🌸 StormCycle — AI-Powered Menstrual Health Platform

Clinical-grade cycle tracking & PCOD screening built for real-world irregular cycles.

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🖼️ Product Preview

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🎯 Overview

StormCycle transforms raw menstrual cycle data into actionable health intelligence using AI.

It delivers:

• 🔮 Personalized predictions for irregular cycles (26–50+ days)
• 🩺 Clinical PCOD risk scoring (Bayesian model)
• 🤖 AI health assistant (RAG-based)
• 📄 Doctor-ready reports
• 🔒 Privacy-first architecture

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✨ Why This Matters

Most apps assume a 28-day cycle.

Reality:

• Only ~16% of women follow that pattern
• Millions get inaccurate predictions

👉 StormCycle adapts to your biology — not averages.

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🚨 Where Existing Apps Fail

Issue	Impact
Calendar-based prediction	Up to 8-day errors
No PCOD detection	Missed early diagnosis
High false positives	Anxiety & misinformation

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🧠 Core Innovation

🔮 LSTM Cycle Prediction

• Learns individual cycle patterns
• Works on irregular ranges
• 91.3% accuracy

🩺 Bayesian PCOD Screening

• Multi-signal analysis
• Reduces false positives
• Clinically grounded

🤖 AI Health Assistant

• Based on ACOG, NHS, NIH
• Context-aware responses
• Human escalation for risk cases

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🏗️ System Architecture (Production View)

StormCycle is designed as a modular AI health intelligence system.

🔁 High-Level Architecture

flowchart TD
    A[User Input Cycle Data] --> B[Frontend React UI]
    B --> C[API Gateway FastAPI]

    C --> D[AI Engine Layer]

    D --> D1[LSTM Prediction Model]
    D --> D2[Bayesian PCOD Model]
    D --> D3[RAG Health Assistant]

    D1 --> E[Insight Generator]
    D2 --> E
    D3 --> E

    E --> F[Report Builder]
    F --> G[Secure Storage Firestore]
    G --> H[Dashboard Output]

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⚙️ Backend Architecture

🧠 AI Processing Layer

• LSTM model for cycle forecasting
• Bayesian inference for PCOD probability
• RAG-based medical assistant (ACOG / NIH / NHS knowledge base)

⚡ API Layer (FastAPI)

Handles:

• User cycle data ingestion
• Model inference calls
• Report generation
• Authentication middleware

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🎨 Frontend Architecture

📱 User Interface Flow

Login → Input Cycle Data → AI Analysis → Health Dashboard → Report Export

UI Modules:

• 📅 Cycle Tracker UI
• 📊 Prediction Dashboard
• 🩺 PCOD Risk Panel
• 🤖 AI Chat Assistant
• 📄 Report Generator

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🧠 AI Model Architecture

flowchart LR
    A["📊 Historical Cycle Data"] --> B["🧹 Preprocessing Layer"]

    B --> C["🔮 LSTM Model"]
    B --> D["🩺 Bayesian Model"]

    C --> E["📈 Cycle Prediction"]
    D --> F["⚠️ Risk Probability"]

    E --> G["🧠 Fusion Engine"]
    F --> G

    G --> H["📄 Final Health Score"]

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🔐 Security Architecture

StormCycle follows health-grade data protection standards:

🛡️ Security Layers

• AES-256 encryption (data at rest)
• TLS 1.3 (data in transit)
• JWT authentication
• Role-based access control
• Optional zk-SNARK privacy layer

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☁️ Deployment Architecture

flowchart TD
    A["Frontend (Vercel / Netlify)"]
    B["Backend (FastAPI - Cloud Run / AWS)"]
    C["Database (Firebase / Firestore)"]
    D["AI Models (Server-side GPU optional)"]

    A --> B
    B --> C
    B --> D
    D --> B

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📡 API Structure

🔹 Core Endpoints

Endpoint	Function
/predict-cycle	LSTM prediction
/pcod-risk	Bayesian risk score
/health-report	Full report generation
/chat	AI assistant (RAG)

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📊 Data Flow (End-to-End)

1. User enters cycle data
2. Frontend sends structured payload
3. FastAPI validates request
4. AI models run inference
5. Results merged in fusion engine
6. Report generated
7. Stored securely
8. Displayed in dashboard

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🚀 System Design Summary

StormCycle is not a monolithic app — it is a modular AI healthcare intelligence system designed for real-world clinical scalability.

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🧰 Tech Stack

Layer	Technology
Frontend	TypeScript, HTML, CSS
Backend	FastAPI
Database	Firebase Firestore
AI Models	LSTM, RAG
Security	AES-256, TLS 1.3
Compliance	DPDP Act 2023

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⚡ Quick Start

git clone https://github.com/MadhuTiwari-345/StormCycle.git
cd StormCycle
npm install

Create .env.local:

GEMINI_API_KEY=your_key

Run:

npm run dev

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📊 Sample Output

Health Score        → 84%
Cycle Prediction    → Starts in 3 days
Anomaly             → +4 day deviation
PCOD Risk           → LOW

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🔒 Privacy First

• AES-256 encryption
• TLS 1.3 secure transfer
• Zero-knowledge analytics (zk-SNARKs)

👉 Your health data is never exposed.

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📈 Market Opportunity

Year	Market Size
2024	$1.69B
2030	$5.07B
2035	$13.11B

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💰 Pricing Model

Tier	Price
Free	₹0
Premium	₹99/month
B2B	₹49/user/month

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🧠 Key Insights

• Irregular cycles are the norm
• Multi-signal diagnosis > single indicators
• Privacy is a core feature
• India = fastest growing femtech market

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📁 Project Structure

StormCycle/
├── src/
├── public/
├── README.md
├── package.json
└── vite.config.ts

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🧾 Conclusion

StormCycle is not just a tracker.

It is a personalized clinical intelligence system that bridges:

• AI
• Healthcare
• Privacy

👉 Moving femtech from tracking → diagnosis support

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Built with ❤️ by Madhu Tiwari - Team 🌸 SheStorm 🌸

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