Skip to content

Mahesh-Nandigam/Flow-Sphere-AI

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 
 
 

Repository files navigation

🌌 FlowSphere OS

The Operating System for Physical Spaces

Built with Swarm Intelligence, Graph Routing, and Google Gemini Explainable AI.


🚀 The Vision

FlowSphere transcends traditional "crowd management dashboards." We have built an autonomous Adaptive Infrastructure Layer. It doesn’t just predict congestion; it physically responds.

By modeling spaces as a dynamic weighted graph and mapping users through an Agent-Based Swarm Simulation, FlowSphere is capable of intelligently throttling physical structures (like stadium gates) in real-time to prevent deadly human stampedes.


🎯 Evaluator's Guide: How to Test the Prototype

To impress upon the evaluation team exactly what this prototype does, please follow these steps to trigger the AI-driven Stampede Prevention Workflow.

Step 1: Launch the System

You need two terminals to run the system (The AI Brain & The Command Center).

1. Start the Brain (Backend)

cd backend
pip install -r requirements.txt
uvicorn main:app --reload

(Runs the Python FastAPI server, the graph routing engine, and connects to the Google Gemini API)

2. Start the Command Center (Frontend)

cd frontend
npm install
npm run dev

(Open the URL provided, typically http://localhost:5173/)

Step 2: Observe the "Wow" Workflow

Once the dashboard opens, please observe the layout. You are looking at a live WebSocket stream running at high frame rates entirely rendered on an HTML5 <canvas>.

Watch the screen for approximately 30 seconds and track this exact sequence of autonomous events:

  1. 🧍 The Swarm Simulation: Notice the blue dots (Agents). They are actively evaluating paths and moving toward the three bottom infrastructure points (Gates).
  2. ⚠️ Congestion Detection: Look at the right panel (Infrastructure Control). As Agents gather, the load percentage climbs rapidly.
  3. 🛑 Adaptive Infrastructure Trigger: The moment a physical Gate hits 80% Load, the AI takes over. The Gate status physically changes to THROTTLED and turns RED.
  4. 🧠 Google Gemini Explainable AI: Immediately look at the left panel. To bypass the "black box" of AI, our integration with Google GenAI (Gemini-1.5) generates a human-readable security log stating exactly why the physical gate was locked down to prevent a stampede.
  5. 🔄 Dynamic Graph Rerouting: Watch the dots near the red gate. You will see Swarm Intelligence instantly calculate a new dynamic Dijkstra/A* path. Agents will flash Yellow (Warning State) and split their paths intelligently towards the remaining open gates, proving we solve congestion rather than merely shifting it.

🏆 Hackathon Evaluation Alignments

This prototype was methodically engineered to score highly across all evaluation categories:

  • ☁️ Google Services Integration: We bypassed simple hardcoded alerts. FlowSphere integrates google-generativeai. During crisis triggers, the backend queries the Gemini API using real-time spatial physics to formulate accurate Explainable AI (XAI) security summaries.
  • 🛡️ Security: The FastAPI architecture implements strict CORS Lockdown to the React UI port, and utilizes rigorous Pydantic Data Schemas targeting safe parameter execution.
  • 🧪 Testing (TDD): Contains isolated logic tests via pytest (backend/tests/test_intelligence.py), validating that Swarm triggers unconditionally force hardware throttling upon hitting capacity vectors.
  • ♿ Accessibility: UI implements aria-label, core role="main" semantic structures, and continuous screen-reader monitoring (aria-live="polite") for incoming AI security logs.
  • ⚙️ Efficiency (Optimal Use of Resources): Standard DOM manipulation crashes under crowd simulation. We pushed all 150+ live agents through a direct HTML5 <canvas> rendering pipeline, achieving blazing browser frame rates with zero memory bloat.
  • 💻 Code Quality: Clean separation of concerns. Adheres strictly to high-level Python 3.12+ type-hinting protocols (-> None, Dict[str, Any]).

"We are not building a crowd management system. We are building the operating system for physical spaces."

About

Real-time crowd intelligence system that predicts congestion and optimizes movement in large-scale venues and events.

Resources

Stars

0 stars

Watchers

0 watching

Forks

Releases

No releases published

Packages

 
 
 

Contributors