🌱 GreenBuddy AI — Less Scrolling, More Exploring
Live Demo: https://greenbuddy-ai-kxqx3wshurh5ggzjx9nb4v.streamlit.app/
GitHub Repository: https://github.com/kavin553/GreenBuddy-AI
🌍 The Problem
We spend a lot of time looking at screens, even when we have free time to enjoy the world around us. Sometimes, we want to go outside or try something new, but we don't know what activity to choose.
I built GreenBuddy AI, a nature companion that recommends practical activities based on a person's available time, interests, and surroundings.
🌿 What Does GreenBuddy AI Do?
Users can select their available time, interests, and preferred location to receive a personalised activity recommendation.
Key features include:
- 🌱 Personalised nature and outdoor activity suggestions
- ⏱️ Activity duration checks to match the user's available time
- 🔄 Attempts to avoid recommending recently completed activities
- ✅ A feature to mark activities as completed
- 📚 An activity history view
- 🍃 A nature-inspired, responsive Streamlit interface
🛠️ Tech Stack
- Python
- Streamlit
- Groq API for the deployed AI experience
- Requests for API communication
- GitHub for source control
- Streamlit Community Cloud for deployment
🧠 What I Learned
Building GreenBuddy AI taught me how to connect an AI model to a user-facing application, design a simple interaction flow, handle API errors, and deploy a Python application online.
My initial local prototype used Ollama with the Qwen2.5:3b model. For the public cloud deployment, I switched to a hosted AI API because the local Ollama service on my laptop could not be accessed directly by the cloud app.
This was an important lesson: deploying an AI application requires decisions about model hosting, API security, cost, and data persistence—not just building the interface.
🔓 Why Open Innovation Matters
Open models and open-source tools give developers opportunities to experiment, learn, and build applications they can adapt to their needs.
My initial local-model experiment helped me understand the benefits and limitations of running AI on a personal device. I want to explore a deployment that supports local open-weight inference alongside a hosted option.
🚀 Try It
Live Demo: https://greenbuddy-ai-kxqx3wshurh5ggzjx9nb4v.streamlit.app/
Source Code: https://github.com/kavin553/GreenBuddy-AI
I'd love to hear your suggestions: what small activity would help you spend less time on screens and more time exploring the world?
Let's make technology a companion to real-world experiences, not a replacement for them. 🌿
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