Mamora

Landing and Sign up
Dashboard and Multilingual Support to set Accross the platrom
Symptom based diet serach
Maternal Reminders, Schedules & Alarms
Calender to check Appointments and Remainders and Clinical Maternal Immunization Timeline
Roadmap of Pregnancy Milestones Breakdown Explore baby size transitions, organ developments, and vital maternal advice Month by Month.
Essential Hospital Bag Checklist
Report Analysis
Verified Report
Storing the reports for future purpose
Find nearby clinics, hospitals, 24/7 pharmacies, and police stations for safety backups(Map and SOS)
Interactive Exercises
AI Assistant Chatbot with Multilingual Speech, text & Offline triage support.
Profile Settings
Platform Works in Offline

Inspiration

Every two minutes, a woman dies from a preventable pregnancy-related complication. In India, millions of expecting mothers — particularly in rural and semi-urban areas — have limited access to consistent prenatal care. They visit a doctor once a month at most, leaving weeks of critical health data unmonitored.

We were inspired by a simple but powerful question: What if a mother could carry a clinical companion in her pocket — one that speaks her language, understands her symptoms, and alerts her before a complication becomes a crisis?

That question became Mamora — a name rooted in "Maatru" (mother in Sanskrit), built to be the digital ASHA worker that never sleeps.

What it does

Mamora is an AI-powered maternal health companion that helps pregnant women monitor, understand, and act on their health — in their own language, anytime, anywhere.

Core Features:

🩺 Clinical Vitals Tracker — Logs hemoglobin, blood pressure (systolic/diastolic), and blood glucose (fasting & post-meal). An algorithm instantly computes a Health Score and flags risks like gestational diabetes, anemia, or preeclampsia.
🤖 AI Chat Assistant — Powered by Google Gemini 1.5 Flash, mothers can ask pregnancy-related questions conversationally. A rule-based fallback ensures responses are always clinically safe.
🌐 3-Language Support — Full runtime localization across English, Tamil, and Hindi — switching languages live across the entire dashboard, charts, chatbot, and voice output.
🗣️ Voice Assistant — Uses the Web Speech API for hands-free, speech-to-text input and text-to-speech responses — critical for semi-literate users.
🥗 Symptom-Based Nutrition Advisor — Matches symptoms like morning sickness, constipation, or low hemoglobin to traditional Indian superfoods (ragi, jaggery, beetroot) with clinical dietary tips.
🗺️ Hospital Finder — Leaflet.js-powered map locates the nearest maternity hospitals and emergency services using the device's GPS.
🚨 Emergency SOS Dispatcher — One-tap button instantly captures GPS coordinates, packages patient details, and fires an emergency cloud alert signal.
📶 Offline Sync Engine — Vitals logged without internet are queued locally and batch-uploaded automatically once connectivity is restored.

How we built it

We designed Mamora as a decoupled, production-grade client-server system:

Frontend

React.js (v18) — SPA architecture with component-based UI design
Framer Motion — Smooth micro-animations and gesture-based transitions
Lottie-React — SVG-based animated illustrations for the onboarding flow
Leaflet.js — Interactive map rendering with OpenStreetMap tiles
Axios — Dynamic API client with environment-aware base URL configuration
Web Speech API — Native browser TTS and STT for voice interactions
Context API — Global language state manager for live localization switching

Backend

Python + FastAPI — High-performance async REST API layer
Pydantic v2 — Strict data validation and schema enforcement
Uvicorn — ASGI server for production-grade async request handling
Rule-based ML Logic — Deterministic clinical risk engine for vitals analysis

Cloud & DevOps

Vercel — Frontend CDN hosting with Git-triggered CI/CD
Render — Backend Python Web Service (Python 3.11.8 runtime)
GitHub — Version control with branch protection and push protection for secrets

External APIs

Google Gemini 1.5 Flash — Contextual, multilingual LLM responses
OpenStreetMap — Free, open-source map tiles for hospital locator

Challenges we ran into

🔒 API Key Security — GitHub's Push Protection blocked our first commit because a Gemini API key was accidentally hardcoded. We rewrote the entire git history using git filter-branch and migrated to .env files.
⚙️ Dependency Conflicts — react-scripts requires TypeScript ^4.x, but the project initially had TypeScript ^6.0.2 in devDependencies, causing ERESOLVE failures on Vercel's clean install. We resolved this by downgrading and regenerating the lockfile.
🐍 Python Version Hell — Render defaulted to Python 3.14 (pre-release), which broke the pillow package compilation. We pinned the runtime to stable 3.11.8 using a .python-version file.
🗣️ Speech Duplication Bug — The text-to-speech assistant was reading messages twice due to a React state re-render cycle. We resolved it using a speechBaselineRef to track the last spoken message.
🌐 Multilingual Accuracy — Ensuring clinical terms translated correctly across Tamil and Hindi without losing medical precision required iterative refinement of translation keys in the localization context.

Accomplishments that we're proud of

✅ Fully deployed, production-grade application — live on Vercel + Render with automatic CI/CD pipelines.
✅ Three-language real-time localization — seamless runtime switching with zero page reloads, across every component.
✅ Hybrid AI safety model — combining the flexibility of Gemini LLM with hard clinical guardrails from the deterministic rule engine, preventing dangerous AI hallucinations on medical topics.
✅ Offline-first architecture — vitals are never lost, even without internet access.
✅ Zero-cost infrastructure — the entire platform runs on free-tier services, making it instantly replicable by NGOs or government health programs.

What we learned

Security-first development matters from Day 1 — secrets in source code can cause irreversible reputation and compliance damage.
LLMs need guardrails in healthcare — never let a generative model make unconstrained clinical decisions; always pair it with deterministic validation.
Accessibility is not optional — designing for semi-literate, rural users forced us to rethink every UI choice, from button sizes to voice-first interaction flows.
Offline capability is a feature, not an afterthought — in target regions, intermittent connectivity is the norm, not the exception.

What's next for Mamora

👩‍⚕️ Live Teleconsultation — Direct video calls with certified ASHA workers and OB/GYN specialists
⌚ Wearable Device Integration — Bluetooth sync with affordable IoT BP monitors and pulse oximeters
🧠 ML-Powered Risk Prediction — Replace the rule engine with a trained model on real maternal health datasets for higher diagnostic accuracy
🗄️ Persistent Database — Migrate from in-memory storage to PostgreSQL with secure JWT-authenticated user accounts
🔔 Push Notifications — Medication reminders, missed appointment alerts, and weekly fetal milestone updates
🌏 Expanded Languages — Telugu, Kannada, Malayalam, and Bengali localization
🏥 EHR Integration — HL7 FHIR API connection to hospital Electronic Health Records