BarkBoard 🐶

BarkBoard is like Pinterest for dogs—discover and collect adorable dog photos in a visually rich, interactive gallery. Powered by Streamlit for fast UI, Sensei for API client generation, and SQLModel for seamless persistence.

🚀 Features

1. Random & Breed-Specific Dogs: Get surprise pups or choose your favorite breed.
2. Pin to Favorites: Save photos to your own gallery for later viewing.
3. Gallery View: Scroll through saved dogs in a clean, grid-based interface.
4. Stats Dashboard: See which breeds you love most with real-time charts.

Screenshots

Main Page

Favorites View

Stats Dashboard

🛠️ Tech Stack

• Streamlit — Build data apps with pure Python, no frontend needed. (docs.streamlit.io)
• Sensei — Generate type-safe API wrappers using decorators and Pydantic models. (github.com)
• SQLModel — Define Python classes that serve as both Pydantic models and SQLAlchemy ORM tables. (sqlmodel.tiangolo.com)
• Dog CEO API — Source of 20,000+ open-source dog images. (dog.ceo)

📦 Installation

# 1. Clone repository
git clone https://github.com/your_username/barkboard.git
cd barkboard

# 2. Create and activate a virtual environment
python -m venv venv
source venv/bin/activate    # macOS/Linux
venv\Scripts\activate       # Windows

# 3. Install dependencies
pip install poetry
poetry install

▶️ Running the App

streamlit run app.py

Open your browser at http://localhost:8501 to view the app.

📂 Project Structure

streampaws/
├── app.py               # Streamlit UI script
├── client/              # Sensei API wrapper module
│   └── api.py           # DogImage model & get_random_dog endpoint
├── models.py            # SQLModel ORM definitions and DB init
├── LICENSE              # License file
├── dogs.db              # Database with featured dogs
├── pyproject.toml       # Pinned dependencies
└── README.md            # This file

📄 License

Released under the MIT License.