# Project export: VisionZ+ This document was generated by HackStack to give an AI agent context about a hackathon project. Sections are labeled with their provenance; content marked as truncated was cut to keep this document small. ## Project metadata - Hackathon: TreeHacks 2025 - Tagline: VisionZ+ is your data scientist. Itโ€™s a big data analytics and visualization dashboard that transforms any dataset into sleek graphs, killer insights, and bold predictions. Drag, drop, and dominate. - Devpost: https://devpost.com/software/visionz - GitHub: https://github.com/AntoDono/visionzplus - Video: https://www.youtube.com/embed/8bB2tO9vXhY?enablejsapi=1&hl=en_US&rel=0&start=&version=3&wmode=transparent - Team: 3 GitHub contributor(s) โ€” AntoDono (34 commits), Taorui Huang (13 commits), Nick (2 commits) ## Devpost submission (written by the team) ### Overview VisionZ+ is your data scientist. Our Team!! ### Inspiration We are mainly interested in Palmer Luckey's disruptive innovations in defense systems and Marc Andreessen's forward-thinking vision of technology, this project embraces American dynamism to empower military personnel. The aim is to leverage cutting-edge wearable tech to boost productivity and efficiency on the field. ### What it does VisionZ+ is a first-of-its-kind data visualization and analysis platform that lets users analyze any dataset through a simple drag-and-drop interface. With our dataset assistant, you can simply ask questions about your data and let the AI find patterns and insights for you. The platform is powered by a Multi-Agent AI Architecture that processes data through multiple specialized agents - from initial analysis and orchestration to planning, execution via mini-agent swarms, and validation. This makes sophisticated data analysis accessible to anyone, regardless of their technical background. Whether you're a medical researcher analyzing patient data, a military strategist evaluating operations, a financial advisor tracking markets, or a small business owner understanding operations, VisionZ+ eliminates the need for a dedicated data science team - just ask questions and get insights. ### How we built it VisionZ+ is powered by a sophisticated Multi-Agent AI Architecture leveraging OpenAI's API and Terra's API, enhanced with our custom chain-of-thought processing and specialized agents. At its core, we developed a hierarchical orchestration system where each agent serves a distinct purpose: Our Analysis Agent processes natural language queries and translates them into structured task specifications The Orchestrator Agent coordinates the workflow The Planning Engine decomposes complex analytical tasks into optimized sub-tasks for parallel processing The real innovation lies in our Multi-Agent Architecture - specialized mini-agents that work in parallel, each focused on specific aspects like data processing, visualization, pattern recognition, and statistical analysis. These agents communicate through a custom-built coordination layer, ensuring efficient task distribution and resource optimization. Every output passes through our Validator Agent, which performs rigorous quality assurance and cross-validation before delivering results. The frontend provides an intuitive drag-and-drop interface, while our robust backend handles the complex orchestration of these AI agents. This architecture enables us to process complex analytical tasks with high accuracy and computational efficiency through systematic decomposition and parallel processing. ### Challenges we ran into One of our biggest hurdles was working with the Terra API integration. The authentication flow required setting up our own SSH tunnel to generate and handle authorization links, which proved to be quite tricky. We spent considerable time troubleshooting the tunnel configuration and ensuring secure, reliable connections for our users' wearable data. The process taught us a lot about handling OAuth flows and secure data transmission in production environments. The implementation of our Multi-Agent Architecture presented its own set of complex challenges, particularly around processing time. We discovered that different types of data analysis required varying levels of computational resources and model capabilities. This led us to develop a dynamic approach where we utilize different models based on the specific analysis needs - from lightweight models for quick statistical analysis to more sophisticated reasoning model for complex pattern recognition. Integrating our dynamic frontend with the backend focused on formatting and passing payload history for wearable data into MongoDB. Managing state on wearable.js involved designing a payload structure that supports seamless, real-time data transfer. The main challenge was reliably passing the complete payload history while maintaining data consistency. ### What's next We're pursuing an ambitious vision: creating the world's first truly autonomous data scientist. Beyond just analyzing data, we're developing VisionZ+ to proactively identify opportunities, generate hypotheses, and conduct independent research - much like a human data scientist would. Our goal is to enable VisionZ+ to not just answer questions, but to ask the right questions and uncover insights that users might not even know to look for. Looking ahead, we're enhancing our Multi-Agent Architecture with more sophisticated reasoning capabilities and expanding into industry-specific solutions. We envision VisionZ+ becoming an indispensable partner in data-driven decision making across all sectors - from healthcare and scientific research to business strategy and public policy. The future of data science is autonomous, and VisionZ+ is leading the way. ## README (from the GitHub repository) # VisionZโž• VisionZ+, Your data scientist. ![VisionZ+ Logo](./images/visionz+.png "VisionZ+ Logo") First of its kind data visualization and analysis platform powered by our groundbreaking Multi-Agent AI Architecture. Analyze **any** dataset with just a drag and drop. Stanford TreeHacks 2025. ### Team: Youwei Zhen (Brown University), Taorui Huang (Stanford), Anni Zimina (Stanford), Nick Adesina (UC Berkeley)

Team photo! Team photo!

## What is VisionZ+ VisionZ+ is a first-of-its-kind data visualization and analysis platform that lets users analyze any dataset through a simple drag-and-drop interface. With our dataset assistant, you can simply ask questions about your data and let the AI find patterns and insights for you. The platform is powered by a Multi-Agent AI Architecture that processes data through multiple specialized agents - from initial analysis and orchestration to planning, execution via mini-agent swarms, and validation. This makes sophisticated data analysis accessible to anyone, regardless of their technical background. Whether you're a medical researcher analyzing patient data, a military strategist evaluating operations, a financial advisor tracking markets, or a small business owner understanding operations, VisionZ+ eliminates the need for a dedicated data science team - just ask questions and get insights. ## Revolutionary Multi-Agent AI Architecture VisionZ+ employs a sophisticated Multi-Agent AI system that processes your requests through a highly orchestrated pipeline: ![VisionZ+ Multi-Agent Architecture](./images/architecture.png "Architecture Diagram") 1. **Analysis Agent** ๐Ÿ” - First point of contact for all user requests - Performs initial request analysis and requirement extraction - Translates natural language into structured task specifications 2. **Orchestrator Agent** ๐ŸŽฎ - Central command center of the system - Coordinates complex workflows between agents - Manages resource allocation and task prioritization 3. **Planning Engine** ๐Ÿ“‹ - Breaks down complex tasks into smaller, manageable sub-tasks - Develops execution strategies for optimal results - Creates dynamic task graphs for parallel processing 4. **Mini-Agents Swarm** ๐Ÿ - Specialized micro-agents working in parallel - Each agent focuses on a specific sub-task: - Data Processing Agents - Visualization Agents - Pattern Recognition Agents - Statistical Analysis Agents - Dynamic scaling based on task complexity 5. **Validator Agent** โœ… - Quality assurance and result verification - Ensures consistency and accuracy of outputs - Performs cross-validation of results - Optimizes final output for user consumption This advanced pipeline delivers: - Highly accurate and validated results - Parallel processing for faster insights - Scalable architecture for complex tasks - Robust error handling and quality control - Real-time adaptive processing ## Inspiration We believe everyone deserves their own personal data scientist. In today's data-driven world, the ability to extract meaningful insights from data shouldn't be limited to those with technical expertise or resources to hire analysts. Whether you're a medical researcher analyzing patient outcomes, a military strategist evaluating field operations, or a financial advisor tracking market trends, understanding your data is crucial for making informed decisions. This vision drove us to create VisionZ+. In healthcare, it can help doctors identify patterns in patient vitals and treatment responses. Military personnel can use it to analyze sensor data from equipment and personnel, enhancing operational efficiency and safety. Financial institutions can leverage it to detect market patterns and assess risk factors. Even small business owners can gain valuable insights about their operations without needing a dedicated data science team. By combining cutting-edge AI with an intuitive interface, we're making sophisticated data analysis accessible to everyone, regardless of their technical background or industry. ## Setup Instructions ### Backend Setup 1. Navigate to the backend directory: ```bash cd backend ``` 2. Install dependencies: ```bash npm install ``` 3. Set up environment variables: - Edit `.env` and add your OpenAI API key: ``` PORT = Server port (default: 5000) MONGODB_URI = MongoDB connection string OPENAI_API_KEY = Your OpenAI API key (required) NODE_ENV = Node environment (development/production) ``` - Update other environment variables as needed 4. Start the backend server: ```bash npm start ``` ### Frontend Setup 1. Navigate to the frontend directory: ```bash cd frontend ``` 2. Install dependencies: ```bash npm install ``` 3. Start the development server: ```bash npm run dev ``` ## Environment Variables ### Backend (.env) - `PORT`: Server port (default: 5000) - `MONGODB_URI`: MongoDB connection string - `OPENAI_API_KEY`: Your OpenAI API key (required) - `NODE_ENV`: Node environment (development/production) ## API Endpoints ### AI Generation - `POST /api/ai/generate` - Generates AI-powered visualizations and analysis - Requires valid OpenAI API key - Request body: ```json { "prompt": "Your analysis prompt here" } ``` ## Error Handling The API includes comprehensive error handling for: - Missing or invalid OpenAI API key - API quota exceeded - Invalid requests - Server errors ## Detected evidence (automated analysis) Indexed codebase: 30 recognized source files, 570 KB. - CSS (language) โ€” detected in the code - Express (technology) โ€” detected in the code - JavaScript (language) โ€” detected in the code - MongoDB (technology) โ€” detected in the code - Next.js (technology) โ€” detected in the code - OpenAI (technology) โ€” detected in the code - React (technology) โ€” detected in the code - Tailwind CSS (technology) โ€” detected in the code - HTML (language) โ€” claimed on Devpost, not found in the code ## Codebase structure (from repository index) ### Files (44 of 44) ``` .gitignore backend/.env.example backend/.gitignore backend/package.json backend/src/models/AIInteraction.js backend/src/models/TerraData.js backend/src/models/UserWearable.js backend/src/routes/ai.js backend/src/routes/wearable.js backend/src/scripts/exportMongoData.js backend/src/server.js exports/heart_rate_data.json exports/hypnogram_samples.json exports/mongodb_export.json exports/students.json frontend/.gitignore frontend/app/globals.css frontend/app/layout.js frontend/app/page.js frontend/components/feature-item.js frontend/components/header.js frontend/components/Navbar.js frontend/components/ui/button.js frontend/components/ui/grid-illustration.js frontend/components/ui/mobile-menu.js frontend/components/ui/navigation-menu.js frontend/components/ui/pulse-animation.js frontend/eslint.config.mjs frontend/jsconfig.json frontend/next.config.js frontend/package.json frontend/pages/_app.js frontend/pages/analyze.js frontend/pages/api/analyze.js frontend/pages/result.js frontend/pages/wearable.js frontend/postcss.config.js frontend/postcss.config.mjs frontend/public/chart.js frontend/public/d3.js frontend/README.md frontend/tailwind.config.js frontend/tailwind.config.mjs README.md ``` ### Dependencies - backend/package.json: cors@^2.8.5, dotenv@^16.4.7, express@^4.18.2, express-session@^1.18.1, mongoose@^8.0.0, nodemon@^3.0.1, openai@^4.85.1, terra-api@^1.4.8 - frontend/package.json: @eslint/eslintrc@^3, @tailwindcss/typography@^0.5.16, autoprefixer@^10.4.20, eslint@^9, eslint-config-next@15.1.7, framer-motion@^12.4.3, next@15.1.7, openai@^4.85.1, postcss@^8, react@^19.0.0, react-dom@^19.0.0, react-dropzone@^14.3.5, react-markdown@^9.0.3, tailwindcss@^3.4.1 ### Recent commits (newest first) - root cleaned - team photo! - updated description - team photo! - adjusting size - team photo! - readme updated - readme - readme - readme - Merge pull request #7 from AntoDono/taoruih - working historical data fetch - Merge branch 'main' into taoruih - change to relative path + download wearable data - Merge pull request #6 from AntoDono/final - done - Merge pull request #5 from AntoDono/final - perfect working version - code not running - currently fixed ## Key source files (fetched from GitHub, selected and truncated for size) ### backend/package.json ``` { "name": "visionzplus-backend", "version": "1.0.0", "description": "Backend server for VisionZPlus", "main": "src/server.js", "type": "module", "scripts": { "start": "node src/server.js", "dev": "nodemon src/server.js" }, "dependencies": { "cors": "^2.8.5", "dotenv": "^16.4.7", "express": "^4.18.2", "express-session": "^1.18.1", "mongoose": "^8.0.0", "openai": "^4.85.1", "terra-api": "^1.4.8" }, "devDependencies": { "nodemon": "^3.0.1" } } ``` ### frontend/package.json ``` { "name": "frontend", "version": "0.1.0", "private": true, "scripts": { "dev": "next dev --turbopack", "build": "next build", "start": "next start", "lint": "next lint" }, "dependencies": { "autoprefixer": "^10.4.20", "framer-motion": "^12.4.3", "next": "15.1.7", "openai": "^4.85.1", "react": "^19.0.0", "react-dom": "^19.0.0", "react-dropzone": "^14.3.5", "react-markdown": "^9.0.3" }, "devDependencies": { "@eslint/eslintrc": "^3", "@tailwindcss/typography": "^0.5.16", "eslint": "^9", "eslint-config-next": "15.1.7", "postcss": "^8", "tailwindcss": "^3.4.1" } } ``` ### frontend/app/layout.js ```javascript import './globals.css'; import { Inter } from 'next/font/google'; import Header from '../components/header'; const inter = Inter({ subsets: ['latin'] }); export const metadata = { title: 'VisionZ+', description: 'Transform your health data into actionable insights', }; export default function RootLayout({ children }) { return (
{children}
); } ``` ### backend/src/server.js ```javascript import express from 'express'; import mongoose from 'mongoose'; import cors from 'cors'; import dotenv from 'dotenv'; import session from 'express-session'; import { fileURLToPath } from 'url'; import { dirname, join } from 'path'; import aiRoutes from './routes/ai.js'; import wearableRoutes from './routes/wearable.js'; dotenv.config(); const app = express(); const PORT = process.env.PORT || 5000; // Middleware app.use(cors()); app.use(express.json()); // Serve static files from public directory app.use(express.static('public')); app.use(session({ secret: process.env.SESSION_SECRET || 'your-secret-key', resave: false, saveUninitialized: false, cookie: { secure: process.env.NODE_ENV === 'production' } })); // MongoDB Connection const connectDB = async () => { try { await mongoose.connect(process.env.MONGODB_URI); console.log('MongoDB connected successfully'); } catch (error) { console.error('MongoDB connection error:', error); process.exit(1); } }; // Routes app.get('/api/health', (req, res) => { res.json({ status: 'ok', message: 'Server is running' }); }); // AI Routes app.use('/api/ai', aiRoutes); // Wearable Routes app.use('/api/wearable', wearableRoutes); // Start server console.log("Connecting to MongoDB") connectDB().then(() => { app.listen(PORT, () => { console.log(`Server is running on port ${PORT}`); }); }); ``` ### frontend/app/page.js ```javascript 'use client'; import Link from 'next/link'; import { motion } from 'framer-motion'; import GridIllustration from '../components/ui/grid-illustration'; import FeatureItem from '../components/feature-item'; import PulseAnimation from '../components/ui/pulse-animation'; export default function Home() { return (
{/* Left side: Text content */}

Transform Your Health
Data Into
Actionable Insights

Connect your wearable devices and unlock powerful analytics to understand your health patterns and make informed decisions about your well-being.

Connect Device View Analytics
{/* Right side: Feature items */}
} title="Real-Time Sync" description="Seamlessly connect and sync data from your wearable devices for instant access to your health metrics." delay={0.2} /> } title="AI Analytics" description="Leverage AI-powered analytics to uncover patterns and trends in your health data." delay={0.4} /> } title="Secure & Private" description="Your health data is encrypted and protected with enterprise-grade security measures." delay={0.6} />
); } ``` ### frontend/postcss.config.js ```javascript module.exports = { plugins: { tailwindcss: {}, autoprefixer: {}, }, } ``` ### frontend/tailwind.config.js ```javascript /** @type {import('tailwindcss').Config} */ module.exports = { content: [ './pages/**/*.{js,ts,jsx,tsx,mdx}', './components/**/*.{js,ts,jsx,tsx,mdx}', './app/**/*.{js,ts,jsx,tsx,mdx}', ], theme: { extend: { colors: { 'visionz': { purple: '#4A1D96', 'purple-light': '#5925B0', 'purple-dark': '#3B1678', }, }, }, }, plugins: [], } ``` ### frontend/next.config.js ```javascript /** @type {import('next').NextConfig} */ const nextConfig = { reactStrictMode: true, publicRuntimeConfig: { // Will be available on both server and client OPENAI_API_KEY: process.env.OPENAI_API_KEY, }, serverRuntimeConfig: { // Will only be available on the server side OPENAI_API_KEY: process.env.OPENAI_API_KEY, }, async rewrites() { return [ { source: '/api/:path*', destination: 'http://localhost:5001/api/:path*' } ]; }, } module.exports = nextConfig ``` ### frontend/pages/_app.js ```javascript import '../app/globals.css'; import { Inter } from 'next/font/google'; import Header from '../components/header'; const inter = Inter({ subsets: ['latin'] }); export const metadata = { title: 'VisionZ+', description: 'Transform your health data into actionable insights', }; export default function App({ Component, pageProps }) { return (
{new Date().getFullYear()} VisionZ+. All rights reserved.
); } ``` ### frontend/components/Navbar.js ```javascript 'use client'; import Image from 'next/image'; import Link from 'next/link'; import { motion } from 'framer-motion'; export default function Navbar() { return ( ); } ``` [18 more indexed source files omitted to keep this export small. The full file list is in the Codebase structure section above.]