ├── template.tex
│       │           ├── helper.py
│       │           ├── preview.png
│       │           └── README.md
│       ├── api/
│       │   └── routes.py
│       ├── examples/
│       │   └── example_usage.py
│       └── app.py
├── tests/
├── docs/
├── .github/
│   └── workflows/
│       └── ci.yml
├── pyproject.toml
├── uv.lock
└── run.py

System Requirements

• Python 3.9+
• uv package manager
• LaTeX distribution (MiKTeX, TeX Live, or MacTeX)

Installation

Quick Setup (Recommended for Developers)

1. Install uv (if not already installed):

   curl -LsSf https://astral.sh/uv/install.sh | sh

2. Clone the repository:

   git clone https://github.com/taleye-com/resume-agent-template-engine
   cd resume-agent-template-engine

3. Run the development setup script:

   chmod +x setup-dev.sh
   ./setup-dev.sh

This script will:

• Sync all dependencies with uv
• Set up pre-commit hooks
• Format the code
• Run tests to ensure everything works

Manual Setup

1. Install uv:

   curl -LsSf https://astral.sh/uv/install.sh | sh

2. Clone the repository:

   git clone https://github.com/taleye-com/resume-agent-template-engine
   cd resume-agent-template-engine

3. Sync dependencies with uv:

   uv sync

4. Install pre-commit hooks (for development):

   uv run pre-commit install

5. Install LaTeX:

   • Windows: Install MiKTeX
   • macOS: Install MacTeX
   • Linux: Install TeX Live:

     sudo apt-get install texlive-full

Usage

Option 1: Streamlit UI (Recommended for Users)

Launch the user-friendly web interface:

# Launch the UI
uv run python run.py --ui

Navigate to http://localhost:8502 for an intuitive form-based interface to:

• Fill in your information using dynamic forms
• Input data in JSON or YAML format with syntax highlighting
• Select from available templates
• Generate and download professional PDFs
• Preview templates and understand data schemas

See UI_README.md for detailed UI documentation.

Option 2: Command Line Interface (For Automation)

The CLI supports both JSON and YAML input formats:

# Generate sample data files
uv run python -m resume_agent_template_engine.cli sample resume data.json
uv run python -m resume_agent_template_engine.cli sample resume data.yaml

# Generate PDF from JSON
uv run python -m resume_agent_template_engine.cli generate resume classic data.json output.pdf

# Generate PDF from YAML
uv run python -m resume_agent_template_engine.cli generate resume classic data.yaml output.pdf

# List available templates
uv run python -m resume_agent_template_engine.cli list

# Get template information
uv run python -m resume_agent_template_engine.cli info resume classic

Option 3: API (For Developers and Integration)

1. Start the server:

   uv run python run.py --api

2. Access the API:

   • Resume generation (JSON): http://localhost:8501/generate
   • Resume generation (YAML): http://localhost:8501/generate-yaml
   • Cover letter generation: http://localhost:8501/generate
   • Template preview: http://localhost:8501/templates
   • Schema information: http://localhost:8501/schema/{document_type}

3. Example API requests:

   JSON Format:

   curl -X POST http://localhost:8501/generate \
     -H "Content-Type: application/json" \
     -d '{
       "document_type": "resume",
       "template": "classic",
       "data": {
         "personalInfo": {
           "name": "John Doe",
           "email": "[email protected]"
         },
         "professionalSummary": "...",
         "education": [...],
         "experience": [...],
         "projects": [...],
         "articlesAndPublications": [...],
         "achievements": [...],
         "certifications": [...],
         "technologiesAndSkills": [...]
       }
     }'

   YAML Format:

   curl -X POST http://localhost:8501/generate-yaml \
     -H "Content-Type: application/json" \
     -d '{
       "document_type": "resume",
       "template": "classic",
       "yaml_data": "personalInfo:\n  name: John Doe\n  email: [email protected]\nprofessionalSummary: ..."
     }'

Template Format

Templates are organized in two main categories:

1. Resume Templates: Located in templates/resume/
2. Cover Letter Templates: Located in templates/cover_letter/

Each template directory contains:

• template.tex: The main LaTeX template file
• helper.py: Template-specific helper functions
• preview.png: Template preview image
• README.md: Template-specific documentation

Data Format

The template engine accepts data in both JSON and YAML formats:

JSON Format

{
  "personalInfo": {
    "name": "John Doe",
    "email": "[email protected]",
    "phone": "+1 (555) 123-4567",
    "location": "New York, NY",
    "website": "https://johndoe.dev",
    "linkedin": "https://linkedin.com/in/johndoe",
    "website_display": "https://johndoe.dev",
    "linkedin_display": "https://linkedin.com/in/johndoe"
  },
  "professionalSummary": "Experienced software engineer...",
  "education": [
    {
      "degree": "Bachelor of Science in Computer Science",
      "institution": "University of Technology",
      "startDate": "2015-09",
      "endDate": "2019-05"
    }
  ],
  "experience": [
    {
      "title": "Senior Software Engineer",
      "company": "Tech Corp",
      "startDate": "2020-01",
      "endDate": "Present",
      "achievements": [
        "Reduced system latency by 40%",
        "Led team of 5 engineers"
      ]
    }
  ],
  "projects": [
    {
      "name": "Cloud Platform",
      "description": ["Scalable microservices platform"],
      "tools": ["Python", "Docker", "Kubernetes"]
    }
  ],
  "articlesAndPublications": [
    {
      "title": "Microservices Best Practices",
      "date": "2023-03"
    }
  ],
  "achievements": [
    "AWS Certified Solutions Architect",
    "Led migration to cloud-native architecture"
  ],
  "certifications": [
    "AWS Certified Solutions Architect - Professional (2023)"
  ],
  "technologiesAndSkills": [
    {
      "category": "Programming Languages",
      "skills": ["Python", "JavaScript", "TypeScript"]
    }
  ]
}

YAML Format

personalInfo:
  name: John Doe
  email: [email protected]
  phone: "+1 (555) 123-4567"
  location: New York, NY
  website: https://johndoe.dev
  linkedin: https://linkedin.com/in/johndoe
  website_display: https://johndoe.dev
  linkedin_display: https://linkedin.com/in/johndoe

professionalSummary: "Experienced software engineer..."

education:
  - degree: Bachelor of Science in Computer Science
    institution: University of Technology
    startDate: "2015-09"
    endDate: "2019-05"

experience:
  - title: Senior Software Engineer
    company: Tech Corp
    startDate: "2020-01"
    endDate: Present
    achievements:
      - Reduced system latency by 40%
      - Led team of 5 engineers

projects:
  - name: Cloud Platform
    description:
      - Scalable microservices platform
    tools:
      - Python
      - Docker
      - Kubernetes

articlesAndPublications:
  - title: Microservices Best Practices
    date: "2023-03"

achievements:
  - AWS Certified Solutions Architect
  - Led migration to cloud-native architecture

certifications:
  - "AWS Certified Solutions Architect - Professional (2023)"

technologiesAndSkills:
  - category: Programming Languages
    skills:
      - Python
      - JavaScript
      - TypeScript

Development

Code Quality and CI/CD

The project uses GitHub Actions for continuous integration and deployment. The workflow includes:

• Linting: Code formatting checks with Black and type checking with MyPy
• Testing: Comprehensive test suite with coverage reporting
• Code Coverage: Automated coverage reporting with Codecov

Pre-commit Hooks

The project uses pre-commit hooks to ensure code quality:

# Install pre-commit hooks
pre-commit install

# Run hooks manually
pre-commit run --all-files

Running Tests

# Run all tests with coverage
cd src
PYTHONPATH=$PYTHONPATH:$(pwd) pytest ../tests/ --cov=resume_agent_template_engine --cov-report=term

# Run specific test types
pytest ../tests/unit/          # Unit tests only
pytest ../tests/integration/   # Integration tests only
pytest ../tests/e2e/          # End-to-end tests only

Code Formatting

# Format code
black src/ tests/

# Check formatting without making changes
black --check src/ tests/

CI/CD Pipeline

The GitHub Actions workflow (/.github/workflows/ci.yml) runs:

1. Lint Job: Code formatting and type checking
2. Test Job: Comprehensive test suite with coverage reporting

Troubleshooting CI Issues

If the CI is failing:

1. Code Formatting Issues: Run black src/ tests/ locally and commit the changes
2. Test Failures: Run tests locally to identify and fix issues
3. Codecov Rate Limiting: This is temporary and doesn't affect the build status

To contribute, please follow the guidelines in CONTRIBUTING.md.

License

This project is licensed under the MIT License - see the LICENSE file for details.