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Project Info

Blaize - Realtime Intelligent Wildfire Information

Devpost

This project did not submit a demo video on Devpost.

Inspiration

Last month, the three of us had our camping trip in the San Bernardino National Forest cut short by the Line Fire as it exploded in size overnight. As we awaited evacuation notices and warnings from official sources, we found ourselves frequently checking different sources for the latest information, which became a hassle as we raced against the clock to pack up our camping gear and prepared to evacuate.

What it does

Blaize automatically gathers and compiles information from authoritative sources, and stores the latest information in one place. Not only can users can view the latest notices and evacuation information from a single app, but they can text or speak with our intelligent AI agent which always has access to a rich set of information in addition to the latest updates from authorities.

How we built it

We built Blaize using a Flask backend for retrieving and compiling information from the WatchDuty API, and utilizes redis for quick information caching. The frontend was built in Vue.js with Tailwind CSS, and the AI agent is provided through Deepgram, based on Google's Gemini-1.5. The AI agent is fed the latest information in a carefully crafted prompt through our Flask API, and can answer user's questions pertaining to a specific wildfire.

Challenges we ran into

We faced slow response times, mostly due to poor wifi connection during development. This made us realize, however, that this simulated an environment closer to the app's real use case, where users may not have access to good cell service. We then implemented a Redis cache to improve load times.

Accomplishments we're proud of

Speaking to or texting the AI agent feels responsive, informative, and realistic, akin to speaking with a trusted expert. We are proud to have built an agent that will quickly and concisely respond to those in affected wildfire areas, which will help to provide information during periods where emergency services are often constrained.

What we learned

We learned the importance of considering real-world conditions during development, such as limited connectivity, which prompted us to implement caching strategies to enhance performance. We also gained valuable experience integrating various technologies, such as combining real-time data APIs with an AI agent to provide dynamic and contextually relevant information.

What's next

We would like to iterate on and streamline our backend, and develop a feature-complete mobile app for iOS and Android. After achieving this, we hope to get in contact with wildfire information sources such as WatchDuty for new integrations and potential collaborations.

Analysis

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Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only
  • CSSIn code
  • FlaskIn code
  • HTMLIn code
  • JavaScriptIn code
  • OpenAIIn code
  • PythonIn code
  • RedisIn code
  • Tailwind CSSIn code
  • VueIn code
  • Google GeminiClaimed

9 of 10 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

67 KB

Source files

42

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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