pymodeller 0.1.0

A package that reads models from YAML and creates Pydantic models

PyModeller

Pymodeller is a powerful CLI tool designed to bridge the gap between configuration specifications and Python code. By using a single YAML source of truth, you can automate the generation of Pydantic models, Peewee ORM classes, and manage environment variables with ease.

Features

• Code Generation: Automatically generate typed Pydantic models or Peewee schemas.
• Environment Management: Create .env.example templates directly from your spec.
• Validation: Ensure your local .env files stay in sync with your definitions.
• Drift Detection: Identify discrepancies between your YAML specification and your generated Python code.

---

Installation

Install pymodeller via pip:

 uv add pymodeller

---

Usage

The CLI provides four main commands to manage your development workflow:

1. Code Generation

Generate typed Pydantic models or Peewee code for your project.

pymodeller codegen --input schema.yaml --output models.py

2. Example Environment Generation

Generate a template .env.example file based on your YAML specification to help collaborators set up their environment.

pymodeller example --input schema.yaml

3. Environment Check

Validate your current .env file against the YAML specification to ensure all required variables are present and correctly formatted.

pymodeller check --env .env --spec schema.yaml

4. Drift Detection

Check for "drift" between your YAML specification and the code already generated. This ensures that your Python models haven't fallen out of date.

pymodeller drift --input schema.yaml --code models.py

---

CLI Command Reference

Data Loading & Specification

The project uses a structured YAML-to-Object mapping to manage environment variables and database schemas. The py_modeller.yaml acts as the Single Source of Truth, which is parsed into an EnvSpec instance.

1. YAML Configuration Example

The py_modeller.yaml file defines the structure of your environment and database models. Below is an example of how to configure sections, variables, and nested models:

# Global configuration for output paths
config:
  - name: PYDANTIC_OUT   # master config_models
    value: src/models/config_models.py
  - name: PEEWEE_OUT    # master config peewee
    value: src/models/db_models.py
  - name: PYDANTIC_FOLDER  # pydantic: output pydantic models folder
    value: src/paper_hough/models
  - name: PEEWEE_FOLDER  # peewee: output orm folder
    value: src/paper_hough/models/db

sections:
  # 1. Settings Section: Used for application-wide flags
  - name: General
    description: Top-level application flags
    type: settings
    variables:
      - name: LOCAL_DEV
        description: Enable local development mode
        type: bool
        default: "true"
      - name: API_KEY
        description: Secure token for external services
        type: secret  # Automatically handled as str + secret: true
        required: true

  # 2. Model Section: Defines data structures and types
  - name: Algorithm
    description: Configuration for processing logic
    type: model
    env_prefix: HOUGH
    variables:
      - name: THRESHOLD
        type: int
        default: "100"
      - name: MATRIX_DATA
        description: Raw matrix input
        type: pnd.NpNDArrayUint8 # Specialized numpy-pydantic type
        required: true

  # 3. Database Integration: Mapping fields to DB specs
  - name: UserProfile
    description: Database schema for users
    type: model
    database:
      table_name: users
      primary_key: ["id"]
    variables:
      - name: USERNAME
        type: str
        db_spec:
          max_length: 50
          unique: true
          allow_null: false

  # 4. Nested Models & Lists
  - name: ProcessingQueue
    type: settings
    variables:
      - name: ITEMS
        description: List of algorithm configurations
        from_model: Algorithm # Reference to another section
        type: list
        required: true

Key Mapping Features:

• Type Normalization: Keywords like integer, bool, or path are automatically mapped to Python types.
• Secret Sugar: Setting type: secret is a shortcut that sets the type to str and enables the secret flag for masking.
• Automatic Aliasing: A variable named SERVER_HOST in YAML will be accessible as serverHost in Python via camelCase conversion.
• Model Reusability: Use from_model to create complex, nested structures from existing sections.

2. Object Hierarchy

• EnvSpec: The root object containing all configuration sections.
• EnvSection: Groups variables logically (e.g., General, Algorithm).
  • Supports env_prefix to namespace variables in the shell.
  • Can optionally hold a DBSpec for table-level database metadata.
• EnvVarSpec: Defines individual settings.
  • Automatic Aliasing: Converts SNAKE_CASE names to camelCase attributes automatically during post_init.
  • Secret Handling: If type: secret is used in YAML, it is normalized to str but flagged as hidden for logs/documentation.
• DBField: Detailed field-level constraints for Peewee ORM integration (lengths, nullability, foreign keys, etc.).

3. Validation

The loading process includes a mandatory validate_no_duplicates() call which ensures:

1. No two environment variables share the same env_name.
2. No two Python attributes (aliases) collide within the same section.

Example YAML Specification

Your py_modeller.yaml should define the structure of your models. See file: py_modeller.yaml

---

Contributing

Contributions are welcome! Please feel free to submit a Pull Request or open an issue if you find a bug or have a feature request.

License

This project is licensed under the MIT License.