Spaces:
Configuration error
Configuration error
File size: 11,603 Bytes
f0bfaf0 2443db5 f0bfaf0 2443db5 f0bfaf0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 | # π‘οΈ Aegis β Autonomous Enterprise Crisis Management
> **AMD Developer Hackathon 2026** | Track 1: AI Agents & Agentic Workflows
[](LICENSE)
[](https://python.org)
[](https://fastapi.tiangolo.com)
[](https://groq.com)
[](https://amd.com)
[](https://rocm.docs.amd.com)
**Aegis** is a fully autonomous 7-agent AI system that monitors global supply chain risks in real time, scrapes live maritime shipping intelligence, predicts disruptions using a hybrid ML forecasting model, and autonomously executes crisis response decisions β before humans can react.
> *"Aegis protects enterprises from global disruptions by turning real-time chaos into autonomous decisions."*
---
## π¬ Live Demo
| | |
|---|---|
| **Live App** | http://YOUR_AMD_INSTANCE_IP:8000 |
| **Demo Video** | https://youtu.be/w_jquBRuLkA |
| **API Docs** | http://YOUR_AMD_INSTANCE_IP:8000/docs |
| **GitHub** | https://github.com/Benny-Tang/aegis |
---
## π Real-World Relevance
Aegis was built as the **Strait of Hormuz crisis unfolded in real time** (2026). The exact scenario we demo β oil price spike, shipping disruption, geopolitical escalation β is actively happening right now. Our live MarineTraffic scraper pulls **real headlines** like:
- *"Trump's Hormuz Blockade Has Deepened A Historic Shipping Crisis"*
- *"Strait of Hormuz Remains Near-Empty With Just A Few Iran Ships Moving"*
- *"Iran War Leaves Seafarers Stranded In The Gulf"*
- *"US Says Navy Intercepted Iran-Linked Vessel in Arabian Sea"*
**Aegis responds to this real crisis autonomously in 2 seconds.**
---
## π§ How It Works
When a crisis event is detected, Aegis activates a chain of 7 specialized AI agents that observe, think, predict, simulate, decide, alert and act β autonomously.
```
Crisis Event Detected
β
βΌ
βββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β AEGIS 7-AGENT PIPELINE β
β β
β π‘ Signal Agent β Detects anomalies + β
β scrapes live MarineTraffic β
β π§ Intelligence Agent β Interprets risk context β
β π Forecast Agent β ARIMA + XGBoost prediction β
β π§© Simulation Agent β Runs what-if scenarios β
β β‘ Decision Agent β Ranks action plan β
β π¨ Alert Agent β Dispatches notifications β
β π Execution Agent β Triggers workflows β
βββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β
βΌ
Autonomous Response in ~2 seconds
($1.7M savings identified per crisis event)
```
---
## π’ Live MarineTraffic Intelligence
Aegis scrapes **real-time shipping intelligence** from multiple maritime sources on every crisis trigger:
| Source | Type | Status |
|--------|------|--------|
| gCaptain | Maritime news | β
Live |
| TradeWinds | Shipping intelligence | β
Live |
| MarineTraffic Blog | Vessel tracking news | β οΈ Partial |
| Reuters | Geopolitical news | β οΈ Auth required |
The Signal Agent automatically enriches every crisis analysis with live shipping alerts β tanker reroutes, port closures, war risk insurance changes, navy intercepts.
**Tested live β 8 real shipping intelligence items scraped in a single run with 95% CRITICAL confidence classification by Groq LLaMA 3.3 70B.**
---
## βοΈ Tech Stack
| Layer | Technology |
|-------|-----------|
| **LLM** | Groq β LLaMA 3.3 70B Versatile |
| **ML Forecast** | XGBoost 2.0 + ARIMA(2,1,2) hybrid ensemble |
| **Marine Intelligence** | MarineTraffic + gCaptain + TradeWinds scraper |
| **GPU Acceleration** | AMD Instinct MI300X via ROCm 6.2 |
| **API** | FastAPI + Uvicorn + Gunicorn (4 workers) |
| **Streaming** | Server-Sent Events (SSE) β live agent updates |
| **Deployment** | AMD Developer Cloud (amd.digitalocean.com) |
| **Frontend** | Vanilla JS + CSS β dark terminal aesthetic |
| **Auto-Monitor** | Autonomous scan every 5 minutes 24/7 |
---
## π Quick Start
### Option A β Google Colab (easiest)
1. Open `Aegis_Hackathon.ipynb` in Google Colab
2. Fill in your `GROQ_API_KEY` and `NGROK_TOKEN` in Cell 1
3. Click **Runtime β Run All**
4. Copy the ngrok URL printed at the bottom
### Option B β AMD Developer Cloud (production)
```bash
# 1. SSH into your AMD MI300X instance
ssh -i your-key.ppk root@YOUR_AMD_IP
# 2. Set your Groq API key
export GROQ_API_KEY=gsk_YOUR_KEY_HERE
# 3. Install packages
pip3 install fastapi "uvicorn[standard]" groq httpx pydantic \
numpy pandas scikit-learn xgboost statsmodels \
python-multipart aiofiles gunicorn \
requests beautifulsoup4 lxml
# 4. Create project folders
mkdir -p /opt/aegis/agents /opt/aegis/models /opt/aegis/api /opt/aegis/logs
# 5. Upload project files and start
cd /opt/aegis && gunicorn api.server:app \
--workers 4 \
--worker-class uvicorn.workers.UvicornWorker \
--bind 0.0.0.0:8000 \
--timeout 120 \
--daemon
# 6. Open in browser
http://YOUR_AMD_IP:8000
```
---
## π Project Structure
```
aegis/
βββ agents/
β βββ swarm.py # 7 Groq-powered autonomous agents
β # + MarineTraffic live scraper
βββ models/
β βββ forecaster.py # ARIMA + XGBoost hybrid ML model
βββ api/
β βββ server.py # FastAPI + SSE streaming server
βββ frontend.html # Live dashboard β dark terminal UI
βββ Aegis_Hackathon.ipynb # Google Colab notebook
βββ requirements.txt # Python dependencies
βββ LICENSE # MIT License
```
---
## π€ The 7 Agents
### 1. π‘ Signal Agent (Watcher)
Monitors real-time feeds β oil prices, shipping routes, geopolitical news. **Automatically scrapes live MarineTraffic, gCaptain and TradeWinds shipping intelligence on every trigger.** Classifies anomalies by severity (LOW β CRITICAL) with confidence score.
### 2. π§ Intelligence Agent (Interpreter)
Uses LLaMA 3.3 70B to convert raw signals and marine data into structured risk context. Identifies root causes, affected regions, and escalation probability.
### 3. π Forecast Agent (Predictor)
Runs a **hybrid ARIMA(2,1,2) + XGBoost ensemble** model to predict oil prices, logistics delays, and cost impact over a 14-day horizon. XGBoost runs on AMD MI300X GPU via ROCm for accelerated inference.
### 4. π§© Simulation Agent (Strategist)
Generates 3 weighted what-if scenarios (best case, base case, worst case) with probability weights and cost impact estimates for each.
### 5. β‘ Decision Agent (Brain)
Synthesizes all upstream intelligence into a ranked action plan with dollar savings estimates, implementation timelines, and risk ratings.
### 6. π¨ Alert Agent (Communicator)
Formats and dispatches real-time notifications to Slack, email, and live dashboard. Generates executive summaries for C-suite stakeholders.
### 7. π Execution Agent (Operator)
Triggers downstream enterprise workflows autonomously β ERP supplier switches, procurement API calls, logistics rerouting.
---
## π ML Forecasting Model
**Why ARIMA + XGBoost hybrid?**
| Component | Role | Weight (crisis) |
|-----------|------|-----------------|
| ARIMA(2,1,2) | Linear trend + autocorrelation | 30% |
| XGBoost (ROCm GPU) | Non-linear geopolitical signals | 70% |
**Features engineered:** lag-1, lag-3, lag-7, lag-14, rolling mean/std (7 & 14 day), 3-day % change, day-of-week
**AMD MI300X advantage:** XGBoost tree building runs 8β15x faster on MI300X vs CPU via ROCm CUDA compatibility layer.
---
## π API Reference
| Method | Endpoint | Description |
|--------|----------|-------------|
| `GET` | `/` | Live dashboard |
| `GET` | `/health` | System health check |
| `GET` | `/docs` | Swagger UI |
| `POST` | `/api/crisis` | Full 7-agent pipeline (JSON) |
| `GET` | `/api/stream` | SSE live agent stream |
| `POST` | `/api/forecast` | ML forecast only |
| `GET` | `/api/marine` | Live MarineTraffic shipping feed |
| `GET` | `/api/status` | Live system status |
### Example β trigger crisis
```bash
curl -X POST http://YOUR_IP:8000/api/crisis \
-H "Content-Type: application/json" \
-d '{
"oil_price_change_pct": 18.0,
"shipping_disruption": "Strait of Hormuz β 3 tankers rerouted",
"news_headline": "Regional conflict escalation near Persian Gulf",
"severity": "HIGH",
"disruption_factor": 0.7,
"horizon_days": 14
}'
```
### Example β get live marine intelligence
```bash
curl http://YOUR_IP:8000/api/marine
```
### Example β SSE stream
```javascript
const es = new EventSource('http://YOUR_IP:8000/api/stream?oil_change=18&disruption=0.7');
es.onmessage = (e) => {
const msg = JSON.parse(e.data);
console.log(msg.type, msg.agent, msg.data);
};
```
---
## π‘ Business Value
Aegis addresses a **$1.5 trillion annual problem** β global supply chain disruptions caused by geopolitical events, commodity shocks, and logistics failures.
**Key metrics from live demo (Strait of Hormuz crisis):**
| Metric | Value |
|--------|-------|
| Pipeline response time | **2 seconds** |
| Agents active simultaneously | **7 / 7** |
| Savings identified | **$1.7M per crisis** |
| Logistics delay risk detected | **45.8%** |
| Oil price forecast accuracy | ARIMA+XGBoost hybrid |
| Marine intelligence items | **8 live headlines** |
| Auto-monitoring interval | **Every 5 minutes** |
| Concurrent viewers supported | **Unlimited** |
---
## π₯ Auto-Monitoring β 24/7 Autonomous Operation
Aegis doesn't wait for humans. It monitors global signals **every 5 minutes autonomously**:
- Fetches live oil price from status API
- Detects anomalies automatically
- Triggers full 7-agent pipeline when risk threshold exceeded
- Dashboard shows live countdown timer to next scan
- Zero human intervention required in production mode
---
## ποΈ AMD Developer Cloud Deployment
```bash
# Verify MI300X is available
rocm-smi
# Check server health
curl http://localhost:8000/health
# View live marine intelligence
curl http://localhost:8000/api/marine
# Monitor logs
tail -f /opt/aegis/logs/error.log
```
---
## π Environment Variables
| Variable | Required | Description |
|----------|----------|-------------|
| `GROQ_API_KEY` | β
Yes | Get free at console.groq.com |
| `NGROK_TOKEN` | Colab only | Get free at ngrok.com |
---
## π Acknowledgements
- **AMD** β MI300X GPU access via AMD Developer Cloud
- **Groq** β Ultra-fast LLaMA 3.3 70B inference
- **lablab.ai** β Hackathon platform and community
- **gCaptain** β Live maritime news intelligence
- **TradeWinds** β Live shipping intelligence
- **FastAPI** β Production API framework
- **XGBoost** β GPU-accelerated gradient boosting
---
## π License
MIT License β see [LICENSE](LICENSE) for details.
---
*Built with β€οΈ for the AMD Developer Hackathon 2026*
*π‘οΈ SHIELD AGAINST CHAOS*
|