Eco AI System

Inspiration

Our planet faces severe climate challenges, yet many communities lack engaging, localized educational tools to understand the immediate impact of losing specific botanical species. We built Eco AI System to bridge this generational gap and make nature conservation personal and actionable for everyone.

What it does

Eco AI System is a multi-platform educational suite:

1. Python MVP Application: A responsive web interface that simulates botanical scans. It features a unique adaptive intelligence layer that dynamically shifts its language and depth based on the user's demographic: Explorer Mode for children (offering friendly, narrative-driven insights) and Adults Mode (delivering scientific and rigorous ecological impact data).
2. Scratch Interactive Simulation: A visual playground that mirrors our core logic, enabling younger users to interact with an AI assistant robot to receive instant botanical data.

How we built it

We used Python & Streamlit to code the responsive backend matrix and manage user states via st.session_state for continuous chat options. The Scratch Engine was utilized to create the automated visual simulation flows.

Challenges we ran into

The main technical hurdle was designing a multi-layered response matrix that handles generation-gated content seamlessly. Ensuring that our local environment paths matched remote execution instances required strict attention to modular code logic.

Accomplishments that we're proud of

We are proud of creating a cross-platform educational ecosystem where both Python and Scratch frameworks share the exact same logical rules, bilingul content, and nature conservation goals.

What we learned

Aligning logic between a professional Python backend and a visual Scratch frontend taught us how to design consistent user experiences. In compliance with the rules, we disclose that we utilized OpenAI's ChatGPT to assist in generating clean mockups and structuring code components.

What's next for Eco AI System

Our future goal is connecting live computer vision cameras and real-time LLM APIs to deploy global autonomous botanical guides directly in protected national parks.

Built With

• artificial-intelligence
• python
• scratch
• streamlit