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Neurocognitive Mirror

A privacy-first cognitive self-tracking monorepo with two independent applications:

  • streamlit_mvp/: zero-infrastructure Streamlit app for immediate Community Cloud deployment.
  • full_saas/: local-first SaaS with React, FastAPI, PostgreSQL/pgvector, Redis, Elasticsearch, and a local OpenAI-compatible LLM service.

Screenshots

Placeholder: Dashboard screenshot

Placeholder: Cognitive test screenshot

Placeholder: AI narrative report screenshot

Architecture

flowchart TD
  User[Browser] --> MVP[Streamlit MVP]
  MVP --> JSON[(Local JSON)]
  User --> FE[React Frontend]
  FE --> IDB[(IndexedDB)]
  FE --> API[FastAPI Backend]
  API --> PG[(Postgres + pgvector)]
  API --> Redis[(Redis cache)]
  API --> ES[(Elasticsearch search)]
  API --> LLM[Local OpenAI-compatible LLM]
  API --> RAG[RAG retrieval pipeline]
  RAG --> PG
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Quickstart

Streamlit MVP

cd streamlit_mvp
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
streamlit run app.py

Full SaaS

cd full_saas
cp .env.example .env
docker compose up --build

Open:

MVP vs Full SaaS

Capability Streamlit MVP Full SaaS
Cognitive tests Yes Yes
Demo data Yes Yes
Local persistence JSON + session IndexedDB + optional sync
Backend analytics In-process FastAPI
RAG Rule/context-lite pgvector + sentence-transformers
LLM Optional OpenAI-compatible Local OpenAI-compatible service
Deployment Streamlit Cloud Docker Compose / local server

Privacy Philosophy

Neurocognitive Mirror treats cognition data as highly sensitive biometric-adjacent data. The system defaults to local storage, never stores raw cognitive event streams on the backend by default, supports anonymized aggregate analytics, and makes server sync opt-in.

Roadmap

  • Add additional validated tasks such as N-back and task switching.
  • Add encrypted export bundles.
  • Add clinician-facing local review mode.
  • Add WebAuthn support.
  • Expand RAG corpus with user-supplied papers.

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