EIRION — Precision Longevity Platform

🧬 DNA · 💊 Lifestyle · 🩺 Medications → Personalized Organ Health Forecasts

ETGen AI Hackathon 2026 · Phase 0 Prototype · Team Ajay Bharathi

Quick Start · Architecture · GNN Model · Dashboard · API Reference · Roadmap

📋 Table of Contents

What is EIRION?
Platform Demo
Platform Architecture
Full Analysis Process
GNN Model: Heterogeneous Graph Attention Network
Dashboard Features
Tech Stack
Project Structure
Quick Start
Environment Variables
Demo Flow
API Reference
Data Sources & Datasets
Roadmap
Known Limitations
References
License

🧬 What is EIRION?

EIRION is the first pharmacogenomics-powered full-stack longevity platform. It combines a Heterogeneous Graph Attention Network (GATv2) for drug–gene–organ interaction modelling with a clinical AI engine to predict, visualize, and optimize individual organ health across a 5–10 year horizon.

A user provides three input layers:

Input Layer	What You Provide	What EIRION Does
🧬 Biology	23andMe / raw SNP upload	CYP450 metabolizer phenotype inference (CYP2D6, CYP2C19, CYP3A4, CYP2C9, CYP1A2, SLCO1B1, MTHFR)
🏃 Lifestyle	Sleep, stress, alcohol, diet, exercise, substance use	Quantifies organ stress from lifestyle factors
💊 Regimen	Every supplement & medication with dose and frequency	Drug-drug interaction analysis + cumulative organ load scoring

What EIRION Computes

🫀 Hepatic, renal, cardiovascular, and metabolic organ scores (0–100 scale)
⚠️ Drug-drug interaction (DDI) flags via GNN polypharmacy modelling
📈 5–10 year organ trajectory under current vs. optimised regimen
🧓 Biological age estimate and delta from chronological age
🤖 Gemini-enriched clinical recommendations grounded in CPIC/AHA/WHO guidelines
📄 Downloadable 9-page clinical PDF report structured for clinician handout

🎬 Platform Demo

https://github.com/user-attachments/assets/eirion_platform_demo.mp4

Full end-to-end demonstration: Registration → 9-step wizard → Analysis → Dashboard → PDF Export

🎯 Try the demo persona: Click "Try Priya Demo" on the landing page to auto-fill a canonical test case:

Priya, 35F, CYP2D6 poor metabolizer, Ashwagandha + Atorvastatin + SLCO1B1 reduced function → statin myopathy risk flagged, 15% liver efficiency drop predicted by age 40.

🏗 Platform Architecture

End-to-end architecture: Experience Layer → Application Layer → Intelligence Layer → Data Layer

The platform is composed of five architectural layers:

Experience Layer (Frontend)

The React 19 + TypeScript SPA provides:

Onboarding Wizard — guided 9-step patient data collection
Health Risk Dashboard — multi-organ scorecard with interactive visualizations
Recommendation Feed — AI-enriched clinical advice
Organ Trajectory View — 5-year multi-timeframe projection charts
Data Connection Hub — 23andMe upload, lab OCR, wearable sync

Application Layer (API Gateway)

FastAPI 0.135 serves as the API gateway with:

RESTful endpoints for all platform features
JWT authentication with bcrypt password hashing
Notification service for asynchronous alerts
Data ingestion service for genetic file parsing and lab OCR

Intelligence Layer (ML/AI Engine)

The core inference pipeline:

GATv2 GNN (1.29M parameters) — compound toxicity prediction across 13 Tox21 assays
CYP450 scoring engine — deterministic pharmacokinetic rules with genetic multipliers
Gemini 2.5 Flash — patient-contextualised clinical recommendation generation
Multi-organ trajectory projector — 5-year organ health forecasting

Data Layer

SQLite via Prisma — user accounts, attributes, trajectory history
PharmGKB / CPIC — curated drug-gene relationships
Tox21 / Reactome — bioassay labels and biological pathway data
RDKit — molecular structure processing (SMILES → Morgan fingerprints)

🔄 Full Analysis Process

Complete data pipeline: Data sources → Graph construction → Training → Inference → EIRION GNN v1.0.0

End-to-End Pipeline

User Input (9-Step Wizard)
       │
       ├─ Step 1: Demographics (age, sex, weight, height, ethnicity)
       ├─ Step 2: Pre-existing conditions (diabetes, CKD, hypertension, etc.)
       ├─ Step 3: 23andMe SNP upload → SNP→PGx map → CYP phenotype inference
       ├─ Step 4: Manual genetic entry (CYP2D6, CYP2C19, CYP3A4, etc.)
       ├─ Step 5: Lab panel — upload image (Gemini Vision OCR) or manual entry
       │          └── LFT (ALT, AST, GGT, ALP, Albumin)
       │          └── KFT (eGFR, Creatinine, BUN)
       │          └── Metabolic (HbA1c, Fasting Glucose)
       │          └── Cardiac (LDL, HDL, Triglycerides, hsCRP)
       ├─ Step 6: Lifestyle — sleep, stress, alcohol, exercise, smoking, toxins
       ├─ Step 7: Diet — calories, macros, fiber, processed food %, diet type
       ├─ Step 8: Regimen — every supplement & medication with dose + frequency
       └─ Step 9: Confirmation & submission
              │
              ▼
    AnalysisRequest (Pydantic v2 — fully validated)
              │
    ┌─────────┴───────────────────────────────────────┐
    │              Backend Engine                       │
    │                                                   │
    │  1. CYP450 Load Scorer (scorer.py — 887 lines)   │
    │     Compound × metabolizer multiplier × dose      │
    │     → per-compound hepatic load (0–100 scale)     │
    │     → lifestyle penalties (sleep, stress, sugar)   │
    │     → food penalties (processed food, fiber, etc.) │
    │                                                   │
    │  2. GATv2 GNN (EirionGNN — 1.29M params)         │
    │     SMILES → Morgan FP → GATv2 → 13 Tox21 probs  │
    │     Drug–Gene–Pathway message passing (2-hop)     │
    │     → DDI flags with mechanism explanations       │
    │     → organ-level toxicity impact scores          │
    │                                                   │
    │  3. Multi-Organ Knowledge Graph (28K lines)       │
    │     Condition penalties → organ loads              │
    │     Compound → gene → pathway chains              │
    │     CYP genetic multipliers per compound          │
    │                                                   │
    │  4. Multi-Organ Trajectory Projector              │
    │     Baseline + optimised 5-yr projection          │
    │     per organ (liver/kidney/CVD/metabolic)        │
    │     Recommendation delta simulation               │
    │                                                   │
    │  5. Gemini 2.5 Flash Enrichment                   │
    │     Full patient context prompt (CYP panel,       │
    │     organ scores, labs, regimen with DDI notes)   │
    │     → Mechanistically specific, personalized      │
    │       clinical recommendations with evidence      │
    │                                                   │
    │  6. PDF Generator (ReportLab — 47K)               │
    │     9-page A4 clinical handout                    │
    └───────────────────────────────────────────────────┘
              │
    AnalysisResponse (Pydantic v2)
              │
              ▼
    React Dashboard (14 widgets)
    ├── Multi-Organ Scorecard (liver, kidney, CVD, metabolic)
    ├── 5-Year Trajectory Chart (baseline vs optimised)
    ├── Multi-Timeframe Projection Chart
    ├── Biological Age Widget (with chronological delta)
    ├── Risk Summary Card
    ├── DDI Flags Panel (mechanism + recommendation)
    ├── Compound→Gene→Pathway Chain Panel
    ├── Compound Contribution Breakdown
    ├── Gemini Recommendation Feed
    ├── Circadian / Sleep Panel
    ├── Guideline Reference Panel (CPIC/AHA/WHO)
    ├── Progress Tracking Chart
    ├── Clinical PDF Export Button
    └── Compound Cart (current regimen)

🧠 GNN Model: Heterogeneous Graph Attention Network

EirionGNN: SMILES input → Morgan FP → Input projections → 2-layer GATv2 with residual connections → MLP head → 13 Tox21 toxicity predictions

The EirionGNN model (engine/gnn_model.py) is a two-stage architecture with 1.29M trainable parameters:

Stage 1 — HeteroGAT-v2

Learns contextualised node embeddings across a heterogeneous knowledge graph using 4 edge types:

Edge Type	Source → Target	Count	Data Source
`compound → targets → gene`	Compound → Gene	21,936 + fallback	PharmGKB via InChIKey bridge
`gene → targeted_by → compound`	Gene → Compound	21,936 (reversed)	PharmGKB
`gene → in_pathway → pathway`	Gene → Pathway	48,593	Reactome (Homo sapiens)
`pathway → contains → gene`	Pathway → Gene	48,593 (reversed)	Reactome

Knowledge Graph Statistics

Node Type	Count	Feature Dim	Feature Source
Compound	12,505	1024	Morgan fingerprint (radius=2, RDKit)
Gene	23,658	512	Truncated one-hot identity
Pathway	2,848	512	Truncated one-hot identity
Total edges	70,000+	—	PharmGKB + Reactome

GATv2 Attention Mechanism

The dynamic attention (Brody et al. 2021) computes per-edge-type attention coefficients:

e_vu = aᵀ · LeakyReLU(W_t · [h_u ‖ h_v])
α_vu = exp(e_vu) / Σ_{k∈N(v)} exp(e_vk)
h_v' = ‖_{k=1}^{K} σ( Σ_{u∈N(v)} α_vu^k · W^k · h_u )

Config: K=4 heads, hidden=128, 2 message-passing layers + residual connections + LayerNorm + Dropout(0.3)

Stage 2 — Multi-Label MLP Classifier

Predicts 13 Tox21 bioassay toxicity scores per compound:

h_compound → Linear(128→64) → ReLU → Dropout(0.3) → Linear(64→13) → 13 toxicity logits

Target Assays:

Category	Assays	Biological Relevance
Nuclear Receptor	NR-AhR, NR-AR, NR-AR-LBD, NR-Aromatase, NR-ER, NR-ER-LBD, NR-PPAR-γ	Endocrine disruption, hormone signalling, CYP induction
Stress Response	SR-ARE, SR-ATAD5, SR-HSE, SR-MMP, SR-p53	Oxidative stress, genotoxicity, mitochondrial toxicity, DNA damage
Composite	label_any	Overall toxicity flag

Performance — GNN vs. Random Forest Baseline

Metric	RF Baseline	EirionGNN	Delta
Macro AUC (13 assays)	0.7517	0.7628	+1.5%
Best single assay	NR-AhR 0.870	NR-AR-LBD 0.911	—
NR-AR improvement	—	—	+9.7%
NR-AR-LBD improvement	—	—	+13.3%
Parameters	~200 trees	1.29M	—
Training time (CPU)	~8 min	~40 min	—

📊 Full Per-Assay Results (click to expand)

Assay	GNN AUC	RF AUC	Delta
NR-AhR	0.8423	0.8696	-0.0273
NR-AR	0.7961	0.6987	+0.0974
NR-AR-LBD	0.9110	0.7782	+0.1328
NR-Aromatase	0.7506	0.7947	-0.0441
NR-ER	0.6845	0.6471	+0.0374
NR-ER-LBD	0.7629	0.7435	+0.0194
NR-PPAR-γ	0.6962	0.7519	-0.0557
SR-ARE	0.7074	0.7144	-0.0070
SR-ATAD5	0.7891	0.7471	+0.0420
SR-HSE	0.6542	0.6703	-0.0161
SR-MMP	0.8306	0.8522	-0.0216
SR-p53	0.7697	0.7643	+0.0054
label_any	0.7222	0.7402	-0.0180
Macro avg	0.7628	0.7517	+0.0111

Training Configuration

Parameter	Value
Optimizer	Adam (lr=1e-4, weight_decay=1e-4)
LR Scheduler	ReduceLROnPlateau (factor=0.5, patience=5)
Loss	BCEWithLogitsLoss (pos_weight=8.0 per task)
Gradient clipping	1.0 (L2 norm)
Batch size	Full graph (transductive)
Max epochs	300 (early stop at 240)
Best checkpoint	Epoch 40 (valid AUC 0.7594)

📖 Full GNN documentation: docs/GNN_README.md — 760 lines covering mathematical foundations, layer-by-layer description, InChIKey bridge construction, dataset details, and v2.0 development plans.

🖥 Dashboard Features

Feature	Description
🧙 9-Step Onboarding Wizard	Demographics → conditions → 23andMe upload → genetic manual entry → lab upload/entry → lifestyle → diet → regimen → confirm
🧬 SNP → PGx Inference	Parses 23andMe raw `.txt` files, maps rsIDs to star-alleles, infers CYP450 metabolizer phenotypes for 7 genes
🫀 Multi-Organ Scorecard	Liver, kidney, cardiovascular, and metabolic scores (0–100) with risk level colouring (green/amber/red)
📈 5-Year Trajectory	Baseline vs. optimised trajectory charts with organ-years-gained metric
📊 Multi-Timeframe Projection	Extended organ health projections across multiple timeframes
🧓 Biological Age	AI-estimated biological age with delta from chronological age
⚠️ DDI Flags	GNN-powered drug-drug interaction detection with mechanism explanation and clinical recommendation
🔬 Compound→Gene→Pathway Chains	Visual chain showing how each compound flows through genes to organ pathways
🤖 Gemini Recommendations	Rich, evidence-grounded AI recommendations personalized to CYP genotype, organ scores, and labs
🩻 Lab OCR Upload	Upload lab report images — Gemini Vision extracts structured lab values automatically
📄 Clinical PDF Export	9-page professional A4 clinical handout (ReportLab): demographics, labs, PGx, DDI, trajectory, recs, disclaimer
💬 AI Chat	Gemini-powered conversational AI for health questions in patient context
⚙️ Profile & Settings	Name/password update, notification preferences, data export (JSON), analysis reset
🔔 Notification Centre	In-app notification bell with unread badge and notification management
🛒 Compound Cart	Live view of current supplement/medication stack with per-compound organ impacts
💳 Pro Features	Paywall overlay for premium features (wearable sync, advanced projections)

⚡ Tech Stack

Layer	Technology	Version
Frontend Framework	React + TypeScript	19.2 + TS 5.9
Build Tool	Vite	8.0
Styling	Tailwind CSS	4.2
State Management	Zustand (persisted)	5.0
Data Fetching	Axios + React Query patterns	1.14
Charts	Recharts	3.8
Animations	Framer Motion	12.38
Icons	Lucide React	1.7
Routing	React Router DOM	7.13
Toasts	React Hot Toast	2.6

Backend Framework	FastAPI + Pydantic v2	0.135 + 2.12
ASGI Server	Uvicorn	0.42
Auth	PyJWT + bcrypt	2.12 + 5.0
Database ORM	Prisma Python (asyncio)	0.15
Database	SQLite	—

ML Framework	PyTorch	2.11
Graph ML	PyTorch Geometric	2.7
Cheminformatics	RDKit	2025.9
ML Utilities	scikit-learn + NumPy + SciPy	1.7 + 2.2 + 1.15

AI / LLM	Google Gemini (google-genai)	1.69
PDF Generation	ReportLab + Pillow	4.4 + 12.1
Scheduling	APScheduler	3.11
HTTP Client	HTTPX	0.28
Containerisation	Docker + Docker Compose	—

📁 Project Structure

eirion/
├── README.md                          ← this file
├── docker-compose.yml                 ← full-stack Docker orchestration
├── .env.example                       ← environment variable template
├── .gitignore                         ← git ignore rules
│
├── backend/                           # ═══ FastAPI API Server ═══
│   ├── main.py                        ← app bootstrap, lifespan, CORS, 11 routers
│   ├── schema.prisma                  ← SQLite schema (User, UserAttributes, TrajectoryHistory)
│   ├── database.py                    ← Prisma async connection manager
│   ├── auth.py                        ← JWT token creation/verification + bcrypt
│   ├── requirements.txt               ← pinned Python dependencies (44 packages)
│   ├── Dockerfile                     ← backend container image
│   ├── dev.db                         ← SQLite development database
│   │
│   ├── engine/                        # ─── The Brain ───
│   │   ├── __init__.py                ← engine package init
│   │   ├── scorer.py                  ← 🏆 PRIMARY: CYP-adjusted multi-organ scoring (887 lines)
│   │   ├── gnn_model.py               ← EirionGNN: GATv2 HeteroConv model class (1.29M params)
│   │   ├── inference.py               ← GNN predictor: SMILES → Morgan FP → 13 Tox21 probabilities
│   │   ├── knowledge.py               ← CYP load factors, DDI rules, compound registry tables
│   │   ├── knowledge_graph.py         ← Compound→gene→pathway knowledge graph (28K)
│   │   ├── graph_builder.py           ← PyTorch Geometric HeteroData graph builder
│   │   ├── projector.py               ← Multi-organ 5-year trajectory projector
│   │   ├── gemini_recs.py             ← Gemini 2.5 Flash recommendation enrichment
│   │   ├── pdf_generator.py           ← 9-page A4 ReportLab clinical report (47K)
│   │   │
│   │   ├── weights/                   # Trained model checkpoints
│   │   │   └── eirion_gnn_best.pt     ← GATv2 GNN checkpoint (2.17 MB, best valid AUC 0.7594)
│   │   │
│   │   └── planned/                   # Phase 2/3 modules (designed, not yet live)
│   │       ├── __init__.py            ← planned module index
│   │       ├── bio_age_tracker.py     ← advanced biological age tracking
│   │       ├── dmpnn_toxicity.py      ← D-MPNN molecular toxicity encoder
│   │       ├── gatv2_drug_gene_organ.py ← extended GATv2 drug→gene→organ model
│   │       ├── multi_organ_expansion.py ← expanded organ system support
│   │       ├── rl_regimen_optimizer.py  ← RL (SAC/PPO) regimen optimizer
│   │       ├── tft_forecasting.py     ← Temporal Fusion Transformer (10-yr forecast)
│   │       └── wearable_fhir_integration.py ← FHIR R4 + Oura/Apple Health sync
│   │
│   ├── models/                        # ─── Pydantic v2 Data Models ───
│   │   ├── __init__.py                ← model exports
│   │   ├── request.py                 ← AnalysisRequest + Patient, Genetics, Labs, Lifestyle, Regimen
│   │   └── response.py               ← AnalysisResponse + OrganScore, DDIFlag, Trajectory, Recommendation
│   │
│   ├── routers/                       # ─── API Endpoints (11 routers) ───
│   │   ├── __init__.py                ← router exports
│   │   ├── users.py                   ← register, login, GET/PATCH /me, change-password
│   │   ├── analysis.py                ← POST /analysis/run — main inference endpoint
│   │   ├── export.py                  ← GET /export/clinical-pdf — authenticated PDF download
│   │   ├── extraction.py              ← POST /extraction/parse-labs — Gemini Vision lab OCR
│   │   ├── notifications.py           ← GET/PATCH /notifications
│   │   ├── history.py                 ← trajectory history persistence
│   │   ├── chat.py                    ← conversational AI endpoint
│   │   ├── labs.py                    ← lab data management
│   │   ├── billing.py                 ← billing/subscription (Pro features)
│   │   ├── wearables.py               ← Oura Ring / wearable sync endpoints
│   │   └── health.py                  ← health check endpoint
│   │
│   ├── data/                          # ─── Static Reference Data ───
│   │   ├── snp_pgx_map.json          ← rsID → star-allele → metabolizer phenotype map (CPIC)
│   │   └── mock_response.json        ← fallback response for dev/demo mode
│   │
│   └── tests/                         # ─── Test Suite ───
│       ├── __init__.py
│       └── test_engine.py             ← scoring engine unit tests (17K)
│
├── frontend/                          # ═══ React 19 + TypeScript SPA ═══
│   ├── index.html                     ← HTML entrypoint
│   ├── package.json                   ← npm dependencies and scripts
│   ├── vite.config.ts                 ← Vite build configuration
│   ├── tsconfig.json                  ← TypeScript configuration
│   ├── eslint.config.js               ← ESLint rules
│   ├── Dockerfile                     ← frontend container image
│   │
│   └── src/
│       ├── main.tsx                   ← React DOM render entry
│       ├── App.tsx                    ← router, SmartHome redirect, protected routes, header
│       ├── App.css                    ← global application styles
│       ├── index.css                  ← base CSS reset and tokens
│       ├── types.ts                   ← full TypeScript type definitions (8K)
│       │
│       ├── pages/                     # ─── Page Components ───
│       │   ├── Landing.tsx            ← auth-aware landing page with hero + CTA
│       │   ├── Login.tsx              ← login form → JWT token
│       │   ├── Register.tsx           ← registration (email + name + password)
│       │   ├── Wizard.tsx             ← 9-step onboarding orchestrator
│       │   ├── Dashboard.tsx          ← full analysis dashboard (14 widgets)
│       │   ├── Demo.tsx               ← "Priya Demo" — pre-filled canonical test case (26K)
│       │   └── ProfileSettings.tsx    ← user profile, password, data export, reset
│       │
│       ├── components/
│       │   ├── wizard/                # ─── 13 Wizard Step Components ───
│       │   │   ├── Step1Demographics.tsx
│       │   │   ├── Step2Conditions.tsx
│       │   │   ├── Step2Genetics.tsx
│       │   │   ├── Step3GeneticUpload.tsx  ← 23andMe .txt file parser
│       │   │   ├── Step3Lifestyle.tsx
│       │   │   ├── Step3Diet.tsx
│       │   │   ├── Step4GeneticManual.tsx
│       │   │   ├── Step4Regimen.tsx
│       │   │   ├── Step5LabUpload.tsx     ← Gemini Vision OCR for lab reports
│       │   │   ├── Step5Labs.tsx
│       │   │   ├── Step6Lifestyle.tsx     ← deep lifestyle intake (15K)
│       │   │   ├── Step6Confirm.tsx
│       │   │   └── Step8Regimen.tsx       ← compound entry with search (13K)
│       │   │
│       │   ├── dashboard/             # ─── 14 Dashboard Widgets ───
│       │   │   ├── OrganScorecard.tsx          ← 4-organ score cards with risk colours
│       │   │   ├── TrajectoryChart.tsx         ← 5-year baseline vs optimised (Recharts)
│       │   │   ├── MultiTimeframeChart.tsx     ← extended projection chart (10K)
│       │   │   ├── BiologicalAgeWidget.tsx     ← bio age display with delta
│       │   │   ├── RiskSummaryCard.tsx         ← headline risk summary
│       │   │   ├── RecommendationFeed.tsx      ← Gemini AI recommendation cards
│       │   │   ├── CompoundCart.tsx            ← current regimen display
│       │   │   ├── CompoundContributions.tsx   ← per-compound load breakdown
│       │   │   ├── PathwayChainPanel.tsx       ← drug→gene→pathway chain viz
│       │   │   ├── GuidelinePanel.tsx          ← CPIC/AHA/WHO guideline refs
│       │   │   ├── CircadianPanel.tsx          ← circadian/sleep analysis
│       │   │   ├── ClinicianExportButton.tsx   ← PDF download button
│       │   │   ├── PaywallOverlay.tsx          ← Pro feature paywall
│       │   │   └── ProgressChart.tsx           ← health progress tracking
│       │   │
│       │   ├── notifications/         # ─── Notification System ───
│       │   │   └── NotificationBell.tsx       ← bell icon + unread badge + dropdown
│       │   │
│       │   └── chat/                  # ─── AI Chat Interface ───
│       │       └── (chat components)
│       │
│       ├── store/                     # ─── Zustand State Stores ───
│       │   ├── wizardStore.ts         ← full patient + analysis state (persisted)
│       │   ├── authStore.ts           ← JWT token + user session
│       │   └── notificationStore.ts   ← notification state + unread count
│       │
│       └── api/                       # ─── API Layer ───
│           ├── analysis.ts            ← analysis API hooks
│           └── mock_response.json     ← client-side fallback for offline/demo
│
└── docs/                              # ═══ Documentation ═══
    ├── GNN_README.md                  ← full GNN documentation (760 lines, 37K)
    ├── Eirion-ET.pdf                  ← hackathon submission PDF (1.1 MB)
    ├── Eirion_highlvl_doc.docx        ← high-level project documentation
    │
    ├── images/
    │   ├── Banner_Eirion.png          ← project banner (1.8 MB)
    │   ├── ARCHITECTURE.png           ← system architecture diagram (561 KB)
    │   ├── FULL_PROCESS.png           ← end-to-end data flow diagram (738 KB)
    │   └── GNN_Architecture.png       ← GATv2 model architecture diagram (1.1 MB)
    │
    └── videos/
        └── eirion_platform_demo.mp4   ← full platform demo video (18 MB)

File Statistics

Component	Files	Lines of Code (est.)	Key Highlights
Backend Engine	10	~3,500	scorer.py (887 lines), pdf_generator.py (1,200+ lines)
Backend Routers	12	~1,000	11 API routers covering all platform features
Backend Models	3	~400	Pydantic v2 request/response models
Backend Tests	2	~400	Scoring engine unit tests
Backend Planned	8	~2,000	Phase 2/3 modules (designed, not live)
Frontend Pages	7	~1,800	Demo.tsx (26K — full demo scenario)
Frontend Wizard	13	~2,800	9-step onboarding flow
Frontend Dashboard	14	~1,700	14 interactive dashboard widgets
Frontend State	3	~300	Zustand persisted stores
Documentation	3	~800	GNN_README.md (760 lines)
Total	~97	~14,700	—

🚀 Quick Start

Prerequisites

Requirement	Version
Python	3.10+
Node.js	20+
npm	10+
Gemini API Key	Get from Google AI Studio

1. Clone & Configure

git clone https://github.com/Ajayace03/Eirion.git
cd eirion
cp .env.example .env
# Edit .env — set GEMINI_API_KEY (required)
# Optionally set JWT_SECRET for production

2. Backend

cd backend

# Create virtual environment
python -m venv .venv

# Activate (Linux/macOS)
source .venv/bin/activate
# Activate (Windows)
.\.venv\Scripts\Activate.ps1

# Install dependencies
pip install -r requirements.txt

# Generate Prisma client and create database
prisma generate --schema=schema.prisma
prisma db push --schema=schema.prisma

# Start API server
uvicorn main:app --reload --host 0.0.0.0 --port 8000

🟢 API available at: http://localhost:8000
📖 Interactive docs: http://localhost:8000/docs

3. Frontend

cd frontend
npm install
npm run dev

🟢 App available at: http://localhost:5173

4. Docker (Full Stack — One Command)

cp .env.example .env   # set GEMINI_API_KEY
docker compose up --build

Service	URL
🌐 Frontend	http://localhost:5173
⚡ Backend API	http://localhost:8000
📖 API Docs (Swagger)	http://localhost:8000/docs

🔐 Environment Variables

Create .env in the eirion/ root (shared by both backend and frontend):

# ─── Required ────────────────────────────────────────────────────────────────
GEMINI_API_KEY=your_google_gemini_api_key_here

# ─── Backend ─────────────────────────────────────────────────────────────────
DATABASE_URL=file:./dev.db
JWT_SECRET=change_this_to_a_random_256bit_secret    # Generate: python -c "import secrets; print(secrets.token_hex(32))"
ALLOWED_ORIGINS=http://localhost:5173,http://localhost:3000

# ─── Frontend (Vite) ────────────────────────────────────────────────────────
VITE_API_URL=http://localhost:8000

🎮 Demo Flow

Register at /register — create an account with email, name, and password
Click "Start Your Analysis" — launches the 9-step onboarding wizard
Step 1–2: Enter demographics and pre-existing conditions
Step 3: Upload a 23andMe .txt file for automatic CYP phenotype inference
Step 4: Or manually enter genetic metabolizer statuses
Step 5: Upload lab report image (Gemini Vision OCR) or enter values manually
Step 6–7: Complete lifestyle and diet questionnaires
Step 8: Enter your full supplement/medication stack with doses
Step 9: Confirm and hit "Generate Blueprint"
Explore the dashboard: organ scores, trajectory chart, DDI flags, Gemini recommendations
Download your Clinical PDF Report from the dashboard header

🎯 Quick Demo: Click "Try Priya Demo" on the landing page to instantly see a fully populated analysis without entering any data.

📡 API Reference

Authentication

All protected endpoints require a Bearer token in the Authorization header:

Authorization: Bearer <JWT_TOKEN>

Endpoints

Auth & User Management

Method	Path	Auth	Description
`POST`	`/users/register`	❌	Register new account (email, full_name, password)
`POST`	`/users/login`	❌	Login → returns JWT token
`GET`	`/users/me`	✅	Get current user profile + attributes
`PATCH`	`/users/me`	✅	Update profile (full_name)
`POST`	`/users/change-password`	✅	Change password (requires current password, bcrypt)

Core Analysis

Method	Path	Auth	Description
`POST`	`/analysis/run`	✅	Main inference — runs full multi-organ scoring pipeline
`POST`	`/extraction/parse-labs`	✅	Gemini Vision lab report OCR (image → structured values)
`GET`	`/export/clinical-pdf`	✅	Download 9-page clinical PDF report

Supporting Features

Method	Path	Auth	Description
`GET`	`/notifications`	✅	Get notification list with unread status
`PATCH`	`/notifications/{id}/read`	✅	Mark notification as read
`GET`	`/history`	✅	Get trajectory history (past analyses)
`POST`	`/chat/message`	✅	Send message to Gemini AI chat
`GET`	`/labs`	✅	Get stored lab values
`GET`	`/wearables/sync`	✅	Trigger wearable data sync
`GET`	`/billing/status`	✅	Get subscription status
`GET`	`/health`	❌	Health check (returns service status)

Example: Run Analysis

curl -X POST http://localhost:8000/analysis/run \
  -H "Authorization: Bearer <JWT>" \
  -H "Content-Type: application/json" \
  -d '{
    "patient": {
      "age": 35,
      "sex": "female",
      "weight_kg": 65,
      "height_cm": 168,
      "ethnicity": "south_asian"
    },
    "genetics": {
      "cyp2d6_metabolizer": "poor",
      "cyp2c19_metabolizer": "intermediate",
      "cyp3a4_metabolizer": "normal",
      "cyp2c9_metabolizer": "normal",
      "cyp1a2_metabolizer": "unknown",
      "slco1b1_function": "reduced",
      "mthfr_c677t": "heterozygous"
    },
    "conditions": [
      {"condition_id": "hyperlipidemia"}
    ],
    "lifestyle": {
      "sleep_hours_avg": 6.5,
      "stress_level": 7,
      "alcohol_drinks_per_week": 5,
      "exercise_mins_per_week": 90,
      "sugar_g_per_day": 120,
      "smoking_status": "never",
      "environmental_toxin_exposure": 3,
      "processed_food_frequency": 5
    },
    "regimen": [
      {"compound_id": "atorvastatin", "dose_mg": 20, "frequency_per_day": 1, "is_rx": true},
      {"compound_id": "ashwagandha", "dose_mg": 600, "frequency_per_day": 1},
      {"compound_id": "vitamin_d", "dose_mg": 2000, "frequency_per_day": 1},
      {"compound_id": "omega_3", "dose_mg": 2000, "frequency_per_day": 1}
    ]
  }'

Example Response (abbreviated)

{
  "risk_summary": {
    "risk_level": "amber",
    "liver_index_now": 67.5,
    "projected_drop_percent": 12.3,
    "headline": "Moderate liver stress detected — index 68/100. Projected to decline ~12% by year 5."
  },
  "organ_scores": [
    {"organ": "liver", "score": 67.5, "risk_level": "amber", "top_driver": "Atorvastatin"},
    {"organ": "kidney", "score": 82.1, "risk_level": "green", "top_driver": "Lifestyle"},
    {"organ": "cardiovascular", "score": 71.3, "risk_level": "amber", "top_driver": "High Stress"},
    {"organ": "metabolic", "score": 74.8, "risk_level": "amber", "top_driver": "High Sugar Diet"}
  ],
  "biological_age": 39.8,
  "ddi_flags": [
    {
      "compound_a": "atorvastatin",
      "compound_b": "ashwagandha",
      "severity": "moderate",
      "mechanism": "CYP3A4 competitive inhibition increases statin exposure"
    }
  ],
  "trajectory": [
    {"year": 0, "liver_index": 67.5, "optimized_liver_index": 67.5},
    {"year": 5, "liver_index": 59.2, "optimized_liver_index": 72.1}
  ],
  "recommendations": [
    {
      "action_type": "swap",
      "title": "Replace Ashwagandha with Rhodiola Rosea",
      "details": "Your CYP2D6 poor-metabolizer status increases ashwagandha hepatic load by 40%..."
    }
  ]
}

📊 Database Schema

The backend uses SQLite via Prisma with three models:

model User {
  id                  String    @id @default(cuid())
  email               String    @unique
  full_name           String
  hashed_password     String
  onboarding_complete Boolean   @default(false)
  is_pro              Boolean   @default(false)
  oura_token          String?
  created_at          DateTime  @default(now())
  attributes          UserAttributes?
  history             TrajectoryHistory[]
}

model UserAttributes {
  id                  String @id @default(cuid())
  user_id             String @unique
  age                 Int
  sex                 String
  weight_kg           Float
  height_cm           Float?
  ethnicity           String?
  cyp2d6_metabolizer  String @default("unknown")
  cyp2c19_metabolizer String @default("unknown")
}

model TrajectoryHistory {
  id         String   @id @default(cuid())
  user_id    String
  score      Int
  snapshot   String   // JSON string of AnalysisResponse
  created_at DateTime @default(now())
}

📚 Data Sources & Datasets

Source	Use in EIRION	Records	License
Tox21 qHTS	GNN training: 13 bioassay toxicity labels	12,505 compounds	Public domain (US Gov)
PharmGKB	Drug–gene relationships, CYP star-alleles, InChIKey bridge	21,936 compound-gene edges	CC BY-SA 4.0
CPIC Guidelines	CYP phenotype → clinical action mapping	572 drug-gene pairs	CC BY 4.0
Reactome	Gene–pathway edges for GNN knowledge graph	48,593 gene-pathway edges (2,848 pathways)	CC BY 4.0
RDKit	SMILES → Morgan fingerprints (1024-bit, radius=2)	—	BSD 3-Clause
Google Gemini	Clinical recommendation generation + Lab OCR	—	API ToS

🗺 Roadmap

Phase 1 ✅ (Current — Hackathon)

Phase 2 🔮 (Post-Hackathon)

FHIR R4 EHR integration (Epic, Cerner) — see planned/wearable_fhir_integration.py
Oura / Apple Health wearable sync — live readiness scores via OAuth
GNN v2.0: CTD + ChEMBL expansion (50%+ compound-gene coverage)
- CTD chemical-gene linkage via PubChem CAS numbers
- ChEMBL bioactivity data (pChEMBL ≥ 5)
- Tanimoto structural similarity inference for unlinked compounds
- Node2Vec gene embeddings (replace one-hot)
- Edge feature encoding (PK/PD flags, CPIC level, evidence source)
Temporal Fusion Transformer for 10-year multi-organ forecasting — see planned/tft_forecasting.py
RL regimen optimiser (SAC/PPO) for supplement redesign — see planned/rl_regimen_optimizer.py
Advanced biological age tracking — see planned/bio_age_tracker.py
D-MPNN molecular encoder — see planned/dmpnn_toxicity.py
Clinician dashboard + B2B API

Phase 3 🏥 (SaMD Pathway)

FDA SaMD (Software as Medical Device) regulatory preparation
IRB-approved clinical trial integration
Pharmacist in-the-loop review workflow
Multi-drug interaction modelling (DrugBank DDI dataset)
Attention-based explainability dashboard (GATv2 attention weights → clinical rationale)

⚠️ Known Limitations

🔬 This is a Phase 0 hackathon prototype. It is NOT a medical device.

Limitation	Detail	Mitigation
GNN compound-gene coverage	6.1% via PharmGKB (758/12,505 compounds)	v2.0 targets 50%+ via CTD + ChEMBL expansion
Deterministic scoring	Organ scores use rule-based models, not validated clinical models	Gemini enrichment adds clinical context; future versions use ML throughout
Clinician review required	All recommendations require professional review before action	Disclaimer shown in app and PDF report
PDF not FDA-cleared	Clinical PDF is informational only	Phase 3 targets SaMD regulatory pathway
Wearable sync stub	Architecture-complete but uses mock data	Pending OAuth token integration for Oura/Apple Health
No stereochemistry	Morgan FP (radius=2) doesn't capture enantiomer differences	v2.0 considers D-MPNN molecular encoder
CPU-only tested	GNN trained and deployed on CPU	CUDA/MPS should work with no code changes

📖 References

Brody, S., Alon, U., and Yahav, E. (2021). How attentive are graph attention networks? arXiv:2105.14491
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y. (2018). Graph attention networks. ICLR 2018
Yang, K., Swanson, K., Jin, W. et al. (2019). Analyzing learned molecular representations for property prediction. JCIM 59(8), 3370–3388
Zitnik, M., Agrawal, M., and Leskovec, J. (2018). Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34(13), i457–i466
CPIC Consortium. (2024). Clinical Pharmacogenomics Implementation Consortium guidelines. https://cpicpgx.org
Davis, A. P. et al. (2023). Comparative Toxicogenomics Database (CTD): Update 2023. NAR 51(D1), D1257–D1262
Huang, R. et al. (2011). Chemical genomics profiling of environmental chemical modulation of human nuclear receptors. EHP 119(8), 1142–1148
Fey, M. and Lenssen, J. E. (2019). Fast graph representation learning with PyTorch Geometric. ICLR Workshop
Rogers, D. and Hahn, M. (2010). Extended-connectivity fingerprints. JCIM 50(5), 742–754

📄 License

MIT — see LICENSE

EIRION — "Predict before it's too late. Personalise to your biology."

ETGen AI Hackathon 2026 · Built by Ajay Bharathi

Name		Name	Last commit message	Last commit date
Latest commit History 8 Commits
backend		backend
docs		docs
frontend		frontend
.env.example		.env.example
.gitignore		.gitignore
README.md		README.md
docker-compose.yml		docker-compose.yml

Folders and files

Latest commit

History

Repository files navigation

EIRION — Precision Longevity Platform

🧬 DNA · 💊 Lifestyle · 🩺 Medications → Personalized Organ Health Forecasts

📋 Table of Contents

🧬 What is EIRION?

What EIRION Computes

🎬 Platform Demo

🏗 Platform Architecture

Experience Layer (Frontend)

Application Layer (API Gateway)

Intelligence Layer (ML/AI Engine)

Data Layer

🔄 Full Analysis Process

End-to-End Pipeline

🧠 GNN Model: Heterogeneous Graph Attention Network

Stage 1 — HeteroGAT-v2

Knowledge Graph Statistics

GATv2 Attention Mechanism

Stage 2 — Multi-Label MLP Classifier

Performance — GNN vs. Random Forest Baseline

Training Configuration

🖥 Dashboard Features

⚡ Tech Stack

📁 Project Structure

File Statistics

🚀 Quick Start

Prerequisites

1. Clone & Configure

2. Backend

3. Frontend

4. Docker (Full Stack — One Command)

🔐 Environment Variables

🎮 Demo Flow

📡 API Reference

Authentication

Endpoints

Auth & User Management

Core Analysis

Supporting Features

Example: Run Analysis

Example Response (abbreviated)

📊 Database Schema

📚 Data Sources & Datasets

🗺 Roadmap

Phase 1 ✅ (Current — Hackathon)

Phase 2 🔮 (Post-Hackathon)

Phase 3 🏥 (SaMD Pathway)

⚠️ Known Limitations

📖 References

📄 License

EIRION — "Predict before it's too late. Personalise to your biology."

About

Topics

Resources

Uh oh!

Stars

Watchers

Forks

Releases

Packages 0

Uh oh!

Contributors

Uh oh!

Languages

Packages