# Models Directory

This directory contains pure data models for the Paper Dynasty web app, migrated from the Discord app following the **Model/Service Architecture** pattern.

## Architecture Principle

**Models = Pure Data | Services = Business Logic**

- **Models**: Field definitions, relationships, basic validators only
- **Services**: Complex logic, UI formatting, game management, AI decisions

## Migration Status

### ✅ Completed Models

| Model | Status | Description | Business Logic Extracted |
|-------|--------|-------------|--------------------------|
| `ManagerAi` | ✅ Complete | AI configuration data | → `AIService` (9 methods) |
| `Cardset` | ✅ Complete | Card set metadata | None (pure data) |

### 🚧 In Progress

| Model | Status | Description | Business Logic to Extract |
|-------|--------|-------------|--------------------------|
| `Team` | 📋 Next | Team identity data | → `UIService` (embed property) |
| `Player` | 📋 Planned | Player metadata | → `UIService` (Discord markdown) |

### 📋 Future Phases

- **Phase 3**: Game structure (Game, Play models)
- **Phase 4**: Card and rating models
- **Phase 5**: Web-specific models (sessions, preferences)

## Model Patterns

### Pure Data Model Structure

```python
# Base model for validation and field definitions
class ModelBase(SQLModel):
    id: int | None = Field(default=None, primary_key=True)
    name: str = Field(index=True, description="Field description")

    @field_validator('name')
    @classmethod
    def validate_name(cls, v: str) -> str:
        # Basic validation only
        if not v or not v.strip():
            raise ValueError("Name cannot be empty")
        return v

# Table model for database operations
class Model(ModelBase, table=True):
    # relationships: List["RelatedModel"] = Relationship(...)
    pass
```

### What STAYS in Models

✅ **Field definitions and types**
```python
name: str = Field(index=True)
ranked_legal: bool = Field(default=False)
```

✅ **Database relationships**
```python
players: List["Player"] = Relationship(back_populates="cardset")
```

✅ **Basic field validation**
```python
@field_validator('name')
def validate_name_not_empty(cls, v: str) -> str:
    if not v.strip():
        raise ValueError("Name cannot be empty")
    return v
```

### What MOVES to Services

❌ **Complex business logic**
```python
# BEFORE (in model)
def check_steal_opportunity(self, game, to_base):
    # Complex AI decision logic...

# AFTER (in service)
def check_steal_opportunity(self, manager_ai, game, to_base):
    # Same logic but in AIService
```

❌ **UI formatting**
```python
# BEFORE (in model)
@property
def embed(self) -> discord.Embed:
    # Discord-specific formatting...

# AFTER (in service)
def format_team_display(self, team) -> dict:
    # Platform-agnostic formatting
```

❌ **Game mechanics**
```python
# BEFORE (in model)
def initialize_play(self, session):
    # Complex game setup logic...

# AFTER (in service)
def initialize_game(self, game_id) -> Play:
    # Same logic but in GameService
```

## Testing Strategy

All models use the factory pattern with transaction rollback:

```python
# test_model.py
def test_model_creation(db_session):
    model = ModelFactory.create(db_session, field="value")
    assert model.field == "value"
    # Automatic rollback ensures isolation
```

See `tests/README.md` for complete testing documentation.

## File Organization

```
models/
├── __init__.py              # Export all models
├── manager_ai.py           # ✅ AI configuration (complete)
├── cardset.py              # ✅ Card set metadata (complete)
├── team.py                 # 🚧 Team identity (next)
├── player.py               # 📋 Player metadata (planned)
├── game.py                 # 📋 Game structure (planned)
├── play.py                 # 📋 Gameplay state (planned)
└── ai_responses.py         # AI decision response models
```

## Migration Guidelines

When migrating a model from `../discord-app/`:

### 1. Analyze Original Model
```bash
# Find the model in Discord app
grep -r "class ModelName" ../discord-app/
```

### 2. Separate Data from Logic
- **Keep**: Field definitions, relationships, basic validation
- **Extract**: Methods, computed properties, complex logic

### 3. Create Pure Data Model
```python
class ModelBase(SQLModel):
    # Only field definitions and basic validation

class Model(ModelBase, table=True):
    # Only relationships
```

### 4. Extract Business Logic
```python
class ModelService(BaseService):
    def extracted_method(self, model_instance, params):
        # Migrated business logic
```

### 5. Create Comprehensive Tests
```python
# Validation tests (no database)
def test_model_validation():
    model = ModelFactory.build(invalid_field="bad")
    # Test validation

# Database tests (with rollback)
def test_model_persistence(db_session):
    model = ModelFactory.create(db_session)
    # Test database operations
```

### 6. Update Migration Plan
- Mark model as complete in `.claude/model-migration-plan.md`
- Update this README with new model status

## Dependencies

Models depend on:
- `sqlmodel` - Database ORM and validation
- `pydantic` - Field validation and serialization
- `sqlalchemy` - Advanced database features

Models should NOT depend on:
- `discord.py` - Platform-specific library
- `fastapi` - Web framework
- Service classes - Business logic layer

## Best Practices

### DO:
- ✅ Keep models as simple data containers
- ✅ Use descriptive field documentation
- ✅ Add basic validation for data integrity
- ✅ Follow naming conventions from original models
- ✅ Create comprehensive factory-based tests

### DON'T:
- ❌ Add business logic methods to models
- ❌ Include platform-specific dependencies
- ❌ Create computed properties with complex logic
- ❌ Hard-code values that belong in services
- ❌ Skip validation or tests

## Future Considerations

As we complete the migration:

1. **Web-specific models** will be added for session management
2. **Performance optimization** may require relationship tuning
3. **Database migrations** will be managed via Alembic
4. **API serialization** will use Pydantic's serialization features

The goal is to have a clean, testable, platform-agnostic data layer that can support web, mobile, and future interfaces.