Ecolyze

Inspiration

Climate change and sustainability are urgent challenges. Yet, many individuals and organizations struggle to access, interpret, and act on environmental data. I wanted to create a platform that makes real-time environmental insights accessible and actionable using the power of AI and cloud computing.

What it does

Ecolyze is a web app that collects environmental data (like CO₂ levels, temperature, and humidity), stores it in MongoDB, processes it with AI models, and displays actionable summaries through a sleek Streamlit dashboard.

How we built it

Frontend: Built with Streamlit, allowing for rapid dashboard creation and interactivity, Database: Environmental sensor data is ingested and stored in MongoDB Atlas, AI Layer: Python-based models analyze trends and generate summaries, Cloud Integration: Google Cloud Storage and BigQuery are used for backup and advanced querying, Google Cloud Service Accounts handle authentication securely via st.secrets, Google Cloud Functions (optional) support scalable computation.

Challenges we ran into

Setting up and authenticating with Google Cloud credentials inside Streamlit was tricky, especially formatting the private key correctly. Ensuring secure handling of credentials using st.secrets. Avoiding API permission issues with BigQuery IAM roles. Optimizing data handling between MongoDB and BigQuery without excessive latency.

Accomplishments that we're proud of

Successfully integrated real-time data from MongoDB to BigQuery. Built a fully functional, interactive AI dashboard in under a week. Implemented a clean and scalable cloud authentication system. Learned to work efficiently with multiple cloud services in one stack.

What we learned

How to securely integrate Google Cloud services into a Streamlit app. Best practices for handling and storing secrets in cloud-based apps. How to structure data for analytics in BigQuery. Real-world application of AI in climate and environmental tech.

What's next for Ecolyze

Expand to ingest live data from IoT devices and open APIs. Add alerts and notifications for anomalies or thresholds. Integrate with Google Maps API to visualize geospatial trends. Enhance the AI component to provide predictive recommendations for sustainable actions.

Built With

• atlas
• gcp
• github
• google-bigquery
• mongodb
• python
• streamlit