I Built G-Buudddyy : Bringing AI-Powered Experiences Beyond the Screen

AI Garden Buddy is a local-first gardening assistant that uses AI to help people start gardening. It recommends suitable plants, generates a personalized seven-day gardening plan, and suggests one small outdoor task to complete each day.

Built for the Hacktoberfest Open-Source AI Challenge — Week 1: Touch Grass.

Live Application: https://hacktoberfest-tawny.vercel.app/

GitHub Repository: https://github.com/yogesh968/hacktoberfest

Note: AI plan generation requires the FastAPI backend and Ollama. The deployed frontend needs a reachable backend to generate plans for online users.

React + Vite Frontend | v FastAPI Backend | v Ollama Inference Runtime | v Gemma AI Model | v Validated Gardening Plan | v Outdoor Task and Progress Tracking Enter fullscreen mode Exit fullscreen mode Getting Started Prerequisites Python 3.10 or later Node.js and npm Ollama Sufficient memory and disk space for your selected AI model 1. Clone the Repository git clone https://github.com/yogesh968/hacktoberfest.git cd hacktoberfest Enter fullscreen mode Exit fullscreen mode 2. Download the AI Model Install Ollama and run:

ollama pull gemma3:4b Enter fullscreen mode Exit fullscreen mode Alternatively, use the smaller model:

ollama pull gemma3:1b Enter fullscreen mode Exit fullscreen mode 3. Set Up the Backend cd backend python -m venv.venv Enter fullscreen mode Exit fullscreen mode Activate the environment.

. venv \Scripts\Activate.ps1 Enter fullscreen mode Exit fullscreen mode macOS/Linux:

source.venv/bin/activate Enter fullscreen mode Exit fullscreen mode Install dependencies:

pip install -r requirements.txt Enter fullscreen mode Exit fullscreen mode Copy the example environment file:

# Windows copy.env.example.env # macOS/Linux cp.env.example.env Enter fullscreen mode Exit fullscreen mode 4. Set Up the Frontend Open another terminal from the repository root:

cd frontend npm install Enter fullscreen mode Exit fullscreen mode Copy the frontend environment file:

# Windows copy.env.example.env # macOS/Linux cp.env.example.env Enter fullscreen mode Exit fullscreen mode For local development, configure the backend URL:

VITE_API_BASE_URL=http://localhost:8000 Enter fullscreen mode Exit fullscreen mode 5. Run the Application Start Ollama if it is not already running:

ollama serve Enter fullscreen mode Exit fullscreen mode Start the backend from the backend directory:

uvicorn app.main:app --reload --port 8000 Enter fullscreen mode Exit fullscreen mode Start the frontend from the frontend directory:

npm run dev Enter fullscreen mode Exit fullscreen mode Open the application:

Make sure the model configured in the backend has been downloaded.

Checks backend health and model connectivity.

Generates a personalized gardening plan.

curl -X POST http://localhost:8000/api/garden-plan \ -H "Content-Type: application/json" \ -d '{"city":"Pune","garden_type":"balcony","sunlight":"partial_sun","experience":"beginner","minutes_per_day":15}' Enter fullscreen mode Exit fullscreen mode Use the exact request fields and accepted values defined by the backend's Pydantic model.

The backend uses multiple validation layers to improve reliability:

When AI generation is unavailable, the application can display general gardening advice labelled "Not AI-generated" instead of pretending it came from the model.

The backend includes 13 automated API tests using pytest.

The tests use a fake HTTP transport instead of Ollama, allowing API behavior and failure scenarios to be tested without running an actual model.

python -m pytest -q Enter fullscreen mode Exit fullscreen mode Run frontend checks from the frontend directory:

npm run lint npm run build Enter fullscreen mode Exit fullscreen mode Privacy With the default local configuration:

These privacy statements apply to the local configuration. A remote backend or cloud model may process data outside the user's computer.

Gemma models have their own licensing terms. Gemma 3 is distributed under Google's Gemma Terms of Use, which differ from an OSI-approved open-source license.

Check the applicable terms for the specific model version you use. The application's code license and the AI model's license are separate.

Contributions and suggestions are welcome.

Please do not commit secrets, environment files, or other sensitive information.

AI Garden Buddy explores how locally run AI can help people move from browsing and learning to taking meaningful action in the real world.