Key Features

• Instant access to the complete Pokémon database
• Personal collections with notes and favorites
• Intuitive visualization of Pokémon data
• Persistent storage with easy retrieval
• User-friendly interface for researchers

Technical Highlights

• PokeAPI integration with caching
• Clean separation of concerns (MVC pattern)
• Multiple interfaces (GUI, console)
• Comprehensive test coverage
• Documentation and examples

Component Breakdown

• Presentation Layer (Brock): Streamlit web interface and console tools
• Business Logic Layer (Misty): PokeAPI integration, Pokemon collections, and operations
• Data Layer (Serena): Storage interfaces, JSON implementation, and data validation
• Integration (Ash): Cross-component communication and project structure

Directory Structure

tool-pokeapi/
├── core/                 # Core functionality
│   ├── models.py         # Data models
│   ├── storage.py        # Storage implementation
│   ├── operations.py     # Collection operations
│   └── pokeapi_service.py # API integration
├── interfaces/
│   └── streamlit_app/    # Streamlit interface
├── examples/             # Example scripts
├── tests/                # Unit tests
└── docs/                 # Documentation

PokeAPI Integration

Seamless integration with the PokeAPI to retrieve comprehensive Pokemon data:

# Search for Pokémon
results = PokeApiService.search_pokemon("pikachu")

# Get Pokémon details
details = PokeApiService.get_pokemon_details("pikachu")

# Create Pokémon object
pokemon = PokeApiService.create_pokemon_from_api_data(details)

Implemented by: Misty

Data Persistence

Flexible storage system with JSON implementation:

# Initialize storage
storage = JsonStorage("pokemon_collection.json")

# Create collection
collection = PokemonCollection(storage)

# Add, retrieve, update, and delete operations
collection.add_pokemon(pokemon)
collection.get_pokemon(pokemon_id)
collection.update_pokemon(pokemon)
collection.delete_pokemon(pokemon_id)

Implemented by: Serena

User Interface

Intuitive Streamlit interface with rich visualization:

• Responsive grid layout for Pokemon cards
• Type-based color coding for easy identification
• Progress bars for visualizing Pokemon stats
• Expandable sections for detailed information

Implemented by: Brock

Pokemon Model

Comprehensive data model for Pokemon with validation:

@dataclass
class Pokemon:
    name: str
    pokemon_id: int
    types: List[str]
    sprites: Dict[str, str]
    height: int
    weight: int
    stats: List[Dict[str, any]]
    abilities: List[str]
    moves: List[str]
    favorite: bool = False
    notes: str = ""
    id: str = field(default_factory=lambda: str(uuid4()))
    date_added: datetime = field(default_factory=datetime.now)

Implemented by: Ash

Coverage Targets

Our test suite maintains high coverage across components:

Quality Assurance Process

Led by Serena, our QA process includes:

• Automated Tests: Run on every code change
• Code Reviews: Every PR requires approval
• Documentation: Required for all new features
• Type Hinting: Used throughout the codebase
• Error Handling: Graceful recovery from failures

Run It Yourself

To run PokeSense on your own machine:

# Clone the repository
git clone https://github.com/CTS285_FA22/tool-pokeapi.git

# Install dependencies
pip install -r requirements.txt

# Run the Streamlit web interface
cd interfaces/streamlit_app
streamlit run pokeapi_app.py

# Or try the MVP console demo
cd examples
python pokemvp.py

Future Enhancements

Planned features for future releases:

• Team building with type coverage analysis
• Evolutionary chain visualization
• Advanced filtering by stats
• Mobile responsive design
• Dark mode support
• Internationalization