BridgeNote

Mental health support, between sessions.

BridgeNote is an AI-powered continuity-of-care platform that keeps therapy working in the 10,000 minutes between sessions. Clients do daily check-ins and chat with a personalized AI companion. Therapists see mood trends, journal sentiment, and AI-generated session summaries — before the next appointment begins.

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What it does

Role	Experience
Client	Daily mood + slider check-in → optional journal → chat with AI companion anytime
Therapist	Dashboard with mood trends, slider charts, check-in history, and AI conversation summaries per client

The AI companion is not generic — it knows each client's treatment goals, triggers, strengths, diagnoses, and last session summary because the therapist enters that context once. Every chatbot response is built on that foundation.

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Live Demo

Requirements: Redis (local or Docker) + an Anthropic API key. The seed script pre-loads all demo data — no manual setup beyond that.

1. Clone & configure

git clone https://github.com/crystallamar/BridgeNote.git
cd BridgeNote

Copy the example env file and add your key:

cp backend/.env.example backend/.env
# then open backend/.env and set ANTHROPIC_API_KEY=your_key_here

2. Start Redis

# Docker (easiest)
docker-compose up redis -d

# Or Homebrew on Mac
brew services start redis

3. Start the backend

cd backend
pip install -r requirements.txt
python -m textblob.download_corpora   # one-time setup
uvicorn main:app --reload
# → http://localhost:8000

4. Seed demo data

# From the backend/ directory
python seed_demo.py

This loads 5 demo clients with 7 days of check-ins, therapist context, and personalized check-in configs. The primary demo client is Ethan Rhodes (client-001) — a CS junior navigating academic burnout and imposter syndrome.

5. Start the frontend

cd ../frontend
npm install --legacy-peer-deps
npm start
# → http://localhost:3000

6. Walk the demo

Therapist view (loads by default):

• Select Ethan Rhodes from the client dropdown
• Overview tab — treatment goals, triggers, strengths, last session summary entered by therapist
• Check-ins tab — mood entries, stress/energy sliders, journal snippets, "What got in the way?" habit tags
• Conversations tab — AI-generated session summaries ready before the next appointment

Client view (toggle in nav):

• Complete a check-in: mood rating → stress/energy sliders → habit buttons → AI-generated journal prompt
• Chat with the AI companion — it already knows Ethan's full context

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Demo Clients

ID	Name	Profile
client-001	Ethan Rhodes ⭐	CS junior, academic burnout, imposter syndrome
client-002	Jordan Reid	Depression, GRAPES framework
client-003	Sam Tran	PTSD stabilization, grounding focus
client-004	Maya Krishnamurthy	College anxiety/depression, biweekly sessions
client-005	Alex Morales	GAD, workplace performance anxiety

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Tech Stack

Layer	Technology
Frontend	React 18, React Router v6, Recharts
Backend	FastAPI (Python 3.12), Uvicorn
AI	Claude claude-sonnet-4-6 (Anthropic) — streaming SSE
Sentiment analysis	TextBlob
Storage	Redis (async via redis.asyncio) — no SQL
Containerization	Docker Compose

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Safety Protocol

The AI companion has a hardcoded safety guardrail that activates on any language related to suicidal ideation, self-harm, or intent to harm others — whether direct, hypothetical, or through metaphor:

1. Stays present and warm — never ends the conversation
2. Explicitly discloses it is an AI, not a human
3. Provides crisis resources immediately: 988 Lifeline and Crisis Text Line (HOME to 741741)
4. Offers grounding (5-4-3-2-1 senses, box breathing)
5. Stays in conversation and asks grounding follow-up questions

This is an absolute constraint in the system prompt — it cannot be overridden by user messages.

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API Reference

POST /checkin/                    Submit a check-in (runs TextBlob sentiment)
GET  /checkin/recent/{id}         Fetch last N check-ins for a client
POST /checkin/journal-prompt      Generate AI journaling prompt from mood + context
POST /chat/start                  Create a new conversation
POST /chat/message                Streaming SSE chat response
GET  /chat/history/{conv_id}      Full conversation history
GET  /chat/summary/{conv_id}      AI-generated therapist summary
POST /therapist/context           Create or update therapist client context
GET  /therapist/context/{id}      Fetch therapist context
GET  /therapist/dashboard/{id}    Full dashboard: mood trend + check-ins + summaries

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Project Structure

BridgeNote/
├── backend/
│   ├── main.py                     FastAPI app entry point + CORS
│   ├── seed_demo.py                Seeds 5 demo clients into Redis
│   ├── services/
│   │   ├── claude_client.py        System prompt builder, streaming, safety protocol
│   │   └── redis_client.py         All Redis reads/writes
│   └── routers/
│       ├── chat.py                 Streaming SSE chat endpoint
│       ├── checkin.py              Check-in submission + journal prompt
│       └── therapist.py            Dashboard aggregation + context CRUD
├── frontend/
│   └── src/
│       ├── components/
│       │   ├── Chat/               Streaming chat UI
│       │   ├── CheckIn/            3-step check-in form
│       │   └── TherapistDashboard/ Recharts mood trends, summaries
│       └── hooks/
│           └── useChat.js          SSE streaming hook
└── docker-compose.yml

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Notes for Reviewers

• No auth system — client/therapist IDs are hardcoded (client-001 / therapist-001) for hackathon scope
• Redis is not persisted between restarts unless configured — run seed_demo.py again if data disappears
• npm install --legacy-peer-deps is required due to an ajv@8 peer dependency conflict with react-scripts 5
• The /chat/message endpoint returns text/event-stream; the frontend proxy in package.json forwards to localhost:8000

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Built in 48 hours. Mental health tooling deserves better infrastructure.