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f492127 d6dde3f f492127 d6dde3f f492127 d6dde3f f492127 d6dde3f f492127 d6dde3f f492127 d6dde3f f492127 d6dde3f f492127 d6dde3f | 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 | # HaramGuard — Agentic AI Crowd Management System
## 📁 Folder Structure
```
HaramGuard/
│
├── config.py ← All thresholds & API keys (edit here only)
├── pipeline.py ← RealTimePipeline — orchestrates all agents
├── dashboard.py ← Streamlit dashboard entry point
├── requirements.txt
│
├── core/ ← Shared infrastructure (no agent logic here)
│ ├── __init__.py
│ ├── models.py ← FrameResult, RiskResult, Decision dataclasses
│ └── database.py ← HajjFlowDB — SQLite (4 tables)
│
├── agents/ ← One file per agent
│ ├── __init__.py
│ ├── perception_agent.py ← YOLO tracking + Guardrails GR1/GR2
│ ├── risk_agent.py ← Clip segmentation + sliding K-window density scoring
│ ├── reflection_agent.py ← Self-critique design pattern (Bias detection)
│ ├── operations_agent.py ← Event-driven playbook + rate-limit guardrail
│ └── coordinator_agent.py ← openai/gpt-oss-120b + output validation guardrails GR-C1..5
│
└── outputs/ ← Auto-created at runtime
├── hajjflow_rt.db ← Main SQLite database
└── plots/ ← Saved charts
```
## 🤖 Agent Pipeline
```
Frame
│
▼
PerceptionAgent → FrameResult (YOLO detect + track)
│
▼
RiskAgent → RiskResult (clip segmentation + K-window density score)
│
▼
ReflectionAgent → reflection{} (bias check + correction)
│
▼
OperationsAgent → Decision (event-driven, P0/P1/P2)
│
▼
CoordinatorAgent → plan{} (openai/gpt-oss-120b action plan, all priorities)
│
▼
pipeline.state → Dashboard
```
## 🛡️ Guardrails
| ID | Agent | Description |
|----|-------|-------------|
| GR1 | PerceptionAgent | Person count capped at 500 |
| GR2 | PerceptionAgent | Density score capped at 50 |
| GR3 | RiskAgent | Risk score clamped to [0, 1] (density_pct / 100) |
| GR3b | RiskAgent | level_changed suppressed during K-window warmup (first 17 frames per clip) |
| GR4 | OperationsAgent | P0 rate-limited (1 per 5 min per zone); resets on pipeline restart |
| GR-C1..5 | CoordinatorAgent | LLM output validation (fields, threat_level, confidence, score-level consistency, Arabic fallback) |
| RF1..3 | ReflectionAgent | Chronic LOW bias, rising trend ignored, count-risk mismatch |
## 📊 Run Evaluation
```bash
python evaluation.py
```
Outputs:
- `outputs/plots/eval_perception.png` — PerceptionAgent charts
- `outputs/plots/eval_risk.png` — RiskAgent score trajectories
- `outputs/plots/eval_e2e.png` — End-to-end accuracy + throughput
- `outputs/eval/summary.json` — Final metrics summary
Rubric coverage:
- ✅ End-to-end performance metrics (Section 5)
- ✅ Component-level evaluation (Sections 1–4)
- ✅ Error analysis methodology (Section 6)
- ✅ Evidence of iterative improvement (Section 7)
## 🚀 Run Backend API
```bash
pip install -r requirements.txt
python api.py
``` |