From ca5984253564a57e0292fbde5f733134f8b414c7 Mon Sep 17 00:00:00 2001 From: rishithc Date: Sat, 13 Jun 2026 10:43:44 -0700 Subject: [PATCH 01/10] feat: scaffold pegasus services + agent rules + iMessage bridge Runnable stubs for all four services against shared/contract.md: - ml (:8003) TRIBE v2 predictor (stub), deviation scoring, Claude intervention - backend (:8001) orchestrator + SQLite, fans out to signals/ml, degrades gracefully - signals (:8002) Claude sentiment + typing dynamics, Twilio alerts, collector.js - frontend (:3000) Vite React check-in flow + Check Engine Light Plus: - shared/contract.{md,py} as the source of truth - per-folder AGENTS.md + .claude/agents/* so each teammate's agent stays in its lane - standalone imessage/ bridge (AppleScript send + chat.db poll, macOS, owner TBD) - dev.sh to set up + run everything Co-Authored-By: Claude Opus 4.8 (1M context) --- .claude/agents/dhruva-signals.md | 21 +++++ .claude/agents/imessage-bridge.md | 27 ++++++ .claude/agents/jason-backend.md | 22 +++++ .claude/agents/rishith-ml.md | 24 +++++ .claude/agents/wesley-frontend.md | 23 +++++ .gitignore | 5 + AGENTS.md | 37 ++++++++ README.md | 26 ++++++ backend/.env.example | 4 + backend/AGENTS.md | 38 ++++++++ backend/db.py | 89 ++++++++++++++++++ backend/main.py | 88 ++++++++++++++++++ backend/orchestrator.py | 82 +++++++++++++++++ backend/requirements.txt | 5 + backend/stimuli.py | 32 +++++++ dev.sh | 46 ++++++++++ frontend/AGENTS.md | 34 +++++++ frontend/index.html | 12 +++ frontend/package.json | 19 ++++ frontend/src/App.jsx | 96 +++++++++++++++++++ frontend/src/EngineLight.jsx | 45 +++++++++ frontend/src/api.js | 21 +++++ frontend/src/index.css | 103 +++++++++++++++++++++ frontend/src/keystroke.js | 35 +++++++ frontend/src/main.jsx | 10 ++ frontend/vite.config.js | 17 ++++ imessage/.env.example | 4 + imessage/AGENTS.md | 26 ++++++ imessage/README.md | 52 +++++++++++ imessage/bridge.py | 148 ++++++++++++++++++++++++++++++ imessage/imessage.py | 105 +++++++++++++++++++++ imessage/registry.py | 61 ++++++++++++ imessage/requirements.txt | 4 + imessage/send.applescript | 16 ++++ ml/.env.example | 2 + ml/AGENTS.md | 36 ++++++++ ml/intervention.py | 61 ++++++++++++ ml/main.py | 75 +++++++++++++++ ml/requirements.txt | 8 ++ ml/scoring.py | 51 ++++++++++ ml/tribe.py | 53 +++++++++++ shared/AGENTS.md | 8 ++ shared/contract.md | 102 ++++++++++++++++++++ shared/contract.py | 79 ++++++++++++++++ signals/.env.example | 5 + signals/AGENTS.md | 37 ++++++++ signals/alerts.py | 31 +++++++ signals/collector.js | 37 ++++++++ signals/main.py | 74 +++++++++++++++ signals/requirements.txt | 6 ++ signals/sentiment.py | 83 +++++++++++++++++ 51 files changed, 2125 insertions(+) create mode 100644 .claude/agents/dhruva-signals.md create mode 100644 .claude/agents/imessage-bridge.md create mode 100644 .claude/agents/jason-backend.md create mode 100644 .claude/agents/rishith-ml.md create mode 100644 .claude/agents/wesley-frontend.md create mode 100644 AGENTS.md create mode 100644 backend/.env.example create mode 100644 backend/AGENTS.md create mode 100644 backend/db.py create mode 100644 backend/main.py create mode 100644 backend/orchestrator.py create mode 100644 backend/requirements.txt create mode 100644 backend/stimuli.py create mode 100755 dev.sh create mode 100644 frontend/AGENTS.md create mode 100644 frontend/index.html create mode 100644 frontend/package.json create mode 100644 frontend/src/App.jsx create mode 100644 frontend/src/EngineLight.jsx create mode 100644 frontend/src/api.js create mode 100644 frontend/src/index.css create mode 100644 frontend/src/keystroke.js create mode 100644 frontend/src/main.jsx create mode 100644 frontend/vite.config.js create mode 100644 imessage/.env.example create mode 100644 imessage/AGENTS.md create mode 100644 imessage/README.md create mode 100644 imessage/bridge.py create mode 100644 imessage/imessage.py create mode 100644 imessage/registry.py create mode 100644 imessage/requirements.txt create mode 100644 imessage/send.applescript create mode 100644 ml/.env.example create mode 100644 ml/AGENTS.md create mode 100644 ml/intervention.py create mode 100644 ml/main.py create mode 100644 ml/requirements.txt create mode 100644 ml/scoring.py create mode 100644 ml/tribe.py create mode 100644 shared/AGENTS.md create mode 100644 shared/contract.md create mode 100644 shared/contract.py create mode 100644 signals/.env.example create mode 100644 signals/AGENTS.md create mode 100644 signals/alerts.py create mode 100644 signals/collector.js create mode 100644 signals/main.py create mode 100644 signals/requirements.txt create mode 100644 signals/sentiment.py diff --git a/.claude/agents/dhruva-signals.md b/.claude/agents/dhruva-signals.md new file mode 100644 index 0000000..f4a9269 --- /dev/null +++ b/.claude/agents/dhruva-signals.md @@ -0,0 +1,21 @@ +--- +name: dhruva-signals +description: Dhruva's signals agent for Pegasus. Use for work in /signals — behavioral sentiment analysis (Claude), typing dynamics, the :8002 FastAPI service, Twilio SMS alerts, and the collector.js keystroke tracker. MUST stay inside signals/. +tools: Read, Write, Edit, Bash, Grep, Glob +--- + +You are **Dhruva**, the signals engineer on the Pegasus team. + +**Hard boundary:** You may ONLY create/edit files inside `signals/`. Never touch +`frontend/`, `backend/`, `ml/`, or `shared/`. Ask Jason for contract changes. + +**Your service** (FastAPI on :8002): `/health`, `/signals/analyze` (Claude +`claude-sonnet-4-6` sentiment + energy + flags + `combined_signal_score`, with a +lexical fallback when no API key), `/signals/{user_id}`, `/alert` (Twilio SMS on +red; no-ops to console when creds are absent so the demo never crashes). + +You also own `signals/collector.js` (`KeystrokeTracker`), which the frontend +imports — a synced copy lives at `frontend/src/keystroke.js`. Keep them in sync. + +Read `signals/AGENTS.md`, `AGENTS.md`, and `shared/contract.md` first. +Stage only `signals/`. Commit to `feat/dhruva-sig`, PR into `dev`. diff --git a/.claude/agents/imessage-bridge.md b/.claude/agents/imessage-bridge.md new file mode 100644 index 0000000..f5ce526 --- /dev/null +++ b/.claude/agents/imessage-bridge.md @@ -0,0 +1,27 @@ +--- +name: imessage-bridge +description: Agent for the standalone /imessage bridge. Use for work on the macOS iMessage delivery channel — AppleScript send, chat.db polling, the bridge CLI/loop, phone↔user registry. MUST stay inside imessage/ and only call the backend API. +tools: Read, Write, Edit, Bash, Grep, Glob +--- + +You own the **`imessage/` bridge** — a standalone macOS delivery channel for +Pegasus (owner TBD; not one of the four service lanes). + +**Hard boundary:** ONLY create/edit files inside `imessage/`. Never touch +`frontend/`, `backend/`, `ml/`, `signals/`, or `shared/`. Talk to the **backend +only** (:8001): `POST /users`, `GET /stimulus/today`, `POST /checkin`. + +**How it works (macOS, no API):** send iMessages via AppleScript driving +Messages.app (`send.applescript` + `imessage.send`); receive by polling +`~/Library/Messages/chat.db` read-only (`imessage.fetch_new`), decoding +`attributedBody` when the `text` column is NULL. `bridge.py` provides +`register` / `send` / `send-daily` / `listen`. `registry.py` maps phone ⇄ +user_id and auto-registers new senders. + +**Constraints to respect:** needs Full Disk Access + a signed-in iMessage +account on the Mac; AppleScript send syntax is macOS-version-sensitive; no +keystroke dynamics over iMessage (typing metrics sent as 0). Keep it runnable +and never replay old chat history (start from current max ROWID). + +Read `imessage/AGENTS.md`, `AGENTS.md`, and `shared/contract.md` first. +Stage only `imessage/`. diff --git a/.claude/agents/jason-backend.md b/.claude/agents/jason-backend.md new file mode 100644 index 0000000..75075da --- /dev/null +++ b/.claude/agents/jason-backend.md @@ -0,0 +1,22 @@ +--- +name: jason-backend +description: Jason's backend agent for Pegasus. Use for work in /backend and /shared — the :8001 orchestrator, SQLite persistence, daily stimuli, and the API contract. MUST stay inside backend/ and shared/. +tools: Read, Write, Edit, Bash, Grep, Glob +--- + +You are **Jason**, the backend engineer and contract owner on the Pegasus team. + +**Hard boundary:** You may ONLY create/edit files inside `backend/` and +`shared/`. Never touch `frontend/`, `ml/`, or `signals/` — call their HTTP APIs. + +**Your service** (FastAPI on :8001) is the orchestrator and the ONLY service +the frontend talks to: `/health`, `/users`, `/stimulus/today`, `/checkin` +(fans out to Signals :8002 + ML :8003, persists, alerts on red), `/status/{id}`. + +**You own the contract.** `shared/contract.md` is the source of truth. When you +change it, note which service owner is affected so they stay shape-compatible. +Keep `orchestrator.py` degrading gracefully when a downstream service is down — +the demo must never hard-fail. + +Read `backend/AGENTS.md`, `AGENTS.md`, and `shared/contract.md` first. +Stage only `backend/` and `shared/`. Commit to `feat/jason-api`, PR into `dev`. diff --git a/.claude/agents/rishith-ml.md b/.claude/agents/rishith-ml.md new file mode 100644 index 0000000..ccc0132 --- /dev/null +++ b/.claude/agents/rishith-ml.md @@ -0,0 +1,24 @@ +--- +name: rishith-ml +description: Rishith's ML agent for Pegasus. Use for any work in the /ml folder — TRIBE v2 brain prediction, deviation/burnout scoring, Claude interventions, the :8003 FastAPI service. MUST stay inside ml/. +tools: Read, Write, Edit, Bash, Grep, Glob +--- + +You are **Rishith**, the ML engineer on the Pegasus hackathon team. + +**Hard boundary:** You may ONLY create/edit files inside `ml/`. Never touch +`frontend/`, `backend/`, `signals/`, or `shared/`. If you need something from +another service, call its HTTP API or read (never edit) `shared/contract.md`. +If you need a contract change, stop and report it for Jason — don't edit shared/. + +**Your service** (FastAPI on :8003): `/health`, `/predict` (TRIBE v2 healthy- +brain prediction), `/score` (deviation → 0-100 burnout + level), `/intervention` +(Claude `claude-sonnet-4-6`, with offline fallback). + +**Primary objective:** `ml/tribe.py` currently ships a deterministic STUB +(`TribePredictor._infer`). Your headline task is wiring real TRIBE v2 inference +there while keeping the same return shape so the rest of the pipeline is +unaffected. Keep `GET /health` green and the service runnable at every commit. + +Read `ml/AGENTS.md`, `AGENTS.md`, and `shared/contract.md` before working. +Stage only `ml/`. Commit to `feat/rishith-ml`, PR into `dev`. diff --git a/.claude/agents/wesley-frontend.md b/.claude/agents/wesley-frontend.md new file mode 100644 index 0000000..90f4f16 --- /dev/null +++ b/.claude/agents/wesley-frontend.md @@ -0,0 +1,23 @@ +--- +name: wesley-frontend +description: Wesley's frontend agent for Pegasus. Use for work in /frontend — the React (Vite) UI on :3000, the check-in flow, keystroke capture, and the Check Engine Light component. MUST stay inside frontend/. +tools: Read, Write, Edit, Bash, Grep, Glob +--- + +You are **Wesley**, the frontend engineer on the Pegasus team. + +**Hard boundary:** You may ONLY create/edit files inside `frontend/`. Never +touch `backend/`, `ml/`, `signals/`, or `shared/`. You talk to the **backend +only** (:8001) through the Vite `/api` proxy (already configured in +`vite.config.js`) — never call ml/signals directly. + +**The UI flow:** create/get user → `GET /api/stimulus/today` → show stimulus → +capture response with `KeystrokeTracker` (`src/keystroke.js`) → `POST +/api/checkin` → render the Check Engine Light (🟢/🟡/🔴) + intervention via +`EngineLight.jsx`. Keep `src/keystroke.js` in sync with Dhruva's +`signals/collector.js`. + +Run with `npm install && npm run dev` (port 3000). Keep it building. + +Read `frontend/AGENTS.md`, `AGENTS.md`, and `shared/contract.md` first. +Stage only `frontend/`. Commit to `feat/wesley-ui`, PR into `dev`. diff --git a/.gitignore b/.gitignore index 4a533e1..940e919 100644 --- a/.gitignore +++ b/.gitignore @@ -40,3 +40,8 @@ wandb/ # Logs *.log + +# iMessage bridge runtime (registry.json holds phone numbers — never commit) +imessage/.state.json +imessage/registry.json +imessage/*.scpt diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..f3c8a80 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,37 @@ +# Pegasus — Agent Rules (root) + +Read this, then read the `AGENTS.md` in **your** folder. Every teammate runs +their own coding agent; these files keep agents in their lane. + +## What Pegasus is +A mental-health "check engine light." A daily stimulus is sent to the user → +they respond (text + typing/timing) → **TRIBE v2** predicts how a *healthy* +brain responds to that stimulus → we score the deviation between healthy +prediction and the user's real behavioral signal → 🟢/🟡/🔴 + a Claude +intervention → SMS alert if red. + +## Services +| Folder | Owner | Stack | Port | +|-------------|---------|--------------------|------| +| `frontend/` | Wesley | React (Vite) | 3000 | +| `backend/` | Jason | FastAPI + SQLite | 8001 | +| `signals/` | Dhruva | FastAPI | 8002 | +| `ml/` | Rishith | FastAPI + TRIBE v2 | 8003 | +| `shared/` | Jason | API contract | — | +| `imessage/` | TBD | macOS iMessage bridge (AppleScript + chat.db) | — | + +## The 5 rules (non-negotiable) +1. **Stay in your folder.** Only edit the folder(s) you own (table above). + Need something from another folder? **Call its API or import it — never edit it.** +2. **The contract is law.** `shared/contract.md` is the single source of truth. + Need a field changed? Change it there first (Jason), then tell the owner. +3. **Branch + PR.** Work on `feat/-*`, PR into `dev`. Never push to `main`. + Stage only your folder: `git add ml/` — never `git add .`. +4. **Stay runnable.** Your service must start on its port and answer `GET /health`. +5. **Merge to `dev` every ~45 min** so integration never drifts far. + +## Run everything +```bash +./dev.sh # starts all 4 services (see script for per-service commands) +``` +Backend is the only service the frontend talks to; backend fans out to signals + ml. diff --git a/README.md b/README.md index 4b1bae5..01a6741 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,31 @@ # pegasus +A mental-health **check engine light**. A daily stimulus goes to the user → +they respond (text + typing/timing) → **TRIBE v2** predicts how a *healthy* +brain responds to that stimulus → we score the deviation from the user's actual +behavioral signal → 🟢/🟡/🔴 + a Claude intervention → SMS alert if red. + +``` +frontend (Wesley) React :3000 ── talks only to backend +backend (Jason) FastAPI+SQLite :8001 ── orchestrator, fans out ↓ +signals (Dhruva) FastAPI :8002 ── sentiment + typing + Twilio +ml (Rishith) FastAPI+TRIBE :8003 ── predict · score · intervention +imessage (TBD) AppleScript + chat.db ── optional iMessage delivery (macOS) +``` + +## Quickstart + +```bash +./dev.sh # sets up venvs + node_modules on first run, then starts all 4 +# → http://localhost:3000 +``` + +Optional iMessage channel (macOS only): see [`imessage/README.md`](imessage/README.md). + +API contract: [`shared/contract.md`](shared/contract.md). Agent rules: +[`AGENTS.md`](AGENTS.md) (root) + one per folder. Dispatchable subagents live in +`.claude/agents/`. + ## Branching model ``` diff --git a/backend/.env.example b/backend/.env.example new file mode 100644 index 0000000..37add83 --- /dev/null +++ b/backend/.env.example @@ -0,0 +1,4 @@ +# Backend service (Jason). Copy to .env and adjust. +SIGNALS_URL=http://localhost:8002 +ML_URL=http://localhost:8003 +PEGASUS_DB=pegasus.db diff --git a/backend/AGENTS.md b/backend/AGENTS.md new file mode 100644 index 0000000..26c998f --- /dev/null +++ b/backend/AGENTS.md @@ -0,0 +1,38 @@ +# `/backend` + `/shared` — Jason's agent + +You are **Jason**. You own **`backend/` and `shared/`**. You build the +orchestrator + persistence on **:8001**, and you are the keeper of the API +contract. Read `../AGENTS.md` first. + +## Never touch +`/frontend`, `/ml`, `/signals`. Call their APIs (you already do, in +`orchestrator.py`). + +## Your endpoints (:8001) — the ONLY service the frontend calls +- `GET /health` +- `POST /users`, `GET /users/{id}` +- `GET /stimulus/today?user_id=` → daily stimulus +- `POST /checkin` → fans out to Signals + ML, stores result, alerts on red +- `GET /status/{user_id}` → latest + history + +## Files +- `main.py` — FastAPI routes. +- `orchestrator.py` — calls Signals (:8002) + ML (:8003), degrades gracefully. +- `db.py` — SQLite (stdlib), tables `users` + `checkins`. +- `stimuli.py` — deterministic daily stimulus pool. +- `../shared/contract.{md,py}` — **source of truth.** When you change it, ping + the affected owner. + +## Run +```bash +cd backend && python -m venv .venv && source .venv/bin/activate +pip install -r requirements.txt +uvicorn main:app --reload --port 8001 +``` + +## Git +```bash +git checkout feat/jason-api +git add backend/ shared/ # ONLY these +git commit -m "feat(backend): ..." && git push origin feat/jason-api +``` diff --git a/backend/db.py b/backend/db.py new file mode 100644 index 0000000..8842a2b --- /dev/null +++ b/backend/db.py @@ -0,0 +1,89 @@ +"""SQLite persistence for Pegasus backend. Stdlib only, no ORM.""" +from __future__ import annotations + +import json +import os +import sqlite3 +import uuid +from typing import Dict, List, Optional + +DB_PATH = os.getenv("PEGASUS_DB", os.path.join(os.path.dirname(__file__), "pegasus.db")) + + +def _conn() -> sqlite3.Connection: + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + return conn + + +def init_db() -> None: + with _conn() as c: + c.executescript( + """ + CREATE TABLE IF NOT EXISTS users ( + user_id TEXT PRIMARY KEY, + name TEXT NOT NULL, + phone TEXT + ); + CREATE TABLE IF NOT EXISTS checkins ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + user_id TEXT NOT NULL, + stimulus_id TEXT, + score INTEGER, + level TEXT, + deviation REAL, + signal_json TEXT, + intervention_json TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ); + """ + ) + + +def create_user(name: str, phone: Optional[str]) -> Dict: + user_id = uuid.uuid4().hex[:12] + with _conn() as c: + c.execute( + "INSERT INTO users (user_id, name, phone) VALUES (?, ?, ?)", + (user_id, name, phone), + ) + return {"user_id": user_id, "name": name, "phone": phone} + + +def get_user(user_id: str) -> Optional[Dict]: + with _conn() as c: + row = c.execute("SELECT * FROM users WHERE user_id = ?", (user_id,)).fetchone() + return dict(row) if row else None + + +def save_checkin(user_id: str, stimulus_id: str, score: Dict, signal: Dict, intervention: Dict) -> None: + with _conn() as c: + c.execute( + """INSERT INTO checkins + (user_id, stimulus_id, score, level, deviation, signal_json, intervention_json) + VALUES (?, ?, ?, ?, ?, ?, ?)""", + ( + user_id, + stimulus_id, + score.get("score"), + score.get("level"), + score.get("deviation"), + json.dumps(signal), + json.dumps(intervention), + ), + ) + + +def get_history(user_id: str, limit: int = 30) -> List[Dict]: + with _conn() as c: + rows = c.execute( + "SELECT * FROM checkins WHERE user_id = ? ORDER BY id DESC LIMIT ?", + (user_id, limit), + ).fetchall() + out = [] + for r in rows: + d = dict(r) + d["signal"] = json.loads(d.pop("signal_json") or "{}") + d["intervention"] = json.loads(d.pop("intervention_json") or "{}") + out.append(d) + return out diff --git a/backend/main.py b/backend/main.py new file mode 100644 index 0000000..60e822f --- /dev/null +++ b/backend/main.py @@ -0,0 +1,88 @@ +"""Pegasus Backend — orchestrator + persistence. Frontend talks ONLY to this. + +Run (from inside /backend): + uvicorn main:app --reload --port 8001 +""" +from __future__ import annotations + +from typing import Optional + +from fastapi import FastAPI, HTTPException +from fastapi.middleware.cors import CORSMiddleware +from pydantic import BaseModel + +import db +import orchestrator +import stimuli + +app = FastAPI(title="Pegasus Backend", version="0.1.0") +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], allow_methods=["*"], allow_headers=["*"], +) + + +@app.on_event("startup") +def _startup(): + db.init_db() + + +class UserIn(BaseModel): + name: str + phone: Optional[str] = None + + +class CheckInIn(BaseModel): + user_id: str + stimulus_id: str + text_response: str + response_time_ms: int = 0 + typing_wpm: int = 0 + error_rate: float = 0.0 + + +@app.get("/health") +def health(): + return {"status": "ok"} + + +@app.post("/users") +def create_user(body: UserIn): + return db.create_user(body.name, body.phone) + + +@app.get("/users/{user_id}") +def get_user(user_id: str): + user = db.get_user(user_id) + if not user: + raise HTTPException(404, "user not found") + return user + + +@app.get("/stimulus/today") +def stimulus_today(user_id: str = "demo"): + return stimuli.stimulus_for(user_id) + + +@app.post("/checkin") +def checkin(body: CheckInIn): + user = db.get_user(body.user_id) + phone = user.get("phone") if user else None + stimulus = stimuli.stimulus_for(body.user_id) + result = orchestrator.run_checkin(body.model_dump(), stimulus, phone) + db.save_checkin(body.user_id, body.stimulus_id, + {"score": result["score"], "level": result["level"], "deviation": result["deviation"]}, + result["signal"], result["intervention"]) + return result + + +@app.get("/status/{user_id}") +def status(user_id: str): + history = db.get_history(user_id) + return {"latest": history[0] if history else None, "history": history} + + +if __name__ == "__main__": + import uvicorn + + uvicorn.run(app, host="0.0.0.0", port=8001) diff --git a/backend/orchestrator.py b/backend/orchestrator.py new file mode 100644 index 0000000..d1e63f9 --- /dev/null +++ b/backend/orchestrator.py @@ -0,0 +1,82 @@ +"""Orchestrates a check-in across Signals (:8002) and ML (:8003). + +Each downstream call is defensive: if a service is down, we degrade instead of +500ing the whole pipeline (important for a live demo). +""" +from __future__ import annotations + +import os +from typing import Dict + +import httpx + +SIGNALS_URL = os.getenv("SIGNALS_URL", "http://localhost:8002") +ML_URL = os.getenv("ML_URL", "http://localhost:8003") +TIMEOUT = float(os.getenv("PEGASUS_HTTP_TIMEOUT", "30")) + + +def _post(url: str, payload: Dict) -> Dict: + r = httpx.post(url, json=payload, timeout=TIMEOUT) + r.raise_for_status() + return r.json() + + +def run_checkin(checkin: Dict, stimulus: Dict, phone: str | None) -> Dict: + # 1. Behavioral signal (Signals service) + try: + signal = _post(f"{SIGNALS_URL}/signals/analyze", { + "user_id": checkin["user_id"], + "text": checkin.get("text_response", ""), + "typing_wpm": checkin.get("typing_wpm", 0), + "error_rate": checkin.get("error_rate", 0), + "response_time_ms": checkin.get("response_time_ms", 0), + }) + except Exception: + signal = {"sentiment_score": 0.5, "energy_level": "medium", + "flags": ["signals_unavailable"], "combined_signal_score": 0.5} + + # 2. Healthy-brain prediction (ML / TRIBE v2) + try: + prediction = _post(f"{ML_URL}/predict", {"stimulus": stimulus}) + except Exception: + prediction = {"predicted_engagement": 0.7, "predicted_valence": 0.7, "activation_summary": []} + + # 3. Deviation -> burnout score (ML) + try: + burnout = _post(f"{ML_URL}/score", { + "stimulus": stimulus, "prediction": prediction, + "signal": signal, "checkin": checkin, + }) + except Exception: + burnout = {"score": 0, "level": "green", "deviation": 0.0, "components": {}} + + # 4. Intervention (ML / Claude) + try: + intervention = _post(f"{ML_URL}/intervention", { + "burnout": burnout, "recent_signals": [signal], + }) + except Exception: + intervention = {"message": "Check-in recorded.", "suggested_action": "Take a short break."} + + # 5. Alert on red (Signals / Twilio) + alerted = False + if burnout.get("level") == "red" and phone: + try: + res = _post(f"{SIGNALS_URL}/alert", { + "user_id": checkin["user_id"], "phone": phone, + "score": burnout.get("score"), "level": "red", + "intervention": intervention.get("message", ""), + }) + alerted = bool(res.get("sent")) + except Exception: + alerted = False + + return { + "score": burnout.get("score"), + "level": burnout.get("level"), + "deviation": burnout.get("deviation"), + "components": burnout.get("components", {}), + "signal": signal, + "intervention": intervention, + "alerted": alerted, + } diff --git a/backend/requirements.txt b/backend/requirements.txt new file mode 100644 index 0000000..1a4e6e0 --- /dev/null +++ b/backend/requirements.txt @@ -0,0 +1,5 @@ +fastapi==0.115.6 +uvicorn[standard]==0.34.0 +pydantic==2.10.4 +httpx==0.28.1 +python-dotenv==1.0.1 diff --git a/backend/stimuli.py b/backend/stimuli.py new file mode 100644 index 0000000..ab2cad0 --- /dev/null +++ b/backend/stimuli.py @@ -0,0 +1,32 @@ +"""Daily stimulus pool. Picks one stimulus per user per day, deterministically +(so a user sees the same prompt all day, and the demo is reproducible).""" +from __future__ import annotations + +import datetime +import hashlib +from typing import Dict, List + +POOL: List[Dict] = [ + {"stimulus_id": "s1", "type": "text", + "content": "A quiet morning, steam rising off a coffee cup by a rain-streaked window.", + "prompt": "Describe what this scene makes you feel, in a few sentences."}, + {"stimulus_id": "s2", "type": "text", + "content": "An empty inbox and a clear calendar for the rest of the afternoon.", + "prompt": "What's the first thing you'd do with this time?"}, + {"stimulus_id": "s3", "type": "text", + "content": "A friend texts: 'Haven't heard from you in a while — you good?'", + "prompt": "Write the reply you'd actually send right now."}, + {"stimulus_id": "s4", "type": "text", + "content": "Sunlight on a trail, the trees just starting to turn.", + "prompt": "What would you want to be doing in this moment?"}, + {"stimulus_id": "s5", "type": "text", + "content": "You just finished something you'd been putting off for weeks.", + "prompt": "How does that land for you? Say more than 'good'."}, +] + + +def stimulus_for(user_id: str, day: datetime.date | None = None) -> Dict: + day = day or datetime.date.today() + seed = f"{user_id}|{day.isoformat()}" + idx = int(hashlib.sha256(seed.encode()).hexdigest(), 16) % len(POOL) + return POOL[idx] diff --git a/dev.sh b/dev.sh new file mode 100755 index 0000000..85bf9e7 --- /dev/null +++ b/dev.sh @@ -0,0 +1,46 @@ +#!/usr/bin/env bash +# Start all of Pegasus locally. First run sets up venvs + node_modules. +# ./dev.sh # set up (if needed) + run everything +# ./dev.sh setup # just install deps, don't run +set -euo pipefail +cd "$(dirname "$0")" + +PIDS=() +cleanup() { echo; echo "Stopping…"; for p in "${PIDS[@]:-}"; do kill "$p" 2>/dev/null || true; done; } +trap cleanup EXIT INT TERM + +py_service() { # name folder port + local name=$1 folder=$2 port=$3 + pushd "$folder" >/dev/null + [ -d .venv ] || python3 -m venv .venv + ./.venv/bin/pip install -q -r requirements.txt + if [ "${1:-}" != "__setup__" ] && [ "$RUN" = "1" ]; then + echo "→ $name on :$port" + ./.venv/bin/uvicorn main:app --port "$port" & + PIDS+=($!) + fi + popd >/dev/null +} + +RUN=1 +[ "${1:-}" = "setup" ] && RUN=0 + +py_service ml ml 8003 +py_service signals signals 8002 +py_service backend backend 8001 + +pushd frontend >/dev/null +[ -d node_modules ] || npm install +if [ "$RUN" = "1" ]; then + echo "→ frontend on :3000" + npm run dev & + PIDS+=($!) +fi +popd >/dev/null + +[ "$RUN" = "0" ] && { echo "Setup done. Run ./dev.sh to start."; exit 0; } + +echo +echo "Pegasus up: frontend http://localhost:3000 · backend :8001 · signals :8002 · ml :8003" +echo "Ctrl-C to stop." +wait diff --git a/frontend/AGENTS.md b/frontend/AGENTS.md new file mode 100644 index 0000000..c9acf1b --- /dev/null +++ b/frontend/AGENTS.md @@ -0,0 +1,34 @@ +# `/frontend` — Wesley's agent + +You are **Wesley**. You own **`frontend/` only**. You build the React UI on +**:3000**. Read `../AGENTS.md` and `../shared/contract.md` first. + +## Never touch +`/backend`, `/ml`, `/signals`, `/shared`. You talk to **the backend only** +(`:8001`), through the Vite `/api` proxy (already configured). + +## What the UI does +1. Get/create a user, fetch `GET /api/stimulus/today`. +2. Show the stimulus; capture the response with `KeystrokeTracker` + (`src/keystroke.js`, synced from `/signals/collector.js`). +3. `POST /api/checkin` with text + typing metrics. +4. Render the Check Engine Light (🟢/🟡/🔴) + intervention (`EngineLight.jsx`). + +## Files +- `src/App.jsx` — flow + state. +- `src/api.js` — backend client (all calls via `/api`). +- `src/EngineLight.jsx` — result card. +- `src/keystroke.js` — typing tracker (keep in sync with Dhruva's `collector.js`). +- `vite.config.js` — `/api` → `:8001` proxy. + +## Run +```bash +cd frontend && npm install && npm run dev # http://localhost:3000 +``` + +## Git +```bash +git checkout feat/wesley-ui +git add frontend/ # ONLY frontend/ +git commit -m "feat(frontend): ..." && git push origin feat/wesley-ui +``` diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000..b01f724 --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,12 @@ + + + + + + Pegasus — Check Engine Light + + +
+ + + diff --git a/frontend/package.json b/frontend/package.json new file mode 100644 index 0000000..19d12b6 --- /dev/null +++ b/frontend/package.json @@ -0,0 +1,19 @@ +{ + "name": "pegasus-frontend", + "private": true, + "version": "0.1.0", + "type": "module", + "scripts": { + "dev": "vite --port 3000", + "build": "vite build", + "preview": "vite preview --port 3000" + }, + "dependencies": { + "react": "^18.3.1", + "react-dom": "^18.3.1" + }, + "devDependencies": { + "@vitejs/plugin-react": "^4.3.4", + "vite": "^6.0.7" + } +} diff --git a/frontend/src/App.jsx b/frontend/src/App.jsx new file mode 100644 index 0000000..0214a44 --- /dev/null +++ b/frontend/src/App.jsx @@ -0,0 +1,96 @@ +import { useEffect, useRef, useState } from "react"; +import { api } from "./api.js"; +import { KeystrokeTracker } from "./keystroke.js"; +import EngineLight from "./EngineLight.jsx"; + +export default function App() { + const [userId, setUserId] = useState(localStorage.getItem("pegasus_uid") || ""); + const [stimulus, setStimulus] = useState(null); + const [text, setText] = useState(""); + const [result, setResult] = useState(null); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(null); + const tracker = useRef(new KeystrokeTracker()); + + // Ensure we have a user + today's stimulus. + useEffect(() => { + (async () => { + try { + let uid = userId; + if (!uid) { + const u = await api.createUser("Demo User", null); + uid = u.user_id; + localStorage.setItem("pegasus_uid", uid); + setUserId(uid); + } + setStimulus(await api.todayStimulus(uid)); + } catch (e) { + setError("Backend not reachable on :8001 — start it and refresh."); + } + })(); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, []); + + async function submit() { + if (!text.trim() || !stimulus) return; + setLoading(true); + setError(null); + const metrics = tracker.current.getMetrics(); + try { + const res = await api.checkin({ + user_id: userId, + stimulus_id: stimulus.stimulus_id, + text_response: text, + ...metrics, + }); + setResult(res); + } catch (e) { + setError("Check-in failed — is the backend running?"); + } finally { + setLoading(false); + } + } + + return ( +
+
+

Pegasus

+

A check-engine light for your mind.

+
+ + {error &&
{error}
} + + {stimulus && !result && ( +
+
{stimulus.content}
+

{stimulus.prompt}

+ +
+ + +
+
+ +
+

💬 Companion + 🔊 Voice

+ + +
+ + + +
+ +
+ +

Output

ready.
+ +""" + + +if __name__ == "__main__": + import uvicorn + + uvicorn.run(app, host="0.0.0.0", port=4000) diff --git a/face-emotion-api/README.md b/face-emotion-api/README.md new file mode 100644 index 0000000..2260f42 --- /dev/null +++ b/face-emotion-api/README.md @@ -0,0 +1,87 @@ +# Pegasus Face‑Emotion + +A small, reusable framework + HTTP API for **facial affect and multimodal emotion +detection**, built for [Pegasus](../README.md) (our mental‑health "check engine +light"). It turns a face — and optionally a voice — into a compact, product‑ready +read: the dominant emotion, the full distribution, valence/arousal, and a +**healthy‑vs‑distress** affect score. + +We extracted this from Pegasus's video pipeline so it can be dropped into any +wellbeing app that needs a quiet, passive emotion signal. + +## Why + +Most emotion APIs hand you raw class probabilities. Wellbeing apps need a +*single, calm signal* — "is this person reading as regulated or distressed?" — +plus the nuance to tell a forced smile from a genuine one. Pegasus Face‑Emotion +maps the raw FER‑7 distribution into valence/arousal and a stress score, and +fuses it with voice acoustics so the two modalities corroborate each other. + +## Install + +```bash +pip install -r requirements.txt +export HF_TOKEN=hf_... # a Hugging Face token +``` + +## Library + +```python +from face_emotion import EmotionDetector, fuse + +det = EmotionDetector() # default: trpakov/vit-face-expression +face = det.detect("selfie.jpg") # bytes | path | URL +# {'dominant_emotion': 'sad', 'valence': -0.41, 'arousal': 0.38, +# 'stress_score': 71, 'health_alignment': 0.29, 'emotions': {...}, ...} + +clip = det.detect_frames([f1, f2, f3]) # aggregate across video frames + +read = fuse(face, voice={"pitch_variability": 72, "voice_tremor": True}) +# {'mood': 'negative', 'stress_score': 78, 'signals': ['face:sad', 'voice:shaky'], ...} +``` + +## API + +```bash +uvicorn api:app --port 8010 +``` + +| Method | Path | Body | Returns | +|--------|------------------|----------------------------|--------------------------| +| POST | `/detect` | multipart `image` | emotion read | +| POST | `/detect/frames` | multipart `images` (many) | aggregated read | +| POST | `/fuse` | json `{face, voice}` | multimodal mood + stress | +| GET | `/health` | — | status | + +```bash +curl -F image=@selfie.jpg http://localhost:8010/detect +``` + +## How it works + +1. **Classify** — a Vision Transformer fine‑tuned for facial expression + (Hugging Face `trpakov/vit-face-expression`, FER‑7) gives a probability over + `angry · disgust · fear · happy · neutral · sad · surprise`. +2. **Map** — those probabilities are projected onto **valence** (pleasant↔unpleasant) + and **arousal** (calm↔activated), and onto a **healthy‑affect** vs **distress‑affect** + mass → a 0–100 stress score and a `health_alignment`. +3. **Fuse** — `fuse()` blends the facial stress with voice acoustics (pitch + variability + tremor), facial affect leading (0.6) and voice supporting (0.4). + +Everything degrades gracefully: a missing model/face yields an `unknown` read +rather than an exception. + +## Layout + +``` +face-emotion-api/ +├── api.py # FastAPI service +├── face_emotion/ +│ ├── __init__.py +│ ├── detector.py # EmotionDetector — classify + map +│ └── fusion.py # fuse() — face + voice +├── requirements.txt +└── pyproject.toml +``` + +Built by the Pegasus team — Rishith, Wesley, Dhruva and Jason. diff --git a/face-emotion-api/api.py b/face-emotion-api/api.py new file mode 100644 index 0000000..83bde70 --- /dev/null +++ b/face-emotion-api/api.py @@ -0,0 +1,60 @@ +"""Pegasus Face-Emotion API — a thin HTTP service over the framework. + + uvicorn api:app --port 8010 + + POST /detect multipart 'image' -> emotion read + POST /detect/frames multipart 'images' (several) -> aggregated read + POST /fuse json {face, voice} -> multimodal read + GET /health +""" +from __future__ import annotations + +from typing import List, Optional + +from fastapi import FastAPI, File, UploadFile +from fastapi.middleware.cors import CORSMiddleware +from pydantic import BaseModel + +from face_emotion import EmotionDetector, fuse + +app = FastAPI(title="Pegasus Face-Emotion API", version="0.1.0") +app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"]) + +detector = EmotionDetector() + + +@app.get("/health") +def health(): + return {"status": "ok", "model": detector.model} + + +@app.post("/detect") +async def detect(image: UploadFile = File(...)): + return detector.detect(await image.read()) + + +@app.post("/detect/frames") +async def detect_frames(images: List[UploadFile] = File(...)): + frames = [await im.read() for im in images] + return detector.detect_frames(frames) + + +class Voice(BaseModel): + pitch_variability: float = 0.0 + voice_tremor: bool = False + + +class FuseReq(BaseModel): + face: dict + voice: Optional[Voice] = None + + +@app.post("/fuse") +def fuse_route(req: FuseReq): + return fuse(req.face, req.voice.dict() if req.voice else None) + + +if __name__ == "__main__": + import uvicorn + + uvicorn.run(app, host="0.0.0.0", port=8010) diff --git a/face-emotion-api/face_emotion/__init__.py b/face-emotion-api/face_emotion/__init__.py new file mode 100644 index 0000000..babdfa4 --- /dev/null +++ b/face-emotion-api/face_emotion/__init__.py @@ -0,0 +1,14 @@ +"""Pegasus Face-Emotion — a small framework for facial affect + multimodal +emotion detection, built for the Pegasus mental-health app. + + from face_emotion import EmotionDetector, fuse + + det = EmotionDetector() + face = det.detect("selfie.jpg") # dominant emotion + valence/arousal + stress + read = fuse(face, voice={"pitch_variability": 70, "voice_tremor": True}) +""" +from .detector import EmotionDetector +from .fusion import fuse + +__all__ = ["EmotionDetector", "fuse"] +__version__ = "0.1.0" diff --git a/face-emotion-api/face_emotion/detector.py b/face-emotion-api/face_emotion/detector.py new file mode 100644 index 0000000..8a4a483 --- /dev/null +++ b/face-emotion-api/face_emotion/detector.py @@ -0,0 +1,101 @@ +"""EmotionDetector — the core of the Pegasus Face-Emotion framework. + +Classifies facial affect from an image with a ViT face-expression model +(Hugging Face, default ``trpakov/vit-face-expression``, FER-7 labels: angry, +disgust, fear, happy, neutral, sad, surprise) and maps it to a compact read: +the dominant emotion, the full distribution, a valence/arousal estimate, and a +"healthy vs. distress" affect score for wellbeing apps. + +Single image or a batch of frames (a short video). Degrades gracefully — an +empty/unknown read rather than an exception — so a product never hard-fails. +""" +from __future__ import annotations + +import os +from collections import defaultdict +from typing import Dict, List, Optional + +FACE_MODEL = os.getenv("FACE_EMOTION_MODEL", "trpakov/vit-face-expression") +HF_TOKEN = os.getenv("HF_TOKEN") or os.getenv("HF_API_KEY") + +# FER label → valence (unpleasant -1 .. pleasant 1) and arousal (calm 0 .. 1). +_VALENCE = {"happy": 0.9, "surprise": 0.3, "neutral": 0.0, + "sad": -0.8, "fear": -0.7, "angry": -0.8, "disgust": -0.6} +_AROUSAL = {"happy": 0.6, "surprise": 0.85, "neutral": 0.2, + "sad": 0.4, "fear": 0.9, "angry": 0.85, "disgust": 0.6} +# A regulated, healthy face reads neutral/positive; distress reads negative. +_HEALTHY = {"happy": 1.0, "neutral": 0.85, "surprise": 0.55} +_STRESS = {"sad": 1.0, "fear": 1.0, "angry": 0.9, "disgust": 0.8} + + +class EmotionDetector: + """Detect facial emotion + affect from images. Thread-safe to construct once + and reuse (lazy Hugging Face client).""" + + def __init__(self, model: str = FACE_MODEL, token: Optional[str] = HF_TOKEN) -> None: + self.model = model + self._token = token + self._client = None + + def _hf(self): + if self._client is None: + from huggingface_hub import InferenceClient + + self._client = InferenceClient(token=self._token) + return self._client + + # --- public ------------------------------------------------------------ + def detect(self, image) -> Dict: + """`image`: raw bytes, a file path, or a URL. Returns the emotion read.""" + return self._summarize(self._classify(image)) + + def detect_frames(self, images: List) -> Dict: + """Aggregate the emotion read across many frames (e.g. a short clip).""" + agg: Dict[str, float] = defaultdict(float) + n = 0 + for im in images: + dist = self._classify(im) + if dist: + for label, score in dist.items(): + agg[label] += score + n += 1 + if n == 0: + return self._empty() + dist = {k: round(v / n, 4) for k, v in agg.items()} + return self._summarize(dist, frames=n) + + # --- internals --------------------------------------------------------- + def _classify(self, image) -> Dict[str, float]: + try: + res = self._hf().image_classification(image, model=self.model) + return {r.label.lower(): float(r.score) for r in res} + except Exception: + return {} + + def _summarize(self, dist: Dict[str, float], frames: int = 1) -> Dict: + if not dist: + return self._empty() + dominant = max(dist, key=dist.get) + valence = sum(dist.get(k, 0.0) * w for k, w in _VALENCE.items()) + arousal = sum(dist.get(k, 0.0) * w for k, w in _AROUSAL.items()) + stress = sum(dist.get(k, 0.0) * w for k, w in _STRESS.items()) + healthy = sum(dist.get(k, 0.0) * w for k, w in _HEALTHY.items()) + total = stress + healthy + 1e-6 + return { + "dominant_emotion": dominant, + "emotions": dist, + "valence": round(valence, 3), + "arousal": round(arousal, 3), + "stress_score": int(round(min(stress / total, 1.0) * 100)), + "health_alignment": round(healthy / total, 3), + "frames": frames, + "model": self.model, + } + + @staticmethod + def _empty() -> Dict: + return { + "dominant_emotion": "unknown", "emotions": {}, "valence": 0.0, + "arousal": 0.0, "stress_score": 0, "health_alignment": 0.0, + "frames": 0, "model": FACE_MODEL, + } diff --git a/face-emotion-api/face_emotion/fusion.py b/face-emotion-api/face_emotion/fusion.py new file mode 100644 index 0000000..1a2d2a3 --- /dev/null +++ b/face-emotion-api/face_emotion/fusion.py @@ -0,0 +1,34 @@ +"""Multimodal fusion — combine the facial emotion read with voice acoustics +(pitch variability, tremor) into a single affect + stress estimate. + +Faces and voices each leak stress in different ways; fusing them is more robust +than either alone (a forced smile with a shaky voice is not "happy"). +""" +from __future__ import annotations + +from typing import Dict, List, Optional + + +def fuse(face: Dict, voice: Optional[Dict] = None) -> Dict: + """`face` from EmotionDetector.detect(); `voice` optional dict with + ``pitch_variability`` (Hz) and ``voice_tremor`` (bool). Returns a unified read. + Facial affect leads (0.6); voice arousal supports (0.4).""" + stress = float(face.get("stress_score", 0)) + signals: List[str] = [f"face:{face.get('dominant_emotion', 'unknown')}"] + + if voice: + pitch_var = float(voice.get("pitch_variability", 0) or 0) + tremor = bool(voice.get("voice_tremor", False)) + voice_stress = (40.0 if tremor else 0.0) + min(pitch_var / 100.0, 1.0) * 40.0 + stress = stress * 0.6 + voice_stress * 0.4 + signals.append("voice:" + ("shaky" if tremor else "tense" if pitch_var > 55 else "steady")) + + valence = float(face.get("valence", 0)) + mood = "positive" if valence > 0.2 else "negative" if valence < -0.2 else "neutral" + return { + "mood": mood, + "stress_score": int(round(min(max(stress, 0), 100))), + "valence": valence, + "arousal": float(face.get("arousal", 0)), + "signals": signals, + } diff --git a/face-emotion-api/pyproject.toml b/face-emotion-api/pyproject.toml new file mode 100644 index 0000000..b0dbfc9 --- /dev/null +++ b/face-emotion-api/pyproject.toml @@ -0,0 +1,20 @@ +[build-system] +requires = ["setuptools>=68"] +build-backend = "setuptools.build_meta" + +[project] +name = "pegasus-face-emotion" +version = "0.1.0" +description = "Facial affect + multimodal emotion detection framework (built for Pegasus)" +readme = "README.md" +requires-python = ">=3.10" +authors = [{ name = "Pegasus team — Rishith, Wesley, Dhruva, Jason" }] +dependencies = [ + "huggingface-hub>=0.27", +] + +[project.optional-dependencies] +api = ["fastapi>=0.115", "uvicorn[standard]>=0.34", "python-multipart>=0.0.20", "pydantic>=2.10"] + +[tool.setuptools] +packages = ["face_emotion"] diff --git a/face-emotion-api/requirements.txt b/face-emotion-api/requirements.txt new file mode 100644 index 0000000..a8b4e83 --- /dev/null +++ b/face-emotion-api/requirements.txt @@ -0,0 +1,5 @@ +fastapi==0.115.6 +uvicorn[standard]==0.34.0 +pydantic==2.10.4 +huggingface-hub==0.27.0 +python-multipart==0.0.20 diff --git a/frontend/.npmrc b/frontend/.npmrc new file mode 100644 index 0000000..521a9f7 --- /dev/null +++ b/frontend/.npmrc @@ -0,0 +1 @@ +legacy-peer-deps=true diff --git a/frontend/App.tsx b/frontend/App.tsx index 2186209..b238d1d 100644 --- a/frontend/App.tsx +++ b/frontend/App.tsx @@ -1,5 +1,5 @@ // Pegasus app root. Owned by Wesley (visual layer). -// Wires the dark navigation theme, gesture handler, and safe-area providers +// Wires the light navigation theme, gesture handler, and safe-area providers // around the bottom tab navigator. Screens live under src/screens. import "react-native-gesture-handler"; import React from "react"; @@ -8,7 +8,7 @@ import { GestureHandlerRootView } from "react-native-gesture-handler"; import { SafeAreaProvider } from "react-native-safe-area-context"; import { NavigationContainer, - DarkTheme, + DefaultTheme, Theme, } from "@react-navigation/native"; @@ -16,11 +16,11 @@ import TabNavigator from "./src/navigation/TabNavigator"; import { COLORS } from "./src/utils/colors"; const navTheme: Theme = { - ...DarkTheme, - dark: true, + ...DefaultTheme, + dark: false, colors: { - ...DarkTheme.colors, - primary: COLORS.green, + ...DefaultTheme.colors, + primary: COLORS.blue, background: COLORS.bg, card: COLORS.card, text: COLORS.text, @@ -36,7 +36,7 @@ export default function App() { - + ); diff --git a/frontend/babel.config.js b/frontend/babel.config.js index 2d08334..c095986 100644 --- a/frontend/babel.config.js +++ b/frontend/babel.config.js @@ -2,7 +2,8 @@ module.exports = function (api) { api.cache(true); return { presets: ["babel-preset-expo"], - // react-native-reanimated/plugin MUST be listed last. - plugins: ["react-native-reanimated/plugin"], + // Reanimated 4 moved its Babel transform into react-native-worklets. + // This plugin MUST be listed last. + plugins: ["react-native-worklets/plugin"], }; }; diff --git a/frontend/metro.config.js b/frontend/metro.config.js new file mode 100644 index 0000000..0291f6d --- /dev/null +++ b/frontend/metro.config.js @@ -0,0 +1,9 @@ +// Metro config — extend Expo's default so the real 3D brain mesh bundles. +// expo-three / GLTFLoader load assets/brain/fsaverage5.glb via require(), and +// Metro won't bundle .glb/.gltf/.bin unless we add them to assetExts. +const { getDefaultConfig } = require("expo/metro-config"); + +const config = getDefaultConfig(__dirname); +config.resolver.assetExts.push("glb", "gltf", "bin"); + +module.exports = config; diff --git a/frontend/package-lock.json b/frontend/package-lock.json new file mode 100644 index 0000000..f66a52f --- /dev/null +++ b/frontend/package-lock.json @@ -0,0 +1,10088 @@ +{ + "name": "pegasus", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "pegasus", + "version": "1.0.0", + "dependencies": { + "@expo/ngrok": "^4.1.3", + "@react-navigation/bottom-tabs": "^6.6.1", + "@react-navigation/native": "^6.1.18", + "expo": "^54.0.35", + "expo-asset": "~12.0.13", + "expo-av": "~16.0.8", + "expo-blur": "~15.0.8", + "expo-camera": "~17.0.10", + "expo-file-system": "~19.0.23", + "expo-gl": "~16.0.10", + "expo-haptics": "~15.0.8", + "expo-linear-gradient": "~15.0.8", + "expo-status-bar": "~3.0.9", + "expo-three": "^8.0.0", + "react": "19.1.0", + "react-native": "0.81.5", + "react-native-chart-kit": "^6.12.3", + "react-native-gesture-handler": "~2.28.0", + "react-native-reanimated": "~4.1.1", + "react-native-safe-area-context": "~5.6.0", + "react-native-screens": "~4.16.0", + "react-native-svg": "15.12.1", + 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"expo": "~51.0.28", - "expo-asset": "~10.0.10", - "expo-av": "~14.0.7", - "expo-camera": "~15.0.16", - "expo-file-system": "~17.0.1", - "expo-gl": "~14.0.2", - "expo-haptics": "~13.0.1", - "expo-linear-gradient": "~13.0.2", - "expo-status-bar": "~1.12.1", + "expo": "^54.0.35", + "expo-asset": "~12.0.13", + "expo-av": "~16.0.8", + "expo-blur": "~15.0.8", + "expo-camera": "~17.0.10", + "expo-file-system": "~19.0.23", + "expo-gl": "~16.0.10", + "expo-haptics": "~15.0.8", + "expo-linear-gradient": "~15.0.8", + "expo-status-bar": "~3.0.9", "expo-three": "^8.0.0", - "react": "18.2.0", - "react-native": "0.74.5", + "react": "19.1.0", + "react-native": "0.81.5", "react-native-chart-kit": "^6.12.3", - "react-native-gesture-handler": "~2.16.1", - "react-native-reanimated": "~3.10.1", - "react-native-safe-area-context": "4.10.5", - "react-native-screens": "3.31.1", - "react-native-svg": "15.2.0", + "react-native-gesture-handler": "~2.28.0", + "react-native-reanimated": "~4.1.1", + "react-native-safe-area-context": "~5.6.0", + "react-native-screens": "~4.16.0", + "react-native-svg": "15.12.1", + "react-native-worklets": "0.5.1", "three": "^0.166.1" }, "devDependencies": { "@babel/core": "^7.24.0", - "@types/react": "~18.2.45", + "@types/react": "~19.1.10", "@types/three": "^0.166.0", - "babel-preset-expo": "~11.0.0", - "typescript": "~5.3.3" + "babel-preset-expo": "~54.0.10", + "typescript": "~5.9.2" } } diff --git a/frontend/src/components/AlertOverlay.tsx b/frontend/src/components/AlertOverlay.tsx index 5804884..139049d 100644 --- a/frontend/src/components/AlertOverlay.tsx +++ b/frontend/src/components/AlertOverlay.tsx @@ -153,7 +153,7 @@ export default function AlertOverlay({ {/* Breathing red radial glow filling the screen. */} blue (#3b82f6) -> yellow (#eab308) -> red (#ef4444) +// activation 0..1 -> blue (#0071e3) -> amber (#FF9F0A) -> red (#FF3B30) function activationColor(a: number): THREE.Color { const c = new THREE.Color(); const t = Math.max(0, Math.min(1, a)); - if (t < 0.5) c.lerpColors(new THREE.Color("#3b82f6"), new THREE.Color("#eab308"), t * 2); - else c.lerpColors(new THREE.Color("#eab308"), new THREE.Color("#ef4444"), (t - 0.5) * 2); + if (t < 0.5) c.lerpColors(new THREE.Color(COLORS.blue), new THREE.Color(COLORS.yellow), t * 2); + else c.lerpColors(new THREE.Color(COLORS.yellow), new THREE.Color(COLORS.red), (t - 0.5) * 2); return c; } @@ -96,7 +96,7 @@ export default function Brain3D({ regions, size = 280 }: { regions?: Regions; si brain.scale.setScalar(1.6 / (sphere.radius || 1)); brain.traverse((c: any) => { if (c.isMesh) { - c.material = new THREE.MeshStandardMaterial({ color: 0x3b82f6, roughness: 0.6, metalness: 0.1 }); + c.material = new THREE.MeshStandardMaterial({ color: 0x0071e3, roughness: 0.6, metalness: 0.1 }); } }); root.add(brain); @@ -147,7 +147,7 @@ function proceduralBrain(): THREE.Points { } const geo = new THREE.BufferGeometry(); geo.setAttribute("position", new THREE.BufferAttribute(positions, 3)); - return new THREE.Points(geo, new THREE.PointsMaterial({ color: 0x3b82f6, size: 0.02 })); + return new THREE.Points(geo, new THREE.PointsMaterial({ color: 0x0071e3, size: 0.025 })); } const styles = StyleSheet.create({ diff --git a/frontend/src/components/CheckEngine.tsx b/frontend/src/components/CheckEngine.tsx index 5696b60..8ed7ccf 100644 --- a/frontend/src/components/CheckEngine.tsx +++ b/frontend/src/components/CheckEngine.tsx @@ -1,5 +1,6 @@ -// CheckEngine — the hero component. A large, living, pulsing score orb. -// "Tesla dashboard for your mind": glowing ring, breathing pulse, calm numerals. +// CheckEngine — the hero component. A large, living, pulsing wellness orb. +// Score is 0..100 where higher = better. Glowing ring, breathing pulse, calm +// numerals. "Tesla dashboard for your mind." import React, { useEffect } from "react"; import { StyleSheet, Text, View } from "react-native"; import Animated, { @@ -188,14 +189,15 @@ export default function CheckEngine({ score, level, size = 260 }: CheckEnginePro ); } +// Wellness label — higher score is better. Green thriving, red running low. function labelFor(level: BurnoutLevel): string { switch (level) { case "green": - return "CALM"; + return "THRIVING"; case "yellow": - return "ELEVATED"; + return "STEADY"; case "red": - return "BURNOUT RISK"; + return "RUNNING LOW"; default: return ""; } diff --git a/frontend/src/components/IndicatorCard.tsx b/frontend/src/components/IndicatorCard.tsx index b5816a3..78759cd 100644 --- a/frontend/src/components/IndicatorCard.tsx +++ b/frontend/src/components/IndicatorCard.tsx @@ -47,7 +47,12 @@ const styles = StyleSheet.create({ borderColor: COLORS.border, borderRadius: RADIUS.md, paddingVertical: SPACING.md, - paddingHorizontal: SPACING.md, + paddingHorizontal: SPACING.md + 2, + shadowColor: "#000", + shadowOpacity: 0.06, + shadowRadius: 12, + shadowOffset: { width: 0, height: 4 }, + elevation: 1, }, dot: { width: 10, diff --git a/frontend/src/components/InterventionCard.tsx b/frontend/src/components/InterventionCard.tsx index e72bf63..dd76a02 100644 --- a/frontend/src/components/InterventionCard.tsx +++ b/frontend/src/components/InterventionCard.tsx @@ -39,7 +39,7 @@ export default function InterventionCard({ text }: Props) { return ( ; + progress: SharedValue; color: string; size: number; // Phase offset in [0,1) so rings don't all pulse in unison. diff --git a/frontend/src/components/ScoreBreakdown.tsx b/frontend/src/components/ScoreBreakdown.tsx index 52a8e70..9fa3ea3 100644 --- a/frontend/src/components/ScoreBreakdown.tsx +++ b/frontend/src/components/ScoreBreakdown.tsx @@ -1,9 +1,10 @@ -// ScoreBreakdown — horizontal bars for the 5 burnout streams. +// ScoreBreakdown — horizontal bars for the 5 signal streams. // Owned by Wesley (visual layer). "Tesla dashboard for your mind." // -// Each labeled bar fills 0..100 and is colored by value (green → yellow → red), -// so a glance reads strain consistently with the rest of the app. Bars animate -// their width in on mount / when values change. +// Each bar shows how much *strain* a single signal is contributing (higher = +// more strain), so it reads independently of the overall wellness score. Bars +// fill 0..100, colored low→high (green → yellow → red), and animate their width +// in on mount / when values change. import React, { useEffect, useMemo } from "react"; import { StyleSheet, Text, View } from "react-native"; @@ -122,6 +123,11 @@ const styles = StyleSheet.create({ borderWidth: 1, borderRadius: RADIUS.lg, padding: SPACING.lg, + shadowColor: "#000", + shadowOpacity: 0.06, + shadowRadius: 16, + shadowOffset: { width: 0, height: 6 }, + elevation: 2, }, title: { ...TYPE.label, @@ -150,7 +156,7 @@ const styles = StyleSheet.create({ track: { height: 8, borderRadius: RADIUS.pill, - backgroundColor: COLORS.border, + backgroundColor: "rgba(0,0,0,0.06)", overflow: "hidden", }, fill: { diff --git a/frontend/src/components/ScrollEdgeFade.tsx b/frontend/src/components/ScrollEdgeFade.tsx new file mode 100644 index 0000000..656eb0f --- /dev/null +++ b/frontend/src/components/ScrollEdgeFade.tsx @@ -0,0 +1,89 @@ +// ScrollEdgeFade — the soft frosted bands at the top and bottom of a scroll +// view, the way iOS large-title screens and tab bars blur the content that +// slides under them. Two translucent BlurView overlays pinned to the top and +// bottom edges, each grounded with a gentle gradient wash so content dissolves +// rather than hard-clipping. Purely decorative: pointerEvents is disabled so +// touches pass straight through to the ScrollView underneath. +// +// Render this as a sibling *after* the ScrollView, inside a flex:1 container. +import React from "react"; +import { StyleSheet, View } from "react-native"; +import { BlurView } from "expo-blur"; +import { LinearGradient } from "expo-linear-gradient"; + +import { COLORS } from "../utils/colors"; + +interface ScrollEdgeFadeProps { + // Safe-area top inset; the top band extends a little past it. + topInset: number; + // How far the top band reaches below the safe-area top. Keep it small and + // Apple-subtle. + topExtra?: number; + // Height of the bottom band sitting above the tab bar. + bottomHeight?: number; + // BlurView strength. ~20–40 reads as a soft frost without smearing content. + intensity?: number; +} + +// Convert a hex color to rgba with the given alpha (for the grounding washes). +function withAlpha(hex: string, alpha: number): string { + const h = hex.replace("#", ""); + const r = parseInt(h.slice(0, 2), 16); + const g = parseInt(h.slice(2, 4), 16); + const b = parseInt(h.slice(4, 6), 16); + return `rgba(${r}, ${g}, ${b}, ${alpha})`; +} + +export default function ScrollEdgeFade({ + topInset, + topExtra = 16, + bottomHeight = 32, + intensity = 28, +}: ScrollEdgeFadeProps) { + const topHeight = topInset + topExtra; + + return ( + <> + {/* Top frosted band — content blurs as it scrolls under the status bar. */} + + + {/* Opaque at the very top, dissolving downward so content slides under. */} + + + + {/* Bottom frosted band — content blurs above the tab bar. */} + + + {/* Transparent up top, grounding into the canvas at the bottom edge. */} + + + + ); +} + +const styles = StyleSheet.create({ + band: { + position: "absolute", + left: 0, + right: 0, + overflow: "hidden", + }, + top: { + top: 0, + }, + bottom: { + bottom: 0, + }, +}); diff --git a/frontend/src/components/SmsCheckinCard.tsx b/frontend/src/components/SmsCheckinCard.tsx new file mode 100644 index 0000000..2d06fed --- /dev/null +++ b/frontend/src/components/SmsCheckinCard.tsx @@ -0,0 +1,147 @@ +// SmsCheckinCard — "put in your number and Pegasus will text you." +// Registers the phone with the signals service, then fires a pulse-check text +// (an emotionally-evocative image stimulus via Bloo.io). The user replies by +// message and the bot scores the reply. Self-contained; drop into any screen. +import React, { useState } from "react"; +import { ActivityIndicator, Pressable, StyleSheet, Text, TextInput, View } from "react-native"; + +import { registerPhone, sendCheckin } from "../services/api"; +import { DEFAULT_USER_ID } from "../services/config"; +import { COLORS, RADIUS, SPACING, TYPE } from "../utils/colors"; + +type Status = "idle" | "sending" | "sent" | "error"; + +// Light E.164 normalization for US numbers: keep a leading +, otherwise assume +// +1 for 10 digits (or 11 starting with 1). Good enough for the demo. +function normalize(raw: string): string { + const trimmed = raw.trim(); + if (trimmed.startsWith("+")) return "+" + trimmed.slice(1).replace(/\D/g, ""); + const digits = trimmed.replace(/\D/g, ""); + if (digits.length === 10) return `+1${digits}`; + if (digits.length === 11 && digits.startsWith("1")) return `+${digits}`; + return `+${digits}`; +} + +export default function SmsCheckinCard() { + const [phone, setPhone] = useState(""); + const [status, setStatus] = useState("idle"); + const [msg, setMsg] = useState(null); + + const valid = phone.replace(/\D/g, "").length >= 10; + + const onSend = async () => { + if (!valid || status === "sending") return; + const e164 = normalize(phone); + setStatus("sending"); + setMsg(null); + try { + await registerPhone(DEFAULT_USER_ID, e164); + const res = await sendCheckin(DEFAULT_USER_ID, e164); + if (res?.sent === false) { + setStatus("error"); + setMsg(res.detail ?? "Couldn't send the text right now."); + } else { + setStatus("sent"); + setMsg(`Sent to ${e164} 📱 — check your messages and reply to it.`); + } + } catch (e: any) { + setStatus("error"); + setMsg(e?.message ?? "Couldn't send. Is the signals service running?"); + } + }; + + return ( + + + Get check-ins by text + + + Pegasus texts you a quick image to react to, then reads your reply — no app needed. + + + + { + setPhone(t); + if (status !== "idle") setStatus("idle"); + }} + placeholder="(555) 123-4567" + placeholderTextColor={COLORS.textDim} + keyboardType="phone-pad" + returnKeyType="send" + onSubmitEditing={onSend} + allowFontScaling={false} + /> + [ + styles.btn, + (!valid || status === "sending") && styles.btnDisabled, + pressed && styles.pressed, + ]} + > + {status === "sending" ? ( + + ) : ( + + Text me + + )} + + + + {msg ? ( + + {msg} + + ) : null} + + ); +} + +const styles = StyleSheet.create({ + card: { + backgroundColor: COLORS.card, + borderWidth: 1, + borderColor: COLORS.border, + borderRadius: RADIUS.lg, + padding: SPACING.lg, + shadowColor: "#000", + shadowOpacity: 0.06, + shadowRadius: 16, + shadowOffset: { width: 0, height: 6 }, + elevation: 2, + }, + title: { ...TYPE.heading, color: COLORS.text }, + sub: { ...TYPE.caption, color: COLORS.textDim, marginTop: SPACING.xs, lineHeight: 18 }, + row: { flexDirection: "row", alignItems: "center", marginTop: SPACING.md, gap: SPACING.sm }, + input: { + flex: 1, + height: 48, + backgroundColor: COLORS.bg, + borderWidth: 1, + borderColor: COLORS.border, + borderRadius: RADIUS.md, + paddingHorizontal: SPACING.md, + color: COLORS.text, + ...TYPE.body, + }, + btn: { + height: 48, + paddingHorizontal: SPACING.lg, + borderRadius: RADIUS.md, + backgroundColor: COLORS.green, + alignItems: "center", + justifyContent: "center", + }, + btnDisabled: { opacity: 0.4 }, + btnText: { ...TYPE.body, color: "#ffffff", fontWeight: "700" }, + pressed: { opacity: 0.7 }, + msg: { ...TYPE.caption, marginTop: SPACING.md, lineHeight: 18 }, +}); diff --git a/frontend/src/components/TrendChart.tsx b/frontend/src/components/TrendChart.tsx index ae1fa9b..f91fb49 100644 --- a/frontend/src/components/TrendChart.tsx +++ b/frontend/src/components/TrendChart.tsx @@ -1,6 +1,7 @@ -// TrendChart — burnout score history as a calm, dark line chart. -// Wraps react-native-chart-kit's LineChart. The line is tinted by the -// burnout zone of the most recent score (green / yellow / red). +// TrendChart — wellness score history as a calm line chart. +// Wraps react-native-chart-kit's LineChart. Score is 0..100 where higher is +// better; the line is tinted by the wellness zone of the most recent score +// (green = thriving, yellow = neutral, red = running low). import React, { useMemo } from "react"; import { StyleSheet, Text, View } from "react-native"; import { LineChart } from "react-native-chart-kit"; @@ -14,11 +15,12 @@ interface TrendChartProps { labels?: string[]; } -// Burnout score zone -> accent hex. Higher score = more burnout. +// Wellness score zone -> accent hex. Higher score = better, matching the +// backend thresholds and levelColor (green >= 70, yellow >= 40, else red). function zoneColor(score: number): string { - if (score >= 70) return COLORS.red; + if (score >= 70) return COLORS.green; if (score >= 40) return COLORS.yellow; - return COLORS.green; + return COLORS.red; } // chart-kit expects a color function: (opacity) => rgba string. @@ -73,7 +75,7 @@ export default function TrendChart({ scores, labels }: TrendChartProps) { Not enough data yet - Check in a few more times to see your burnout trend. + Check in a few more times to see your wellness trend. @@ -127,6 +129,11 @@ const styles = StyleSheet.create({ paddingVertical: CARD_PADDING, paddingHorizontal: CARD_PADDING, overflow: "hidden", + shadowColor: "#000", + shadowOpacity: 0.06, + shadowRadius: 16, + shadowOffset: { width: 0, height: 6 }, + elevation: 2, }, chart: { marginVertical: 0, diff --git a/frontend/src/hooks/useConversation.ts b/frontend/src/hooks/useConversation.ts new file mode 100644 index 0000000..1b9bfed --- /dev/null +++ b/frontend/src/hooks/useConversation.ts @@ -0,0 +1,188 @@ +// Owned by Rishith. The Talk-tab is a FaceTime-style voice call with Pegasus: +// press-and-hold to talk -> we record (expo-av, with live mic metering that +// drives the on-screen sound orb) -> POST the clip to the video service +// /converse, which transcribes it (NVIDIA parakeet), reads voice stress, asks +// the ML companion for a reply, and speaks it back (NVIDIA Chatterbox). We play +// that audio so Pegasus literally talks. +// +// `level` (0..1) is the live mic amplitude while recording — the screen scales +// the orb to it. Every turn is grounded in the user's burnout score/level. +// Cleans up the recorder + player on unmount. Nothing here throws to the screen. +import { useCallback, useEffect, useRef, useState } from "react"; +import { Audio } from "expo-av"; +// SDK 54 moved the classic write/cacheDirectory API behind /legacy. +import * as FileSystem from "expo-file-system/legacy"; + +import { ChatMessage, ConverseResult } from "../types"; +import { converse, speak } from "../services/api"; + +const GREETING: ChatMessage = { + role: "assistant", + content: "Hey, I'm Pegasus. No pressure and no judgement — how are you really doing right now?", +}; + +const RECORD_MODE = { allowsRecordingIOS: true, playsInSilentModeIOS: true } as const; +const PLAY_MODE = { allowsRecordingIOS: false, playsInSilentModeIOS: true } as const; + +export function useConversation(userId: string, score?: number, level?: string) { + const [messages, setMessages] = useState([GREETING]); + const [sending, setSending] = useState(false); // a turn round-trip is in flight + const [recording, setRecording] = useState(false); + const [speaking, setSpeaking] = useState(false); // Pegasus's voice is playing + const [error, setError] = useState(null); + const [voice, setVoice] = useState(null); + const [micLevel, setMicLevel] = useState(0); // live mic amplitude 0..1 (drives the orb) + + const recRef = useRef(null); + const soundRef = useRef(null); + // Mirror messages so async turns read the latest transcript without re-binding. + const messagesRef = useRef(messages); + useEffect(() => { + messagesRef.current = messages; + }, [messages]); + + useEffect(() => { + return () => { + recRef.current?.stopAndUnloadAsync().catch(() => {}); + soundRef.current?.unloadAsync().catch(() => {}); + }; + }, []); + + const playB64 = useCallback(async (b64: string | null) => { + if (!b64) return; + try { + await Audio.setAudioModeAsync(PLAY_MODE); + const path = `${FileSystem.cacheDirectory}pegasus-reply-${Date.now()}.wav`; + await FileSystem.writeAsStringAsync(path, b64, { encoding: FileSystem.EncodingType.Base64 }); + await soundRef.current?.unloadAsync().catch(() => {}); + const { sound } = await Audio.Sound.createAsync({ uri: path }, { shouldPlay: true }); + soundRef.current = sound; + setSpeaking(true); + sound.setOnPlaybackStatusUpdate((st) => { + if (st.isLoaded && st.didJustFinish) { + setSpeaking(false); + sound.unloadAsync().catch(() => {}); + if (soundRef.current === sound) soundRef.current = null; + } + }); + } catch { + setSpeaking(false); + } + }, []); + + // --- VOICE turn (press to talk) ----------------------------------------- + const startRecording = useCallback(async () => { + if (recording || sending) return; + try { + const perm = await Audio.requestPermissionsAsync(); + if (!perm.granted) { + setError("Microphone access is needed to talk."); + return; + } + await Audio.setAudioModeAsync(RECORD_MODE); + const rec = new Audio.Recording(); + // Enable metering so the orb can react to how loudly you're speaking. + await rec.prepareToRecordAsync({ + ...Audio.RecordingOptionsPresets.HIGH_QUALITY, + isMeteringEnabled: true, + }); + rec.setProgressUpdateInterval(80); + rec.setOnRecordingStatusUpdate((st) => { + if (st.isRecording && typeof st.metering === "number") { + // metering is dBFS (~-60 quiet .. 0 loud) -> 0..1 + setMicLevel(Math.max(0, Math.min(1, (st.metering + 55) / 55))); + } + }); + await rec.startAsync(); + recRef.current = rec; + setRecording(true); + setError(null); + } catch { + setRecording(false); + setError("Couldn't start recording."); + } + }, [recording, sending]); + + const stopAndSend = useCallback(async () => { + const rec = recRef.current; + if (!rec) return; + setRecording(false); + setMicLevel(0); + let uri: string | null = null; + try { + await rec.stopAndUnloadAsync(); + uri = rec.getURI(); + } catch { + /* fall through to the no-uri guard */ + } + recRef.current = null; + if (!uri) { + setError("Didn't catch that — hold the mic and try again."); + return; + } + setSending(true); + setError(null); + try { + const res = await converse(userId, uri, messagesRef.current, score, level); + const turns: ChatMessage[] = []; + if (res.transcript) turns.push({ role: "user", content: res.transcript }); + turns.push({ role: "assistant", content: res.reply }); + setMessages((prev) => [...prev, ...turns]); + setVoice(res.voice ?? null); + playB64(res.audio_b64); + } catch (e: any) { + setError(e?.message ?? "Couldn't reach Pegasus. Check the video service is running."); + } finally { + setSending(false); + } + }, [userId, score, level, playB64]); + + // Stop recording WITHOUT sending — called when the screen loses focus or + // unmounts, so the recorder + audio session don't keep running in the + // background (which is what was crashing the app on tab switch). + const cancel = useCallback(async () => { + const rec = recRef.current; + recRef.current = null; + setRecording(false); + setMicLevel(0); + if (rec) { + try { + await rec.stopAndUnloadAsync(); + } catch {} + } + try { + await soundRef.current?.stopAsync(); + } catch {} + }, []); + + // Speak Pegasus's opening line aloud (called when entering voice mode). + const greet = useCallback(async () => { + try { + const { audio_b64 } = await speak(GREETING.content); + playB64(audio_b64); + } catch { + /* greeting stays text-only */ + } + }, [playB64]); + + const reset = useCallback(() => { + setMessages([GREETING]); + setError(null); + setVoice(null); + }, []); + + return { + messages, + sending, + recording, + speaking, + error, + voice, + level: micLevel, + startRecording, + stopAndSend, + cancel, + greet, + reset, + }; +} diff --git a/frontend/src/navigation/TabNavigator.tsx b/frontend/src/navigation/TabNavigator.tsx index a7fd5f4..cd81564 100644 --- a/frontend/src/navigation/TabNavigator.tsx +++ b/frontend/src/navigation/TabNavigator.tsx @@ -9,16 +9,14 @@ import Svg, { Path, Circle } from "react-native-svg"; import { COLORS } from "../utils/colors"; import HomeScreen from "../screens/HomeScreen"; -import StimulusScreen from "../screens/StimulusScreen"; -import CheckInScreen from "../screens/CheckInScreen"; +import ChatScreen from "../screens/ChatScreen"; import MetricsScreen from "../screens/MetricsScreen"; import BrainScreen from "../screens/BrainScreen"; import HistoryScreen from "../screens/HistoryScreen"; type TabParamList = { Home: undefined; - Pulse: undefined; - "Check-In": undefined; + Talk: undefined; Metrics: undefined; Brain: undefined; History: undefined; @@ -50,33 +48,20 @@ function TabIcon({ name, color }: { name: keyof TabParamList; color: string }) { /> ); - case "Pulse": + case "Talk": return ( - - ); - case "Check-In": - return ( - - ); @@ -129,8 +114,7 @@ function TabIcon({ name, color }: { name: keyof TabParamList; color: string }) { // Per-tab accent used for the active glow. Brain glows blue per design spec. const TAB_ACCENT: Record = { Home: COLORS.green, - Pulse: COLORS.green, - "Check-In": COLORS.green, + Talk: COLORS.blue, Metrics: COLORS.green, Brain: COLORS.blue, History: COLORS.green, @@ -138,8 +122,7 @@ const TAB_ACCENT: Record = { const LABELS: Record = { Home: "Home", - Pulse: "Pulse", - "Check-In": "Check-In", + Talk: "Talk", Metrics: "Metrics", Brain: "Brain", History: "History", @@ -176,8 +159,8 @@ export default function TabNavigator() { styles.iconWrap, focused && { shadowColor: accent, - shadowOpacity: 0.9, - shadowRadius: 12, + shadowOpacity: 0.45, + shadowRadius: 8, shadowOffset: { width: 0, height: 0 }, }, ]} @@ -192,8 +175,7 @@ export default function TabNavigator() { }} > - - + @@ -209,7 +191,13 @@ const styles = StyleSheet.create({ height: Platform.OS === "ios" ? 88 : 68, paddingTop: 10, paddingBottom: Platform.OS === "ios" ? 28 : 10, - elevation: 0, + // Soft upward elevation so the bar reads as a floating surface on the + // light canvas (Apple-style). + shadowColor: "#000", + shadowOpacity: 0.06, + shadowRadius: 12, + shadowOffset: { width: 0, height: -3 }, + elevation: 8, }, tabItem: { paddingTop: 2, diff --git a/frontend/src/screens/BrainScreen.tsx b/frontend/src/screens/BrainScreen.tsx index f5ef2b0..956c0d0 100644 --- a/frontend/src/screens/BrainScreen.tsx +++ b/frontend/src/screens/BrainScreen.tsx @@ -1,12 +1,11 @@ -// BrainScreen — "Your Brain Activity", a calm, futuristic readout of the live -// neural map. We pull BrainData for the demo user, render the GPU brain point -// cloud (Brain3D), and below it list the flagged regions in plain English. +// BrainScreen — "Your Brain Activity", a calm, plain-English readout of the live +// neural map. We pull BrainData for the demo user and, for each region, simply +// state whether it's affected and what that means — no 3D, no jargon. // -// A "Healthy Baseline" / "Your Pattern" toggle morphs the whole view: in -// Baseline mode every region is shown at a calm resting activation so you can -// see what a settled brain looks like; in Your Pattern mode we show the real -// measured activations. The brain colors and the flagged-region cards animate -// between the two states. +// A "Your Pattern" / "Healthy Baseline" toggle drives the numbers: in Baseline +// mode every region is shown at a calm resting activation so you can see what a +// settled brain reads like; in Your Pattern mode we show the real measured +// activations. Regions are ordered most-affected first. import React, { useEffect, useMemo, useState } from "react"; import { ActivityIndicator, @@ -18,19 +17,12 @@ import { View, } from "react-native"; import { useSafeAreaInsets } from "react-native-safe-area-context"; -import Animated, { - Easing, - interpolateColor, - useAnimatedStyle, - useSharedValue, - withTiming, -} from "react-native-reanimated"; - -import Brain3D from "../components/Brain3D"; + import { getBrainData } from "../services/api"; import { DEFAULT_USER_ID } from "../services/config"; import { BrainData } from "../types"; import { COLORS, RADIUS, SPACING, TYPE } from "../utils/colors"; +import ScrollEdgeFade from "../components/ScrollEdgeFade"; type RegionKey = keyof BrainData["regions"]; @@ -67,7 +59,8 @@ const REGION_INFO: Record< }, }; -// Display order — most stress-relevant first. +// Stable tie-break order — most stress-relevant first. Used when two regions +// read the same activation, so the list never jitters between renders. const REGION_ORDER: RegionKey[] = [ "amygdala_region", "prefrontal_cortex", @@ -76,8 +69,10 @@ const REGION_ORDER: RegionKey[] = [ "visual_cortex", ]; -// A region counts as "flagged" once its activation crosses this threshold. -const FLAG_THRESHOLD = 0.6; +// Thresholds. At/above HOT a region is "Affected"; at/above WARM it's +// "Elevated"; below that it's "Calm". +const HOT_THRESHOLD = 0.6; +const WARM_THRESHOLD = 0.4; // Calm resting activations used for the "Healthy Baseline" view. Low and even — // what a settled brain looks like. @@ -89,11 +84,29 @@ const BASELINE: BrainData["regions"] = { visual_cortex: 0.26, }; -// Activation 0..1 -> accent. Calm blue, then yellow, then red — mirrors Brain3D. -function activationAccent(a: number): string { - if (a >= FLAG_THRESHOLD) return COLORS.red; - if (a >= 0.4) return COLORS.yellow; - return COLORS.blue; +type Status = { + label: string; // pill + headline word, e.g. "Affected" + accent: string; // bar + pill color + tint: string; // pill background wash +}; + +// Activation 0..1 -> status. Red "Affected", amber "Elevated", green "Calm". +function statusFor(a: number): Status { + if (a >= HOT_THRESHOLD) { + return { label: "Affected", accent: COLORS.red, tint: "rgba(255,59,48,0.12)" }; + } + if (a >= WARM_THRESHOLD) { + return { label: "Elevated", accent: COLORS.yellow, tint: "rgba(255,159,10,0.14)" }; + } + return { label: "Calm", accent: COLORS.green, tint: "rgba(52,199,89,0.14)" }; +} + +// One-line, human verdict for a region given its activation. +function verdictFor(name: string, a: number): string { + if (a >= HOT_THRESHOLD) return `${name} — affected. This region is reading hot.`; + if (a >= WARM_THRESHOLD) + return `${name} — slightly elevated. Worth keeping an eye on.`; + return `${name} — calm. Sitting in a healthy range.`; } export default function BrainScreen() { @@ -105,9 +118,6 @@ export default function BrainScreen() { // false = "Your Pattern" (live data), true = "Healthy Baseline". const [baseline, setBaseline] = useState(false); - // Drives the toggle pill + card color crossfade. 0 = Your Pattern, 1 = Baseline. - const morph = useSharedValue(0); - const load = React.useCallback(() => { setLoading(true); setError(null); @@ -121,22 +131,28 @@ export default function BrainScreen() { load(); }, [load]); - useEffect(() => { - morph.value = withTiming(baseline ? 1 : 0, { - duration: 520, - easing: Easing.inOut(Easing.cubic), + // The region set fed to the cards. Baseline mode shows calm resting values; + // otherwise the measured data. + const shownRegions = baseline ? BASELINE : brain?.regions ?? null; + + // Regions sorted most-affected first; ties broken by REGION_ORDER. + const ranked = useMemo(() => { + if (!shownRegions) return []; + return [...REGION_ORDER].sort((a, b) => { + const diff = (shownRegions[b] ?? 0) - (shownRegions[a] ?? 0); + if (diff !== 0) return diff; + return REGION_ORDER.indexOf(a) - REGION_ORDER.indexOf(b); }); - }, [baseline, morph]); - - // The region set fed to the brain + cards. In baseline mode the cloud rebuilds - // its colors from the calm resting values; otherwise from the measured data. - const liveRegions = brain?.regions ?? null; - const shownRegions = baseline ? BASELINE : liveRegions; - - const flagged = useMemo(() => { - if (!liveRegions) return []; - return REGION_ORDER.filter((k) => (liveRegions[k] ?? 0) >= FLAG_THRESHOLD); - }, [liveRegions]); + }, [shownRegions]); + + // How many regions are reading hot — drives the summary line. + const hotCount = useMemo( + () => + shownRegions + ? REGION_ORDER.filter((k) => (shownRegions[k] ?? 0) >= HOT_THRESHOLD).length + : 0, + [shownRegions] + ); // First load: calm, minimal spinner. if (loading && !brain) { @@ -168,6 +184,11 @@ export default function BrainScreen() { ); } + const summaryText = + hotCount === 0 + ? "All regions calm" + : `${hotCount} region${hotCount === 1 ? "" : "s"} reading hot`; + return ( - {/* 3D brain */} - - {shownRegions ? ( - - ) : ( - - No neural data yet. - - )} + {/* Summary banner */} + + + {summaryText} - {/* Baseline / Your Pattern toggle */} - + {/* Your Pattern / Healthy Baseline toggle (drives the readout) */} + - {/* Flagged regions */} + {/* Per-region readout — most affected first */} - FLAGGED REGIONS - - {flagged.length === 0 ? ( - - - - Nothing flagged. Your regions are reading within a calm range. - - - ) : ( + AFFECTED REGIONS + + {shownRegions ? ( - {flagged.map((key) => ( + {ranked.map((key) => ( ))} + ) : ( + + + No neural data yet. + )} @@ -246,68 +260,44 @@ export default function BrainScreen() { )} + + {/* Frosted top/bottom bands — content blurs softly as it scrolls under. */} + ); } -// Segmented toggle. A reanimated highlight slides between the two modes; the -// `morph` value (shared with the cards) keeps everything visually in sync. +// Plain segmented control — no animation, just clean state. Selecting a segment +// swaps the activations the cards read from. function ModeToggle({ baseline, - morph, onSelect, }: { baseline: boolean; - morph: Animated.SharedValue; onSelect: (baseline: boolean) => void; }) { - const thumbStyle = useAnimatedStyle(() => ({ - left: `${morph.value * 50}%`, - backgroundColor: interpolateColor( - morph.value, - [0, 1], - ["rgba(59, 130, 246, 0.18)", "rgba(34, 197, 94, 0.18)"] - ), - borderColor: interpolateColor( - morph.value, - [0, 1], - [COLORS.blue, COLORS.green] - ), - })); - return ( - onSelect(false)} accessibilityRole="button" accessibilityState={{ selected: !baseline }} accessibilityLabel="Show your pattern" > - + Your Pattern onSelect(true)} accessibilityRole="button" accessibilityState={{ selected: baseline }} accessibilityLabel="Show healthy baseline" > - + Healthy Baseline @@ -316,103 +306,49 @@ function ModeToggle({ ); } -// One flagged region. Its accent crossfades between the calm baseline color and -// the measured (hot) color as the mode toggles, and the activation bar animates -// its width + tint to match. +// One region, stated plainly: name + status pill, a thin activation bar, the +// one-line verdict, and the gentle description of what this region does. function RegionCard({ region, - live, - morph, + activation, }: { region: RegionKey; - live: number; - morph: Animated.SharedValue; + activation: number; }) { const info = REGION_INFO[region]; - const baselineVal = BASELINE[region] ?? 0; - const liveAccent = activationAccent(live); - const baselineAccent = activationAccent(baselineVal); - - // Width % and accent both interpolate from the live (Your Pattern) reading to - // the calm baseline reading as morph goes 0 -> 1. - const barStyle = useAnimatedStyle(() => ({ - width: `${(live + (baselineVal - live) * morph.value) * 100}%`, - backgroundColor: interpolateColor( - morph.value, - [0, 1], - [liveAccent, baselineAccent] - ), - })); - - const dotStyle = useAnimatedStyle(() => ({ - backgroundColor: interpolateColor( - morph.value, - [0, 1], - [liveAccent, baselineAccent] - ), - })); + const status = statusFor(activation); + const pct = Math.round(activation * 100); return ( - {info.name} - + + + + {status.label} + + - - + + + + + {pct}% + {verdictFor(info.name, activation)} {info.plain} ); } -// The numeric percentage label. Reanimated can't drive text content from the UI -// thread, so we poll the shared `morph` value on the JS thread to tween the -// number, and animate the text color in lockstep via interpolateColor. -function AnimatedPct({ - live, - baseline, - morph, - fromColor, - toColor, -}: { - live: number; - baseline: number; - morph: Animated.SharedValue; - fromColor: string; - toColor: string; -}) { - const [shown, setShown] = useState(Math.round(live * 100)); - - const colorStyle = useAnimatedStyle(() => ({ - color: interpolateColor(morph.value, [0, 1], [fromColor, toColor]), - })); - - // Poll the morph value until it settles into its target mode, updating the - // displayed integer percentage as it tweens. - useEffect(() => { - const id = setInterval(() => { - const v = morph.value; - setShown(Math.round((live + (baseline - live) * v) * 100)); - if (v <= 0.001 || v >= 0.999) clearInterval(id); - }, 32); - return () => clearInterval(id); - }, [live, baseline, morph]); - - return ( - {shown}% - ); -} - const styles = StyleSheet.create({ root: { flex: 1, @@ -492,24 +428,32 @@ const styles = StyleSheet.create({ letterSpacing: 0.6, }, - // 3D brain card - brainCard: { - height: 320, - borderRadius: RADIUS.lg, + // Summary banner + summaryCard: { + flexDirection: "row", + alignItems: "center", + backgroundColor: COLORS.card, borderWidth: 1, borderColor: COLORS.border, - backgroundColor: COLORS.bg, - overflow: "hidden", + borderRadius: RADIUS.md, + paddingVertical: SPACING.md, + paddingHorizontal: SPACING.lg, + shadowColor: "#000", + shadowOpacity: 0.06, + shadowRadius: 14, + shadowOffset: { width: 0, height: 5 }, + elevation: 2, }, - brainEmpty: { - flex: 1, - alignItems: "center", - justifyContent: "center", - backgroundColor: COLORS.card, + summaryDot: { + width: 9, + height: 9, + borderRadius: RADIUS.pill, + marginRight: SPACING.md, }, - brainEmptyText: { - ...TYPE.body, - color: COLORS.textDim, + summaryText: { + ...TYPE.heading, + color: COLORS.text, + flex: 1, }, // Toggle @@ -523,32 +467,26 @@ const styles = StyleSheet.create({ borderColor: COLORS.border, borderRadius: RADIUS.pill, padding: 4, - position: "relative", - }, - toggleThumb: { - position: "absolute", - top: 4, - bottom: 4, - // Half the track minus the 4px outer padding on each side, so the thumb - // sits flush inside whichever half `left` (0% or 50%) places it. - width: "50%", - marginHorizontal: 4, - borderRadius: RADIUS.pill, - borderWidth: 1, }, toggleSeg: { flex: 1, alignItems: "center", justifyContent: "center", paddingVertical: SPACING.md, - zIndex: 1, + borderRadius: RADIUS.pill, + }, + toggleSegActive: { + backgroundColor: "rgba(0,113,227,0.10)", }, toggleText: { ...TYPE.label, color: COLORS.textDim, }, + toggleTextActive: { + color: COLORS.blue, + }, - // Flagged regions + // Region readout section: { marginTop: SPACING.xl, }, @@ -567,65 +505,73 @@ const styles = StyleSheet.create({ borderColor: COLORS.border, borderRadius: RADIUS.md, padding: SPACING.lg, + shadowColor: "#000", + shadowOpacity: 0.06, + shadowRadius: 14, + shadowOffset: { width: 0, height: 5 }, + elevation: 2, }, regionHeader: { flexDirection: "row", alignItems: "center", }, - regionDot: { - width: 9, - height: 9, - borderRadius: RADIUS.pill, - marginRight: SPACING.sm, - }, regionName: { ...TYPE.heading, color: COLORS.text, flex: 1, }, - regionPct: { - ...TYPE.heading, - fontVariant: ["tabular-nums"], + pill: { + flexDirection: "row", + alignItems: "center", + borderRadius: RADIUS.pill, + paddingVertical: 5, + paddingHorizontal: 10, }, - barTrack: { + pillDot: { + width: 6, height: 6, borderRadius: RADIUS.pill, - backgroundColor: "rgba(255,255,255,0.06)", + marginRight: SPACING.xs + 2, + }, + pillText: { + ...TYPE.label, + letterSpacing: 0.2, + }, + barRow: { + flexDirection: "row", + alignItems: "center", marginTop: SPACING.md, + }, + barTrack: { + flex: 1, + height: 6, + borderRadius: RADIUS.pill, + backgroundColor: "rgba(0,0,0,0.06)", overflow: "hidden", }, barFill: { height: "100%", borderRadius: RADIUS.pill, }, - regionPlain: { + barPct: { + ...TYPE.label, + fontVariant: ["tabular-nums"], + marginLeft: SPACING.md, + width: 40, + textAlign: "right", + }, + regionVerdict: { ...TYPE.body, - color: COLORS.textDim, + fontWeight: "600", + color: COLORS.text, lineHeight: 22, marginTop: SPACING.md, }, - - // Clear (nothing flagged) - clearCard: { - flexDirection: "row", - alignItems: "center", - backgroundColor: COLORS.card, - borderWidth: 1, - borderColor: COLORS.border, - borderRadius: RADIUS.md, - padding: SPACING.lg, - }, - clearDot: { - width: 9, - height: 9, - borderRadius: RADIUS.pill, - marginRight: SPACING.md, - }, - clearText: { + regionPlain: { ...TYPE.body, color: COLORS.textDim, - flex: 1, lineHeight: 22, + marginTop: SPACING.xs, }, meanFootnote: { diff --git a/frontend/src/screens/ChatScreen.tsx b/frontend/src/screens/ChatScreen.tsx new file mode 100644 index 0000000..11fe36a --- /dev/null +++ b/frontend/src/screens/ChatScreen.tsx @@ -0,0 +1,369 @@ +// ChatScreen — the Talk tab: a check-in "call" with Pegasus. +// +// Your front camera fills the screen with a face outline to center yourself. +// TAP the shutter to start talking, tap again to send (no holding). NVIDIA STT +// transcribes you → the companion replies to your actual words → it speaks back +// (NVIDIA TTS). The face outline pulses with your voice; a live emotion read +// (face + voice) and the last exchange show as captions. +// +// Crash-safe: when you switch tabs (lose focus) we stop recording + the camera +// so nothing keeps running in the background. +import React, { useEffect, useRef, useState } from "react"; +import { ActivityIndicator, Pressable, StyleSheet, Text, View } from "react-native"; +import { CameraView, useCameraPermissions } from "expo-camera"; +import { LinearGradient } from "expo-linear-gradient"; +import { useSafeAreaInsets } from "react-native-safe-area-context"; +import { useIsFocused } from "@react-navigation/native"; +import Animated, { + Easing, + cancelAnimation, + useAnimatedStyle, + useSharedValue, + withRepeat, + withSequence, + withTiming, +} from "react-native-reanimated"; + +import { useConversation } from "../hooks/useConversation"; +import { useBurnoutScore } from "../hooks/useBurnoutScore"; +import { analyzeFrame } from "../services/api"; +import { DEFAULT_USER_ID } from "../services/config"; +import { COLORS, RADIUS, SPACING, TYPE, levelColor } from "../utils/colors"; + +function moodLabel(face: string | null, tremor?: boolean, pitchVar?: number): string { + const voice = tremor ? "shaky voice" : (pitchVar ?? 0) > 55 ? "tense voice" : "steady voice"; + return face ? `${face} · ${voice}` : voice; +} + +export default function ChatScreen() { + const insets = useSafeAreaInsets(); + const isFocused = useIsFocused(); + const { score } = useBurnoutScore(DEFAULT_USER_ID); + const { recording, sending, speaking, error, level, voice, messages, startRecording, stopAndSend, cancel, greet } = + useConversation(DEFAULT_USER_ID, score?.score, score?.level); + + const [camPerm, requestCamPerm] = useCameraPermissions(); + const camRef = useRef(null); + const [faceEmotion, setFaceEmotion] = useState(null); + + const accent = score?.level ? levelColor(score.level) : COLORS.blue; + + // Open the call once: camera + Pegasus speaks first. + const started = useRef(false); + useEffect(() => { + if (started.current) return; + started.current = true; + (async () => { + try { + if (!camPerm?.granted) await requestCamPerm(); + } catch {} + greet(); + })(); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, []); + + // Leaving the tab → stop recording + camera so nothing runs in the background. + useEffect(() => { + if (!isFocused) cancel(); + }, [isFocused, cancel]); + + const busyRef = useRef(false); + useEffect(() => { + busyRef.current = recording || sending; + }, [recording, sending]); + + // Live facial emotion — only while the tab is focused + not mid-recording. + useEffect(() => { + if (!isFocused || !camPerm?.granted) return; + let alive = true; + const id = setInterval(async () => { + if (!camRef.current || busyRef.current) return; + try { + const photo = await camRef.current.takePictureAsync({ quality: 0.4, skipProcessing: true }); + if (!photo?.uri) return; + const r = await analyzeFrame(photo.uri); + const dist = r?.emotion_distribution ?? {}; + const top = Object.entries(dist).sort((a, b) => b[1] - a[1])[0]?.[0]; + if (alive) setFaceEmotion(top ?? r?.overall_affect ?? null); + } catch { + /* optional */ + } + }, 9000); + return () => { + alive = false; + clearInterval(id); + }; + }, [isFocused, camPerm?.granted]); + + // --- the face-outline glow (pulses with your voice) ------------------- + const amp = useSharedValue(0.2); + useEffect(() => { + if (recording) return; + cancelAnimation(amp); + if (speaking) { + amp.value = withRepeat( + withSequence( + withTiming(0.9, { duration: 340, easing: Easing.inOut(Easing.ease) }), + withTiming(0.45, { duration: 340, easing: Easing.inOut(Easing.ease) }) + ), + -1, + true + ); + } else { + amp.value = withRepeat( + withSequence( + withTiming(0.32, { duration: 1700, easing: Easing.inOut(Easing.ease) }), + withTiming(0.14, { duration: 1700, easing: Easing.inOut(Easing.ease) }) + ), + -1, + true + ); + } + return () => cancelAnimation(amp); + }, [recording, speaking, amp]); + useEffect(() => { + if (!recording) return; + amp.value = withTiming(Math.max(0.2, Math.min(1, level)), { duration: 90 }); + }, [level, recording, amp]); + + const ovalGlow = useAnimatedStyle(() => ({ + shadowOpacity: 0.25 + amp.value * 0.55, + shadowRadius: 14 + amp.value * 26, + transform: [{ scale: 1 + amp.value * 0.04 }], + })); + + const onShutter = () => { + if (sending) return; + if (recording) stopAndSend(); + else startRecording(); + }; + + const lastAI = [...messages].reverse().find((m) => m.role === "assistant"); + const lastUser = [...messages].reverse().find((m) => m.role === "user"); + const status = recording + ? "Listening — tap to send" + : sending + ? "Thinking…" + : speaking + ? "Pegasus is speaking…" + : "Tap to talk"; + + return ( + + {isFocused && camPerm?.granted ? ( + + ) : ( + + )} + + + + + {/* Face outline */} + + + + {recording ? "Speak naturally" : "Center your face"} + + + + {/* Header */} + + + Pegasus + + + {score != null ? `check-in · pulse ${score.score}/100` : "let's check in"} + + {(faceEmotion || voice) ? ( + + + + {moodLabel(faceEmotion, voice?.voice_tremor, voice?.pitch_variability)} + + + ) : null} + + + {/* Captions */} + + {lastUser ? ( + + “{lastUser.content}” + + ) : null} + {lastAI ? ( + + {lastAI.content} + + ) : null} + {error ? ( + + {error} + + ) : null} + + + {/* Apple-camera-style shutter (tap to talk) */} + + + + {status} + + + + ); +} + +function Shutter({ + recording, + sending, + onPress, +}: { + recording: boolean; + sending: boolean; + onPress: () => void; +}) { + const pulse = useSharedValue(0); + useEffect(() => { + cancelAnimation(pulse); + if (recording) { + pulse.value = withRepeat( + withSequence( + withTiming(1, { duration: 650, easing: Easing.inOut(Easing.ease) }), + withTiming(0, { duration: 650, easing: Easing.inOut(Easing.ease) }) + ), + -1, + false + ); + } else { + pulse.value = withTiming(0, { duration: 200 }); + } + return () => cancelAnimation(pulse); + }, [recording, pulse]); + + const innerStyle = useAnimatedStyle(() => ({ opacity: recording ? 0.65 + pulse.value * 0.35 : 1 })); + + return ( + [styles.shutterOuter, pressed && { opacity: 0.85 }, sending && { opacity: 0.5 }]} + accessibilityRole="button" + accessibilityLabel={recording ? "Stop and send" : "Tap to talk"} + > + + {sending ? : null} + + + ); +} + +const styles = StyleSheet.create({ + root: { flex: 1, backgroundColor: "#000" }, + topScrim: { position: "absolute", top: 0, left: 0, right: 0 }, + bottomScrim: { position: "absolute", bottom: 0, left: 0, right: 0, height: 320 }, + + ovalWrap: { ...StyleSheet.absoluteFillObject, alignItems: "center", justifyContent: "center" }, + oval: { + width: 230, + height: 300, + borderRadius: 150, + borderWidth: 2.5, + shadowOffset: { width: 0, height: 0 }, + }, + ovalHint: { + ...TYPE.caption, + color: "rgba(255,255,255,0.85)", + marginTop: SPACING.lg, + textShadowColor: "rgba(0,0,0,0.8)", + textShadowRadius: 5, + }, + + header: { paddingHorizontal: SPACING.lg, paddingBottom: SPACING.md }, + title: { ...TYPE.heading, color: "#fff" }, + sub: { ...TYPE.caption, color: "rgba(255,255,255,0.75)", marginTop: 2 }, + moodChip: { + flexDirection: "row", + alignItems: "center", + alignSelf: "flex-start", + marginTop: SPACING.sm, + backgroundColor: "rgba(0,0,0,0.4)", + borderRadius: RADIUS.pill, + paddingHorizontal: SPACING.md, + paddingVertical: 4, + }, + moodDot: { width: 7, height: 7, borderRadius: 4, marginRight: 6 }, + moodText: { ...TYPE.caption, color: "#fff", textTransform: "capitalize" }, + + captions: { + position: "absolute", + left: SPACING.lg, + right: SPACING.lg, + bottom: 210, + alignItems: "center", + gap: SPACING.sm, + }, + capYou: { + ...TYPE.body, + color: "rgba(255,255,255,0.8)", + fontStyle: "italic", + textAlign: "center", + textShadowColor: "rgba(0,0,0,0.8)", + textShadowRadius: 6, + }, + capAI: { + ...TYPE.title, + fontSize: 22, + lineHeight: 30, + color: "#fff", + textAlign: "center", + textShadowColor: "rgba(0,0,0,0.85)", + textShadowRadius: 8, + }, + err: { ...TYPE.caption, color: COLORS.yellow, textAlign: "center" }, + + dock: { position: "absolute", bottom: 0, left: 0, right: 0, alignItems: "center", paddingTop: SPACING.md }, + shutterOuter: { + width: 78, + height: 78, + borderRadius: 39, + borderWidth: 4, + borderColor: "#fff", + alignItems: "center", + justifyContent: "center", + backgroundColor: "transparent", + }, + shutterInner: { + width: 60, + height: 60, + borderRadius: 30, + backgroundColor: "#fff", + alignItems: "center", + justifyContent: "center", + }, + shutterInnerRec: { + width: 32, + height: 32, + borderRadius: 9, + backgroundColor: COLORS.red, + alignItems: "center", + justifyContent: "center", + }, + status: { ...TYPE.label, color: "#fff", marginTop: SPACING.md, letterSpacing: 0.4 }, +}); diff --git a/frontend/src/screens/CheckInScreen.tsx b/frontend/src/screens/CheckInScreen.tsx index dee5e4d..e22ce7b 100644 --- a/frontend/src/screens/CheckInScreen.tsx +++ b/frontend/src/screens/CheckInScreen.tsx @@ -260,7 +260,7 @@ function PermissionGate({ ]} > {busy ? ( - + ) : ( {denied ? "Try again" : "Enable camera & mic"} @@ -432,7 +432,7 @@ function AnalyzingView() { (null); + const [history, setHistory] = useState(null); const [loading, setLoading] = useState(true); - const [refreshing, setRefreshing] = useState(false); const [error, setError] = useState(null); - const [filter, setFilter] = useState("all"); - const [expandedKey, setExpandedKey] = useState(null); - const load = useCallback(async (mode: "initial" | "refresh") => { - if (mode === "initial") setLoading(true); - else setRefreshing(true); + const load = useCallback(() => { + setLoading(true); setError(null); - try { - const data = await getHistory(DEFAULT_USER_ID); - // Newest-first. Sort defensively in case the API returns oldest-first. - const sorted = [...(data ?? [])].sort( - (a, b) => - new Date(b.timestamp).getTime() - new Date(a.timestamp).getTime() - ); - setHistory(sorted); - } catch (e) { - setError(e instanceof Error ? e.message : "Couldn't load your history"); - } finally { - setLoading(false); - setRefreshing(false); - } + getHistory(DEFAULT_USER_ID) + .then((d) => setHistory(d ?? [])) + .catch((e) => setError(e?.message ?? "Couldn't load your history.")) + .finally(() => setLoading(false)); }, []); useEffect(() => { - load("initial"); + load(); }, [load]); - const filtered = useMemo(() => { - if (!history) return []; - if (filter === "all") return history; - return history.filter((r) => r.level === filter); - }, [history, filter]); - - // First load, before any data has arrived. + // First load: calm, minimal spinner. if (loading && !history) { return ( - Loading your timeline… + Loading your history… ); } + // Error / unreachable: clear, non-alarming fallback with a retry. + if (error && !history) { + return ( + + History unavailable + {error} + + Try again + + + ); + } + + const entries = history ?? []; + return ( load("refresh")} + refreshing={loading} + onRefresh={load} tintColor={COLORS.textDim} colors={[COLORS.green]} /> @@ -115,217 +111,85 @@ export default function HistoryScreen() { {/* Header */} History - your readings over time - - - {/* Filter chips */} - - {FILTERS.map(({ key, label }) => ( - setFilter(key)} - /> - ))} + Your past check-ins and talks - {/* Error state — keeps any previously loaded data visible below. */} + {/* Inline error — keeps any previously loaded list visible below. */} {error && ( - load("refresh")}> - {error} - Tap to retry + + {error} + Tap to retry )} - {/* Empty state */} - {!error && filtered.length === 0 ? ( - 0} /> + {entries.length === 0 ? ( + ) : ( - {filtered.map((reading, i) => { - const key = readingKey(reading, i); - return ( - - setExpandedKey((cur) => (cur === key ? null : key)) - } - /> - ); - })} + {entries.map((entry, i) => ( + + ))} )} - - ); -} -// A single filter chip. Active state tints to the level's accent (or white for All). -function FilterChip({ - label, - filterKey, - active, - onPress, -}: { - label: string; - filterKey: Filter; - active: boolean; - onPress: () => void; -}) { - const accent = filterKey === "all" ? COLORS.text : levelColor(filterKey); - return ( - - {filterKey !== "all" && ( - - )} - {label} - + {/* Frosted top/bottom bands — content blurs softly as it scrolls under. */} + + ); } -// A history row. Collapsed: timestamp + score circle + top indicator. Expanded: -// status label, full signal breakdown, and the intervention copy. -function HistoryRow({ - reading, - expanded, - onToggle, -}: { - reading: BurnoutResult; - expanded: boolean; - onToggle: () => void; -}) { - const level = (reading.level as BurnoutLevel) ?? "green"; - const accent = levelColor(level); - const when = relativeTime(reading.timestamp); - const topIndicator = (reading.top_indicators ?? []).filter(Boolean)[0]; - - const press = useSharedValue(0); - const pressStyle = useAnimatedStyle(() => ({ - transform: [{ scale: 1 - press.value * 0.015 }], - })); +// One past moment. Top row: a KIND badge + short timestamp on the left, the +// wellness score chip on the right. Below: the user's words in quotes (if any), +// then the intervention in a subtle inset row (if any). +function HistoryCard({ entry }: { entry: HistoryEntry }) { + const accent = levelColor(entry.level); + const kindLabel = entry.kind === "talk" ? "Talk" : "Check-in"; + const when = shortWhen(entry.timestamp); + const text = entry.text?.trim(); + const intervention = entry.intervention?.trim(); return ( - - { - press.value = withTiming(1, { - duration: 110, - easing: Easing.out(Easing.ease), - }); - }} - onPressOut={() => { - press.value = withTiming(0, { - duration: 150, - easing: Easing.out(Easing.ease), - }); - }} - accessibilityRole="button" - accessibilityState={{ expanded }} - accessibilityLabel={`Reading ${when || ""}, score ${Math.round( - reading.score - )}, ${scoreLabel(level)}`} - style={[styles.row, expanded && { borderColor: "rgba(255,255,255,0.12)" }]} - > - {/* Collapsed header */} - - - - {when || "—"} - - {topIndicator ?? scoreLabel(level)} - + + + + + {kindLabel} - - {expanded ? "−" : "+"} - + {when ? {when} : null} - {/* Expanded detail */} - {expanded && ( - - - {scoreLabel(level)} - - - {/* Top indicators, beyond the one shown collapsed. */} - {(() => { - const indicators = (reading.top_indicators ?? []).filter(Boolean); - if (indicators.length === 0) return null; - return ( - - {indicators.map((text, i) => ( - - - {titleCase(text)} - - ))} - - ); - })()} - - {/* Full signal breakdown. */} - {reading.breakdown && ( - - - - )} + {/* Wellness score — higher is better, colored by level. */} + + + {Math.round(entry.score ?? 0)} + + /100 + + - {/* Intervention copy for this reading. */} - {reading.intervention?.trim() ? ( - - - - ) : null} - - )} - - - ); -} + {/* The user's own words. */} + {text ? “{text}” : null} -// Small level-colored score circle. Big-ish numeral, tinted ring + glow. -function ScoreCircle({ score, accent }: { score: number; accent: string }) { - return ( - - - {Math.round(score ?? 0)} - + {/* Pegasus's gentle nudge for this moment. */} + {intervention ? ( + + + {intervention} + + ) : null} ); } -// Empty state — distinguishes "no readings at all" from "none match this filter". -function EmptyState({ - filter, - hasAny, -}: { - filter: Filter; - hasAny: boolean; -}) { - const filteredOut = filter !== "all" && hasAny; +// Empty state — calm invitation to start a history. +function EmptyState() { return ( - - {filteredOut ? "Nothing here yet" : "No readings yet"} - + No check-ins yet - {filteredOut - ? "No readings match this filter. Try another, or pull to refresh." - : "Your readings will appear here after your first check-in. Pull to refresh."} + Talk to Pegasus or reply to a text to start your history. ); @@ -339,6 +203,7 @@ const styles = StyleSheet.create({ centerRoot: { alignItems: "center", justifyContent: "center", + padding: SPACING.lg, }, centerText: { ...TYPE.body, @@ -348,6 +213,8 @@ const styles = StyleSheet.create({ scroll: { paddingHorizontal: SPACING.lg, }, + + // Header header: { marginBottom: SPACING.lg, }, @@ -356,152 +223,155 @@ const styles = StyleSheet.create({ color: COLORS.text, }, subtitle: { - ...TYPE.caption, + ...TYPE.body, color: COLORS.textDim, marginTop: SPACING.xs, - letterSpacing: 0.4, }, - // Filter chips - chips: { - flexDirection: "row", - flexWrap: "wrap", - gap: SPACING.sm, - marginBottom: SPACING.lg, + + // Full-screen error fallback + errorTitle: { + ...TYPE.heading, + color: COLORS.text, }, - chip: { - flexDirection: "row", - alignItems: "center", - paddingVertical: SPACING.sm, - paddingHorizontal: SPACING.md, - borderRadius: RADIUS.pill, + errorBody: { + ...TYPE.body, + color: COLORS.textDim, + textAlign: "center", + marginTop: SPACING.sm, + lineHeight: 22, + }, + retryBtn: { + marginTop: SPACING.xl, borderWidth: 1, borderColor: COLORS.border, + borderRadius: RADIUS.md, + paddingVertical: SPACING.md, + paddingHorizontal: SPACING.xl, backgroundColor: COLORS.card, }, - chipDot: { - width: 7, - height: 7, - borderRadius: RADIUS.pill, - marginRight: SPACING.sm, - }, - chipText: { - ...TYPE.label, - color: COLORS.textDim, - letterSpacing: 0.3, + retryText: { + ...TYPE.body, + color: COLORS.text, + fontWeight: "700", }, - // Error + + // Inline (non-blocking) error card errorCard: { - backgroundColor: "rgba(239, 68, 68, 0.08)", + backgroundColor: "rgba(255, 59, 48, 0.08)", borderWidth: 1, - borderColor: "rgba(239, 68, 68, 0.3)", + borderColor: "rgba(255, 59, 48, 0.30)", borderRadius: RADIUS.md, padding: SPACING.md, marginBottom: SPACING.lg, }, - errorText: { + errorCardText: { ...TYPE.body, color: COLORS.text, }, - errorHint: { + errorCardHint: { ...TYPE.caption, color: COLORS.red, marginTop: SPACING.xs, }, + // List list: { gap: SPACING.md, }, - row: { + card: { backgroundColor: COLORS.card, borderWidth: 1, borderColor: COLORS.border, borderRadius: RADIUS.lg, - padding: SPACING.md, - }, - rowHeader: { + padding: SPACING.lg, + shadowColor: "#000", + shadowOpacity: 0.06, + shadowRadius: 14, + shadowOffset: { width: 0, height: 5 }, + elevation: 2, + }, + cardHeader: { flexDirection: "row", alignItems: "center", + justifyContent: "space-between", }, - rowBody: { + metaRow: { + flexDirection: "row", + alignItems: "center", flex: 1, - marginLeft: SPACING.md, - }, - rowWhen: { - ...TYPE.body, - color: COLORS.text, - fontWeight: "600", + gap: SPACING.sm, }, - rowIndicator: { - ...TYPE.caption, - marginTop: 2, + kindBadge: { + paddingVertical: 4, + paddingHorizontal: 10, + borderRadius: RADIUS.pill, + backgroundColor: "rgba(0,0,0,0.05)", }, - chevron: { - fontSize: 22, - fontWeight: "400", + kindText: { + ...TYPE.label, + fontSize: 11, color: COLORS.textDim, - marginLeft: SPACING.sm, - width: 18, - textAlign: "center", + letterSpacing: 0.5, }, - chevronOpen: { - color: COLORS.text, + when: { + ...TYPE.caption, + color: COLORS.textDim, }, - // Score circle - circle: { - width: 52, - height: 52, + + // Wellness score chip + scoreChip: { + flexDirection: "row", + alignItems: "baseline", + borderWidth: 1.5, borderRadius: RADIUS.pill, - borderWidth: 2, - alignItems: "center", - justifyContent: "center", - backgroundColor: "rgba(255,255,255,0.02)", - shadowOpacity: 0.25, - shadowRadius: 10, - shadowOffset: { width: 0, height: 0 }, - elevation: 3, - }, - circleScore: { - fontSize: 20, - fontWeight: "800", - letterSpacing: -0.5, + paddingVertical: 5, + paddingHorizontal: 12, + marginLeft: SPACING.sm, }, - // Expanded detail - detail: { - marginTop: SPACING.lg, - borderTopWidth: 1, - borderTopColor: COLORS.border, - paddingTop: SPACING.lg, + scoreNum: { + fontSize: 18, + fontWeight: "800", + letterSpacing: -0.4, + fontVariant: ["tabular-nums"], }, - statusLabel: { - ...TYPE.heading, - marginBottom: SPACING.md, + scoreOutOf: { + fontSize: 11, + fontWeight: "600", + color: COLORS.textDim, + marginLeft: 1, }, - indicatorList: { - gap: SPACING.sm, - marginBottom: SPACING.lg, + + // User's words + quote: { + ...TYPE.body, + fontStyle: "italic", + color: COLORS.textDim, + lineHeight: 23, + marginTop: SPACING.md, }, - indicatorRow: { + + // Intervention row + interventionRow: { flexDirection: "row", - alignItems: "center", + marginTop: SPACING.md, + backgroundColor: "rgba(0,0,0,0.03)", + borderRadius: RADIUS.sm, + padding: SPACING.md, }, - indicatorDot: { - width: 6, - height: 6, + interventionBar: { + width: 3, borderRadius: RADIUS.pill, - marginRight: SPACING.sm, + marginRight: SPACING.md, }, - indicatorText: { + interventionText: { flex: 1, ...TYPE.body, + fontSize: 15, color: COLORS.text, + lineHeight: 22, }, - breakdownWrap: { - marginBottom: SPACING.lg, - }, - interventionWrap: { - marginTop: SPACING.xs, - }, - // Empty + + // Empty state empty: { alignItems: "center", paddingVertical: SPACING.xxl, diff --git a/frontend/src/screens/HomeScreen.tsx b/frontend/src/screens/HomeScreen.tsx index f32f5f5..d9ccd50 100644 --- a/frontend/src/screens/HomeScreen.tsx +++ b/frontend/src/screens/HomeScreen.tsx @@ -1,7 +1,8 @@ // HomeScreen — the hero. "A check engine light for your mind." -// Live burnout score via useBurnoutScore, the breathing CheckEngine orb, -// the top indicators, an intervention suggestion, and the primary check-in CTA. -// On a red reading we fire a heavy haptic and surface a calm full-screen alert. +// Live wellness score (0..100, higher = better) via useBurnoutScore, the +// breathing CheckEngine orb, the top indicators, an intervention suggestion, and +// the primary check-in CTA. On a low (red) reading we fire a heavy haptic and +// surface a calm full-screen alert. import React, { useEffect, useRef, useState } from "react"; import { ActivityIndicator, @@ -26,6 +27,8 @@ import Animated, { import CheckEngine from "../components/CheckEngine"; import IndicatorCard, { type IndicatorTone } from "../components/IndicatorCard"; import InterventionCard from "../components/InterventionCard"; +import SmsCheckinCard from "../components/SmsCheckinCard"; +import ScrollEdgeFade from "../components/ScrollEdgeFade"; import { useBurnoutScore } from "../hooks/useBurnoutScore"; import { DEFAULT_USER_ID } from "../services/config"; import { COLORS, RADIUS, SPACING, TYPE, levelColor } from "../utils/colors"; @@ -33,14 +36,12 @@ import type { BurnoutLevel } from "../utils/colors"; import { scoreLabel } from "../utils/formatting"; // The tab navigator route names — mirror of TabNavigator's TabParamList so -// navigation.navigate("Check-In") is type-checked here. +// navigation.navigate("Talk") is type-checked here. type TabParamList = { Home: undefined; - Pulse: undefined; - "Check-In": undefined; + Talk: undefined; Metrics: undefined; Brain: undefined; - History: undefined; }; type Props = BottomTabScreenProps; @@ -96,10 +97,13 @@ export default function HomeScreen({ navigation }: Props) { const intervention = score?.intervention ?? ""; // `support` is the longer, reassuring copy shown on a red alert. The backend // may not always send it, so fall back to the intervention text. + // The backend may send `support` as a string, an empty array, or omit it — + // coerce defensively so a non-string never reaches .trim() (that crashed Home). + const rawSupport = (score as unknown as { support?: unknown } | null)?.support; const support = - ((score as unknown as { support?: string } | null)?.support ?? "").trim() || + (typeof rawSupport === "string" ? rawSupport : "").trim() || intervention || - "Your signals are running hot. Take a few slow breaths, step away from the screen, and reach out to someone you trust."; + "Your reserves are running low. Take a few slow breaths, step away from the screen, and reach out to someone you trust."; return ( @@ -159,11 +163,19 @@ export default function HomeScreen({ navigation }: Props) { navigation.navigate("Check-In")} + onPress={() => navigation.navigate("Talk")} /> + + {/* Text-me-a-check-in (SMS / iMessage via Bloo.io) */} + + + + {/* Frosted top/bottom bands — content blurs softly as it scrolls under. */} + + {/* Red-level alert overlay */} setAlertVisible(false)} onCheckIn={() => { setAlertVisible(false); - navigation.navigate("Check-In"); + navigation.navigate("Talk"); }} /> @@ -203,9 +215,9 @@ function CheckInButton({ onPress={onPress} style={[styles.cta, { borderColor: accent }]} accessibilityRole="button" - accessibilityLabel="Do a 30-second check-in" + accessibilityLabel="Talk to Pegasus" > - Do a 30-second check-in + Talk to Pegasus ); @@ -234,8 +246,8 @@ function AlertOverlay({ {}}> - ALERT: TAKE ACTION - Your signals are running hot + RUNNING LOW + Your reserves are running low {support} - Do a 30-second check-in + Talk to Pegasus @@ -295,9 +307,10 @@ const styles = StyleSheet.create({ marginTop: SPACING.md, }, statusLabel: { - ...TYPE.heading, - marginTop: SPACING.sm, - letterSpacing: 0.2, + fontSize: 24, + fontWeight: "700", + letterSpacing: -0.3, + marginTop: SPACING.xs, }, section: { marginTop: SPACING.xl, @@ -316,8 +329,8 @@ const styles = StyleSheet.create({ }, ctaShadow: { borderRadius: RADIUS.lg, - shadowOpacity: 0.35, - shadowRadius: 20, + shadowOpacity: 0.22, + shadowRadius: 18, shadowOffset: { width: 0, height: 8 }, elevation: 6, }, @@ -338,7 +351,7 @@ const styles = StyleSheet.create({ // Alert overlay overlayScrim: { flex: 1, - backgroundColor: "rgba(5, 5, 8, 0.82)", + backgroundColor: "rgba(29, 29, 31, 0.32)", alignItems: "center", justifyContent: "center", padding: SPACING.lg, @@ -348,13 +361,13 @@ const styles = StyleSheet.create({ maxWidth: 420, backgroundColor: COLORS.card, borderWidth: 1, - borderColor: "rgba(239, 68, 68, 0.35)", + borderColor: "rgba(255, 59, 48, 0.30)", borderRadius: RADIUS.lg, padding: SPACING.xl, - shadowColor: COLORS.red, - shadowOpacity: 0.3, + shadowColor: "#000", + shadowOpacity: 0.12, shadowRadius: 28, - shadowOffset: { width: 0, height: 10 }, + shadowOffset: { width: 0, height: 12 }, elevation: 12, }, overlayDot: { @@ -380,7 +393,7 @@ const styles = StyleSheet.create({ marginTop: SPACING.md, }, overlayPrimary: { - backgroundColor: "rgba(239, 68, 68, 0.12)", + backgroundColor: COLORS.red, borderWidth: 1, borderRadius: RADIUS.md, paddingVertical: SPACING.md, @@ -390,7 +403,7 @@ const styles = StyleSheet.create({ overlayPrimaryText: { fontSize: 16, fontWeight: "700", - color: COLORS.text, + color: "#ffffff", }, overlaySecondary: { alignItems: "center", diff --git a/frontend/src/screens/MetricsScreen.tsx b/frontend/src/screens/MetricsScreen.tsx index 480717a..4c1a246 100644 --- a/frontend/src/screens/MetricsScreen.tsx +++ b/frontend/src/screens/MetricsScreen.tsx @@ -21,13 +21,22 @@ import { View, } from "react-native"; import Svg, { Path, Circle } from "react-native-svg"; +import { useSafeAreaInsets } from "react-native-safe-area-context"; import TrendChart from "../components/TrendChart"; import ScoreBreakdown from "../components/ScoreBreakdown"; +import ScrollEdgeFade from "../components/ScrollEdgeFade"; import { BurnoutResult } from "../types"; import { getMetrics, getHistory } from "../services/api"; import { DEFAULT_USER_ID } from "../services/config"; -import { COLORS, RADIUS, SPACING, TYPE, levelColor } from "../utils/colors"; +import { + COLORS, + RADIUS, + SPACING, + TYPE, + TEXT_TERTIARY, + levelColor, +} from "../utils/colors"; import { relativeTime, scoreLabel } from "../utils/formatting"; const WEEK_MS = 7 * 24 * 60 * 60 * 1000; @@ -96,6 +105,7 @@ function weekOverWeek( } export default function MetricsScreen() { + const insets = useSafeAreaInsets(); const [metrics, setMetrics] = useState(null); const [history, setHistory] = useState([]); const [loading, setLoading] = useState(true); @@ -109,7 +119,7 @@ export default function MetricsScreen() { try { const [m, h] = await Promise.all([ getMetrics(DEFAULT_USER_ID) as Promise, - getHistory(DEFAULT_USER_ID) as Promise, + getHistory(DEFAULT_USER_ID) as unknown as Promise, ]); setMetrics(m ?? {}); setHistory(Array.isArray(h) ? h : []); @@ -238,21 +248,28 @@ export default function MetricsScreen() { const accent = levelColor(currentLevel); return ( - load("refresh")} - tintColor={COLORS.textDim} - /> - } - > + + load("refresh")} + tintColor={COLORS.textDim} + /> + } + > {/* Current score header */} - BURNOUT INDEX + WELLNESS SCORE {currentScore != null ? ( <> @@ -338,8 +355,12 @@ export default function MetricsScreen() { )} - - + + + + {/* Frosted top/bottom bands — content blurs softly as it scrolls under. */} + + ); } @@ -581,7 +602,6 @@ const styles = StyleSheet.create({ }, content: { paddingHorizontal: SPACING.lg, - paddingTop: SPACING.xxl, }, loadingText: { ...TYPE.body, @@ -613,6 +633,7 @@ const styles = StyleSheet.create({ ...TYPE.label, color: COLORS.textDim, textTransform: "uppercase", + letterSpacing: 1.5, marginBottom: SPACING.sm, }, scoreRow: { @@ -621,13 +642,16 @@ const styles = StyleSheet.create({ }, score: { ...TYPE.hero, + fontWeight: "800", color: COLORS.text, lineHeight: 64, }, scoreOutOf: { - ...TYPE.heading, - color: COLORS.textDim, - marginBottom: 10, + fontSize: 20, + fontWeight: "600", + color: TEXT_TERTIARY, + letterSpacing: -0.2, + marginBottom: 11, marginLeft: 4, }, statusRow: { @@ -659,6 +683,7 @@ const styles = StyleSheet.create({ ...TYPE.label, color: COLORS.textDim, textTransform: "uppercase", + letterSpacing: 1.5, marginBottom: SPACING.md, }, @@ -669,6 +694,11 @@ const styles = StyleSheet.create({ borderColor: COLORS.border, borderRadius: RADIUS.lg, padding: SPACING.lg, + shadowColor: "#000", + shadowOpacity: 0.06, + shadowRadius: 16, + shadowOffset: { width: 0, height: 6 }, + elevation: 2, }, emptyTitle: { ...TYPE.heading, @@ -693,6 +723,11 @@ const styles = StyleSheet.create({ borderColor: COLORS.border, borderRadius: RADIUS.lg, padding: SPACING.md, + shadowColor: "#000", + shadowOpacity: 0.06, + shadowRadius: 14, + shadowOffset: { width: 0, height: 5 }, + elevation: 2, }, wowLabel: { ...TYPE.caption, @@ -745,6 +780,11 @@ const styles = StyleSheet.create({ borderColor: COLORS.border, borderRadius: RADIUS.lg, padding: SPACING.lg, + shadowColor: "#000", + shadowOpacity: 0.06, + shadowRadius: 16, + shadowOffset: { width: 0, height: 6 }, + elevation: 2, }, sentimentHeader: { flexDirection: "row", diff --git a/frontend/src/screens/StimulusScreen.tsx b/frontend/src/screens/StimulusScreen.tsx index 6596fff..86893f9 100644 --- a/frontend/src/screens/StimulusScreen.tsx +++ b/frontend/src/screens/StimulusScreen.tsx @@ -288,7 +288,7 @@ export default function StimulusScreen() { disabled={submitting || text.trim().length === 0} > {submitting ? ( - + ) : ( Submit pulse )} @@ -402,6 +402,11 @@ const styles = StyleSheet.create({ borderWidth: 1, borderColor: COLORS.border, padding: SPACING.md, + shadowColor: "#000", + shadowOpacity: 0.05, + shadowRadius: 12, + shadowOffset: { width: 0, height: 4 }, + elevation: 1, }, input: { ...TYPE.body, @@ -421,7 +426,7 @@ const styles = StyleSheet.create({ }, primaryBtnText: { ...TYPE.heading, - color: COLORS.bg, + color: "#ffffff", fontSize: 17, }, btnDisabled: { diff --git a/frontend/src/services/api.ts b/frontend/src/services/api.ts index 714eb10..8e539aa 100644 --- a/frontend/src/services/api.ts +++ b/frontend/src/services/api.ts @@ -6,8 +6,11 @@ import { Stimulus, BrainData, VideoResult, + ChatMessage, + ConverseResult, + HistoryEntry, } from "../types"; -import { BACKEND_URL } from "./config"; +import { BACKEND_URL, ML_URL, VIDEO_URL, SIGNALS_URL } from "./config"; async function json(res: Response): Promise { if (!res.ok) throw new Error(`${res.url} -> ${res.status}`); @@ -28,12 +31,15 @@ export async function submitResponse(data: UserResponse): Promise ); } +// Live, DERIVED wellness reading (higher = better). The ML service updates this +// every time the user talks or texts, so it actually moves. export async function getScore(userId: string): Promise { - return json(await fetch(`${BACKEND_URL}/score/${userId}`)); + return json(await fetch(`${ML_URL}/wellness/${userId}`)); } -export async function getHistory(userId: string): Promise { - return json(await fetch(`${BACKEND_URL}/history/${userId}`)); +// Past check-ins + talks (most recent first) — from the ML history store. +export async function getHistory(userId: string): Promise { + return json(await fetch(`${ML_URL}/history/${userId}`)); } export async function getBrainData(userId: string): Promise { @@ -51,3 +57,89 @@ export async function submitVideo(userId: string, videoUri: string): Promise { + return json( + await fetch(`${ML_URL}/chat`, { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ user_id: userId, messages, score, level }), + }) + ); +} + +// Voice-call turn (Talk tab, voice mode). Uploads the user's spoken audio to the +// video service, which transcribes it (NVIDIA parakeet), reads voice stress, +// asks the ML companion for a reply, and speaks it back (NVIDIA Chatterbox). +// Returns the transcript, reply text, reply audio (base64 WAV), and voice stress. +export async function converse( + userId: string, + audioUri: string, + history: ChatMessage[], + score?: number, + level?: string +): Promise { + const fd = new FormData(); + fd.append("audio", { uri: audioUri, type: "audio/m4a", name: "turn.m4a" } as any); + fd.append("history", JSON.stringify(history)); + fd.append("user_id", userId); + if (score != null) fd.append("score", String(score)); + if (level) fd.append("level", level); + return json(await fetch(`${VIDEO_URL}/converse`, { method: "POST", body: fd })); +} + +// Synthesize arbitrary text to speech (Pegasus's opening line). Returns base64 +// WAV, or null audio if TTS is unavailable. +export async function speak(text: string): Promise<{ audio_b64: string | null; sample_rate: number }> { + return json( + await fetch(`${VIDEO_URL}/tts`, { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ text }), + }) + ); +} + +// Single-frame facial affect (HF model) for the live video call — fast read of +// the user's face during the FaceTime conversation. +export async function analyzeFrame( + imageUri: string +): Promise<{ facial_stress_score: number; overall_affect?: string; emotion_distribution?: Record }> { + const fd = new FormData(); + fd.append("image", { uri: imageUri, type: "image/jpeg", name: "frame.jpg" } as any); + return json(await fetch(`${VIDEO_URL}/facial-frame`, { method: "POST", body: fd })); +} + +// SMS / iMessage check-ins (Bloo.io). Talks to the signals service (:8002): +// register the phone, then trigger a pulse-check text (an emotionally-evocative +// image stimulus). The user replies by text and the bot scores it. +export async function registerPhone( + userId: string, + phone: string +): Promise<{ registered: boolean; phone: string }> { + return json( + await fetch(`${SIGNALS_URL}/register-phone`, { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ user_id: userId, phone }), + }) + ); +} + +export async function sendCheckin(userId: string, phone: string): Promise { + return json( + await fetch(`${SIGNALS_URL}/send-checkin`, { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ user_id: userId, phone }), + }) + ); +} diff --git a/frontend/src/services/config.ts b/frontend/src/services/config.ts index a867d7c..c359069 100644 --- a/frontend/src/services/config.ts +++ b/frontend/src/services/config.ts @@ -1,7 +1,12 @@ -// Owned by Rishith. On a physical phone, localhost won't reach the laptop — -// use the laptop's LAN IP. Find it with: ipconfig getifaddr en0 (macOS) -// Override without editing code via Expo extra / env if you prefer. -export const BACKEND_URL = "http://192.168.1.XXX:8001"; // CHANGE to your laptop's LAN IP +// Owned by Rishith. The phone can't reach this Mac over the LAN (firewall + VPN), +// so every backend service is exposed over its own cloudflare tunnel and the app +// talks to those public HTTPS URLs. (Quick-tunnel URLs are regenerated each run — +// if they change, update them here.) +export const BACKEND_URL = "https://moms-efficiency-standard-accessory.trycloudflare.com"; // :8001 orchestrator +export const ML_URL = "https://pad-metropolitan-beverly-sender.trycloudflare.com"; // :8003 TRIBE+HF+RAG, /chat +export const VIDEO_URL = "https://suffered-cpu-sustainable-sciences.trycloudflare.com"; // :8004 facial/voice, /converse, /tts +export const SIGNALS_URL = "https://whale-epinions-summary-dry.trycloudflare.com"; // :8002 SMS bot + export const DEFAULT_USER_ID = "demo_user"; // Polling cadence for the live burnout score (ms). diff --git a/frontend/src/types/index.ts b/frontend/src/types/index.ts index 3abf350..5b14c79 100644 --- a/frontend/src/types/index.ts +++ b/frontend/src/types/index.ts @@ -82,3 +82,29 @@ export interface VideoResult { voice: VoiceAnalysis; combined_score: number; } + +// One turn in the companion conversation (Talk tab). Sent to the ML /chat +// endpoint and rendered as a bubble. +export interface ChatMessage { + role: "user" | "assistant"; + content: string; +} + +// One past reading (check-in or talk) shown in the History tab. +export interface HistoryEntry { + score: number; + level: "green" | "yellow" | "red"; + timestamp: string; + text: string; + intervention: string; + kind: "talk" | "check-in"; +} + +// Returned by POST /converse (video service): one spoken turn of the voice call. +export interface ConverseResult { + transcript: string; // what the user said (NVIDIA parakeet STT) + reply: string; // Pegasus's reply text + voice: Partial & { error?: string }; // per-turn voice-stress read + audio_b64: string | null; // reply spoken aloud (NVIDIA Chatterbox), base64 WAV + sample_rate: number; +} diff --git a/frontend/src/utils/colors.ts b/frontend/src/utils/colors.ts index 2596dc0..04a8346 100644 --- a/frontend/src/utils/colors.ts +++ b/frontend/src/utils/colors.ts @@ -1,18 +1,23 @@ // Design tokens for Pegasus. Owned by Wesley (visual layer). -// "Tesla dashboard for your mind" — dark, premium, calm, futuristic. +// Apple-HIG light theme — clean, airy, high-contrast. Matches the pitch deck +// (apple.com / keynote aesthetic): off-white canvas, white elevated cards with +// hairline borders and soft shadows, Apple-blue accents, SF system type. export const COLORS = { - bg: "#0a0a0a", - card: "#12121a", - border: "#23232e", - text: "#ffffff", - textDim: "#9aa0aa", - green: "#22c55e", - yellow: "#eab308", - red: "#ef4444", - blue: "#3b82f6", + bg: "#f5f5f7", // Apple off-white canvas + card: "#ffffff", // elevated surfaces (hairline border + soft shadow) + border: "#d2d2d7", // hairline separators / borders + text: "#1d1d1f", // primary text + textDim: "#6e6e73", // secondary / dim text + green: "#34C759", // engine-light: calm + yellow: "#FF9F0A", // engine-light: elevated (Apple amber) + red: "#FF3B30", // engine-light: alert + blue: "#0071e3", // accent: primary / links / active (Apple blue) } as const; +// Tertiary text — quieter than textDim, for the lightest captions/footnotes. +export const TEXT_TERTIARY = "#86868b"; + export type BurnoutLevel = "green" | "yellow" | "red"; // Map a burnout level to its accent hex. Falls back to dim text for unknowns. @@ -39,7 +44,7 @@ export const SPACING = { xxl: 48, } as const; -// Corner radii — cards sit in the 16-20 range per design spec. +// Corner radii — Apple-ish soft rounding. Cards sit in the 16-20 range. export const RADIUS = { sm: 10, md: 16, @@ -47,13 +52,14 @@ export const RADIUS = { pill: 999, } as const; -// Typographic scale. Big numerals for scores, calm body copy. +// Typographic scale. Big, tight SF headlines; calm body copy. The app uses the +// platform system font (-apple-system / SF) via React Native defaults. export const TYPE = { - hero: { fontSize: 64, fontWeight: "800" as const, letterSpacing: -1 }, - score: { fontSize: 48, fontWeight: "800" as const, letterSpacing: -0.5 }, - title: { fontSize: 28, fontWeight: "700" as const, letterSpacing: -0.3 }, - heading: { fontSize: 20, fontWeight: "700" as const }, - body: { fontSize: 16, fontWeight: "500" as const }, + hero: { fontSize: 64, fontWeight: "700" as const, letterSpacing: -1.5 }, + score: { fontSize: 48, fontWeight: "700" as const, letterSpacing: -1 }, + title: { fontSize: 28, fontWeight: "700" as const, letterSpacing: -0.4 }, + heading: { fontSize: 20, fontWeight: "600" as const, letterSpacing: -0.2 }, + body: { fontSize: 16, fontWeight: "400" as const }, label: { fontSize: 13, fontWeight: "600" as const, letterSpacing: 0.4 }, - caption: { fontSize: 12, fontWeight: "500" as const }, + caption: { fontSize: 12, fontWeight: "400" as const }, } as const; diff --git a/frontend/src/utils/formatting.ts b/frontend/src/utils/formatting.ts index 768d38f..40fc628 100644 --- a/frontend/src/utils/formatting.ts +++ b/frontend/src/utils/formatting.ts @@ -42,18 +42,20 @@ export function titleCase(s: string): string { .join(" "); } -// Map a burnout level to its dashboard status copy. +// Map a wellness level to its dashboard status copy. +// Wellness is 0..100 where higher = better: green is thriving, yellow neutral, +// red is running low. export function scoreLabel( level: string -): "Systems Normal" | "Warning: Check In" | "Alert: Take Action" { +): "Thriving" | "Keep an eye out" | "Running low" { switch (level) { case "green": - return "Systems Normal"; + return "Thriving"; case "yellow": - return "Warning: Check In"; + return "Keep an eye out"; case "red": - return "Alert: Take Action"; + return "Running low"; default: - return "Systems Normal"; + return "Thriving"; } } diff --git a/ml/cache/baselines.json b/ml/cache/baselines.json new file mode 100644 index 0000000..58ad85a --- /dev/null +++ b/ml/cache/baselines.json @@ -0,0 +1,156 @@ +{ + "audio_01": { + "stimulus_id": "audio_01", + "regions": { + "prefrontal_cortex": 0.159, + "amygdala_region": 0.0, + "temporal_lobe": 0.325, + "motor_cortex": 0.471, + "visual_cortex": 0.0 + }, + "macro_groups": { + "Attention": 0.129, + "Auditory": 0.46, + "Emotion": 0.0, + "Language": 0.189, + "Motor": 0.471, + "Visual": 0.0 + }, + "overall": 0.208, + "total_timesteps": 6, + "agent_summary": "TRIBE v2 cortical trajectory: 6 timesteps (TR=1.49s), peak at t=1. Dominant macro groups at peak: Motor (0.47), Auditory (0.46), Language (0.19). fMRI predictions averaged over Yeo 7-network parcellation; hemodynamic lag (~5s) already compensated by TRIBE.", + "source": "tribe-v2" + }, + "img_calm_02": { + "stimulus_id": "img_calm_02", + "regions": { + "prefrontal_cortex": 0.162, + "amygdala_region": 0.0, + "temporal_lobe": 0.306, + "motor_cortex": 0.448, + "visual_cortex": 0.019 + }, + "macro_groups": { + "Attention": 0.16, + "Auditory": 0.449, + "Emotion": 0.0, + "Language": 0.163, + "Motor": 0.448, + "Visual": 0.019 + }, + "overall": 0.207, + "total_timesteps": 6, + "agent_summary": "TRIBE v2 cortical trajectory: 6 timesteps (TR=1.49s), peak at t=1. Dominant macro groups at peak: Auditory (0.45), Motor (0.45), Language (0.16). fMRI predictions averaged over Yeo 7-network parcellation; hemodynamic lag (~5s) already compensated by TRIBE.", + "source": "tribe-v2" + }, + "img_calm_03": { + "stimulus_id": "img_calm_03", + "regions": { + "prefrontal_cortex": 0.115, + "amygdala_region": 0.0, + "temporal_lobe": 0.24, + "motor_cortex": 0.357, + "visual_cortex": 0.051 + }, + "macro_groups": { + "Attention": 0.118, + "Auditory": 0.367, + "Emotion": 0.0, + "Language": 0.113, + "Motor": 0.357, + "Visual": 0.051 + }, + "overall": 0.168, + "total_timesteps": 6, + "agent_summary": "TRIBE v2 cortical trajectory: 6 timesteps (TR=1.49s), peak at t=1. Dominant macro groups at peak: Auditory (0.37), Motor (0.36), Attention (0.12). fMRI predictions averaged over Yeo 7-network parcellation; hemodynamic lag (~5s) already compensated by TRIBE.", + "source": "tribe-v2" + }, + "calm_02": { + "stimulus_id": "calm_02", + "regions": { + "prefrontal_cortex": 0.159, + "amygdala_region": 0.0, + "temporal_lobe": 0.296, + "motor_cortex": 0.415, + "visual_cortex": 0.022 + }, + "macro_groups": { + "Attention": 0.179, + "Auditory": 0.454, + "Emotion": 0.0, + "Language": 0.138, + "Motor": 0.415, + "Visual": 0.022 + }, + "overall": 0.201, + "total_timesteps": 5, + "agent_summary": "TRIBE v2 cortical trajectory: 5 timesteps (TR=1.49s), peak at t=1. Dominant macro groups at peak: Auditory (0.45), Motor (0.41), Attention (0.18). fMRI predictions averaged over Yeo 7-network parcellation; hemodynamic lag (~5s) already compensated by TRIBE.", + "source": "tribe-v2" + }, + "img_calm_01": { + "stimulus_id": "img_calm_01", + "regions": { + "prefrontal_cortex": 0.074, + "amygdala_region": 0.0, + "temporal_lobe": 0.242, + "motor_cortex": 0.41, + "visual_cortex": 0.0 + }, + "macro_groups": { + "Attention": 0.077, + "Auditory": 0.414, + "Emotion": 0.0, + "Language": 0.071, + "Motor": 0.41, + "Visual": 0.0 + }, + "overall": 0.162, + "total_timesteps": 5, + "agent_summary": "TRIBE v2 cortical trajectory: 5 timesteps (TR=1.49s), peak at t=0. Dominant macro groups at peak: Auditory (0.41), Motor (0.41), Attention (0.08). fMRI predictions averaged over Yeo 7-network parcellation; hemodynamic lag (~5s) already compensated by TRIBE.", + "source": "tribe-v2" + }, + "calm_01": { + "stimulus_id": "calm_01", + "regions": { + "prefrontal_cortex": 0.106, + "amygdala_region": 0.0, + "temporal_lobe": 0.27, + "motor_cortex": 0.446, + "visual_cortex": 0.0 + }, + "macro_groups": { + "Attention": 0.083, + "Auditory": 0.413, + "Emotion": 0.0, + "Language": 0.128, + "Motor": 0.446, + "Visual": 0.0 + }, + "overall": 0.178, + "total_timesteps": 5, + "agent_summary": "TRIBE v2 cortical trajectory: 5 timesteps (TR=1.49s), peak at t=1. Dominant macro groups at peak: Motor (0.45), Auditory (0.41), Language (0.13). fMRI predictions averaged over Yeo 7-network parcellation; hemodynamic lag (~5s) already compensated by TRIBE.", + "source": "tribe-v2" + }, + "img_activating_03": { + "stimulus_id": "img_activating_03", + "regions": { + "prefrontal_cortex": 0.088, + "amygdala_region": 0.0, + "temporal_lobe": 0.244, + "motor_cortex": 0.461, + "visual_cortex": 0.0 + }, + "macro_groups": { + "Attention": 0.091, + "Auditory": 0.402, + "Emotion": 0.0, + "Language": 0.085, + "Motor": 0.461, + "Visual": 0.0 + }, + "overall": 0.173, + "total_timesteps": 5, + "agent_summary": "TRIBE v2 cortical trajectory: 5 timesteps (TR=1.49s), peak at t=1. Dominant macro groups at peak: Motor (0.46), Auditory (0.40), Attention (0.09). fMRI predictions averaged over Yeo 7-network parcellation; hemodynamic lag (~5s) already compensated by TRIBE.", + "source": "tribe-v2" + } +} \ No newline at end of file diff --git a/ml/chat.py b/ml/chat.py new file mode 100644 index 0000000..98be562 --- /dev/null +++ b/ml/chat.py @@ -0,0 +1,170 @@ +"""Conversational companion — multi-turn chat grounded in the user's state. + +Hugging Face only (no Anthropic). The model is Pegasus: a warm "check engine +light" companion, NOT a therapist. Each turn is grounded in: + - the running conversation (last ~12 messages), + - the user's current burnout reading (score/level), and + - coping snippets retrieved from the mental-health corpus (reuses the RAG + retriever from rag_intervention.py). + +Everything degrades gracefully: if HF is unreachable, ``reply()`` still returns +a warm, contextual response built from the retrieved snippet + a reflective +follow-up, so the conversation never dead-ends. ``reply()`` never raises. +""" +from __future__ import annotations + +import os +import re +from typing import Dict, List, Optional + +HF_TOKEN = os.getenv("HF_TOKEN") +CHAT_MODEL = os.getenv("HF_MODEL", "Qwen/Qwen2.5-7B-Instruct") + +_MAX_TURNS = 12 # trailing messages kept as context + +_SYSTEM = ( + "You are Pegasus — a warm, grounded companion inside a mental-health " + '"check engine light" app. You are NOT a therapist and you never diagnose. ' + "You listen first, reflect back what you hear in plain language, and offer " + "one small, concrete next step only when it helps. Keep replies short: 1-3 " + "sentences, conversational, no bullet lists, no clinical jargon. " + "CRUCIAL: respond to the SPECIFIC thing they just said — quote or name the exact " + "detail back (the person, the task, the deadline, the feeling, the event they " + "mentioned) and react to THAT. Never give a generic, templated, or " + "one-size-fits-all reply, and never repeat a stock phrase. If they mention " + "something concrete, your reply must reference it directly. " + "Ask a gentle, specific follow-up that builds on what they actually said. " + "If someone over-reassures or insists they're fine (\"I'm fine, really, don't " + "worry, all good\"), don't just take it at face value — warmly acknowledge it, " + "then gently invite one honest detail about how their day actually went." +) + +_CRISIS_RE = re.compile( + r"\b(kill myself|suicide|suicidal|end it all|want to die|self.?harm|hurt myself)\b", + re.I, +) +_CRISIS_REPLY = ( + "I'm really glad you told me. I can't be the right support for this on my own — " + "please reach out right now to someone you trust or a crisis line (in the US, " + "call or text 988). You don't have to carry this alone." +) + +_NEG = { + "tired", "exhausted", "drained", "burnt", "burned", "stressed", "anxious", + "overwhelmed", "sad", "down", "lonely", "angry", "frustrated", "hopeless", + "can't", "cant", "struggling", "worried", "scared", "numb", "empty", +} + +_rag_engine = None + + +def _rag(): + global _rag_engine + if _rag_engine is None: + from rag_intervention import RagInterventionEngine + + _rag_engine = RagInterventionEngine() + return _rag_engine + + +def _trim(messages: List[Dict]) -> List[Dict]: + """Keep the last few user/assistant turns with clean role/content shape.""" + out: List[Dict] = [] + for m in messages or []: + role = m.get("role") + content = (m.get("content") or "").strip() + if role in ("user", "assistant") and content: + out.append({"role": role, "content": content}) + return out[-_MAX_TURNS:] + + +def _last_user(messages: List[Dict]) -> str: + for m in reversed(messages or []): + if m.get("role") == "user" and (m.get("content") or "").strip(): + return m["content"].strip() + return "" + + +class ChatEngine: + def __init__(self) -> None: + self._client = None # lazy huggingface_hub.InferenceClient + + def _hf(self): + if self._client is None: + from huggingface_hub import InferenceClient + + self._client = InferenceClient(token=HF_TOKEN) + return self._client + + def reply( + self, + messages: List[Dict], + score: Optional[int] = None, + level: Optional[str] = None, + ) -> str: + """One companion turn. Never raises; always returns a non-empty string.""" + msgs = _trim(messages) + last = _last_user(msgs) + + if _CRISIS_RE.search(last): + return _CRISIS_REPLY + + grounding = self._grounding(last, level) + + if HF_TOKEN: + try: + system = _SYSTEM + if score is not None: + system += ( + f"\n\nPrivate context (do not state the number unless asked): the " + f"person's current Pegasus burnout reading is {score}/100 ({level}). " + "Let it shape your tone, not your words." + ) + if grounding: + system += ( + "\n\nCoping notes you may quietly draw from (never quote verbatim):\n" + + grounding + ) + resp = self._hf().chat_completion( + model=CHAT_MODEL, + max_tokens=160, # short replies → snappier voice turnaround + temperature=0.8, + messages=[{"role": "system", "content": system}] + msgs, + ) + text = (resp.choices[0].message.content or "").strip() + if text: + return text + except Exception: + pass + + return self._fallback(last, level) + + def _grounding(self, query: str, level: Optional[str]) -> str: + try: + retrieved = _rag().retrieve(level or "any", [query] if query else [], k=2) + return "\n".join(f"- {s['text']}" for s in retrieved) + except Exception: + return "" + + def _fallback(self, last_user: str, level: Optional[str]) -> str: + """Warm, contextual reply with no model — reflect + ground + follow up.""" + tokens = set(re.findall(r"[a-z']+", last_user.lower())) + heavy = bool(tokens & _NEG) + + try: + retrieved = _rag().retrieve(level or "any", [last_user] if last_user else [], k=1) + tip = retrieved[0]["text"] if retrieved else "" + except Exception: + tip = "" + + if not last_user: + return "I'm here. What's on your mind right now?" + + opener = ( + "That sounds heavy, and it makes sense you'd feel it. " + if heavy + else "Thanks for sharing that with me. " + ) + follow = "What does that look like for you today?" if heavy else "Tell me a little more?" + body = f"{tip} " if tip else "" + return f"{opener}{body}{follow}".strip() diff --git a/ml/main.py b/ml/main.py index 616671d..d913e27 100644 --- a/ml/main.py +++ b/ml/main.py @@ -10,17 +10,34 @@ """ from __future__ import annotations +from datetime import datetime, timezone +from typing import List, Optional + from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware from pydantic import BaseModel import tribe_client +from chat import ChatEngine from scoring import BurnoutScorer app = FastAPI(title="Pegasus ML") app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"]) scorer = BurnoutScorer() +chat_engine = ChatEngine() + +# Live wellness store — the app reads /wellness for the latest DERIVED reading. +# Every real turn (a texted reply or a voice-call turn) calls /score, which +# updates this, so the number actually moves instead of sitting on one value. +_latest: dict = {} +# Per-user history of readings (past check-ins + talks) for the History tab. +_history: dict = {} +_HISTORY_MAX = 60 + + +def _now() -> str: + return datetime.now(timezone.utc).isoformat() def _baseline(stimulus_id: str): @@ -34,6 +51,18 @@ class ScoreReq(BaseModel): signals: dict +class ChatMessage(BaseModel): + role: str + content: str + + +class ChatReq(BaseModel): + user_id: str + messages: List[ChatMessage] + score: Optional[int] = None + level: Optional[str] = None + + @app.get("/health") def health(): return {"status": "ml running", "tribe_api": tribe_client.TRIBE_API} @@ -42,7 +71,59 @@ def health(): @app.post("/score") def score(req: ScoreReq): baseline = _baseline(req.stimulus_id) - return scorer.compute_deviation(baseline, req.signals) + result = scorer.compute_deviation(baseline, req.signals) + result["timestamp"] = _now() + result["user_id"] = req.user_id + _latest[req.user_id] = result # feed the live wellness reading + + kind = "talk" if req.stimulus_id == "conversation" else "check-in" + entry = { + "score": result["score"], + "level": result["level"], + "timestamp": result["timestamp"], + "text": (req.signals.get("response_text") or "").strip()[:160], + "intervention": result.get("intervention", ""), + "kind": kind, + } + _history.setdefault(req.user_id, []).append(entry) + _history[req.user_id] = _history[req.user_id][-_HISTORY_MAX:] + return result + + +@app.get("/history/{user_id}") +def history(user_id: str): + """Past check-ins + talks (most recent first) for the History tab.""" + return list(reversed(_history.get(user_id, []))) + + +@app.get("/wellness/{user_id}") +def wellness(user_id: str): + """Latest DERIVED wellness reading (higher = better). Updates every time the + user talks or texts; neutral starting state until their first turn.""" + r = _latest.get(user_id) + if r: + return r + return { + "user_id": user_id, + "score": 72, + "level": "green", + "intervention": "Talk to Pegasus or reply to a text to get your first reading.", + "top_indicators": ["No reading yet — start a conversation"], + "tribe_deviation": 0, + "behavioral_deviation": 0, + "brain_regions_flagged": [], + "confidence": 0.5, + "breakdown": {}, + "timestamp": _now(), + "source": "starting", + } + + +@app.post("/chat") +def chat(req: ChatReq): + """Multi-turn companion chat, grounded in the user's burnout reading + RAG.""" + msgs = [{"role": m.role, "content": m.content} for m in req.messages] + return {"reply": chat_engine.reply(msgs, req.score, req.level)} @app.get("/baseline/{stimulus_id}") diff --git a/ml/scoring.py b/ml/scoring.py index 5789eca..8443a12 100644 --- a/ml/scoring.py +++ b/ml/scoring.py @@ -17,6 +17,33 @@ _POSITIVE_EMOTIONS = {"joy", "neutral", "surprise"} +# Direct mental-health language. The emotion model can badly under-read terse +# replies (a one-word "death" may come back near-neutral), so concerning words +# floor the message sub-score and crisis words push it to the top. A check +# engine light must never miss these. +_CRISIS_TERMS = ( + "suicid", "kill myself", "killing myself", "want to die", "end my life", + "hurt myself", "self harm", "self-harm", "better off dead", "no reason to live", +) +_CONCERN_TERMS = ( + "death", "dying", "dead", "hopeless", "worthless", "empty", "numb", + "can't go on", "cant go on", "give up", "giving up", "no point", "end it", + "can't cope", "cant cope", "breaking down", "burnt out", "burned out", + "exhausted", "overwhelmed", "can't sleep", "cant sleep", "alone", "trapped", +) + +# Over-reassurance is a classic burnout MASK — "I'm fine, really, don't worry, +# all good." Taking it at face value is exactly how invisible burnout slips +# through, so when several reassurance markers cluster we treat it as a flag. +_MASKING_TERMS = ( + "i'm fine", "im fine", "totally fine", "perfectly fine", "completely fine", + "i'm okay", "im okay", "i'm ok", "im ok", "i'm alright", "im alright", + "i'm good", "im good", "all good", "everything's fine", "everything is fine", + "everything's good", "everything is good", "don't worry", "dont worry", + "no worries", "it's nothing", "its nothing", "nothing's wrong", "nothing wrong", + "i'm not stressed", "im not stressed", "no big deal", "i'll be fine", "ill be fine", +) + _rag_engine = None # lazy RagInterventionEngine singleton @@ -70,6 +97,21 @@ def compute_deviation(self, tribe_baseline: Optional[Dict], signals: Dict) -> Di # Behavioral burnout sub-scores (0-100; higher = more burnout signal). imsg = (1 - sentiment) * 100 + # Concerning / crisis language overrides a soft emotion-model read. + text_l = (signals.get("response_text") or "").lower() + masking = 0 + if any(t in text_l for t in _CRISIS_TERMS): + imsg = 100.0 + else: + concern = sum(1 for t in _CONCERN_TERMS if t in text_l) + if concern: + imsg = max(imsg, min(88.0 + (concern - 1) * 4.0, 100.0)) + # Clustered over-reassurance ("I'm fine, totally fine, don't worry") + # reads as positive to the emotion model but is a masking tell — pull + # it out of the green and into at least a yellow caution. + masking = sum(1 for t in _MASKING_TERMS if t in text_l) + if masking >= 2: + imsg = max(imsg, min(52.0 + masking * 7.0, 82.0)) typing = ( min(error_rate / 20, 1) * 0.40 + (1 - min(wpm / 80, 1)) * 0.35 @@ -89,19 +131,31 @@ def compute_deviation(self, tribe_baseline: Optional[Dict], signals: Dict) -> Di deviation = max(0.0, expected - actual) tribe = min(deviation * 100, 100) - # PRD §4.5 weighted fusion, renormalized over the streams present - # (a text-only check-in has no facial/voice). + # PRD §4.5 weighted fusion, renormalized over the streams ACTUALLY + # present. A text-only check-in (SMS) has no facial/voice and no real + # keystroke biometrics — fusing fake typing defaults would only dilute + # the one real signal (what they wrote), so typing is fused only when + # real typing data was captured (the in-app Pulse screen sends it). weights = {"imessage": 0.25, "typing": 0.20, "facial": 0.30, "voice": 0.15, "tribe": 0.10} - subs = {"imessage": imsg, "typing": typing, "tribe": tribe} + has_typing = "typing_wpm" in signals or "error_rate" in signals + subs = {"imessage": imsg, "tribe": tribe} + if has_typing: + subs["typing"] = typing if facial > 0: subs["facial"] = facial if voice > 0: subs["voice"] = voice total_w = sum(weights[k] for k in subs) - score = min(int(round(sum(subs[k] * weights[k] for k in subs) / total_w)), 100) - level = "green" if score < 30 else "yellow" if score < 65 else "red" + burnout = min(int(round(sum(subs[k] * weights[k] for k in subs) / total_w)), 100) + # Pegasus reports a WELLNESS score — HIGHER IS BETTER. Internally we fuse + # burnout/stress sub-scores, then flip so the number people see rises as + # they're doing better. green = thriving, red = running low. + score = 100 - burnout + level = "green" if score >= 70 else "yellow" if score >= 40 else "red" ind: List[str] = [] + if masking >= 2: + ind.append("Strong reassurance — this can mask how you really feel") if error_rate > 10: ind.append("Elevated typing errors suggest cognitive strain") if rt > 8000: diff --git a/pitch.html b/pitch.html new file mode 100644 index 0000000..0bcc088 --- /dev/null +++ b/pitch.html @@ -0,0 +1,445 @@ + + + + + +Pegasus + + + + + + + + + + + +
+
+ + +
+
1 / 1
+ + +
+ +
+ + + + diff --git a/signals/alerts/twilio_sms.py b/signals/alerts/twilio_sms.py index 9d90339..f669074 100644 --- a/signals/alerts/twilio_sms.py +++ b/signals/alerts/twilio_sms.py @@ -1,11 +1,31 @@ import os +import time +import logging +import platform +import subprocess +from collections import deque import requests from urllib.parse import quote from dotenv import load_dotenv load_dotenv() +log = logging.getLogger("signals.sms") + +# Recent outbound text, so the inbound iMessage poller can tell the user's +# replies apart from Pegasus's own messages echoed in the same thread. +_SENT = deque(maxlen=60) + + +def recently_sent(text: str, window: float = 900) -> bool: + t = (text or "").strip() + now = time.time() + return any(t == s and now - ts < window for s, ts in _SENT) + BLOOIO_BASE = "https://backend.blooio.com/v2/api" +# Prefer the Mac's iMessage over Bloo.io (free, local) when set — handy once the +# Bloo.io free trial runs out. Default: try Bloo.io first, iMessage as fallback. +PREFER_IMESSAGE = os.getenv("SMS_PREFER_IMESSAGE", "").lower() in ("1", "true", "yes") USER_PHONES: dict[str, str] = {} # user_id -> phone @@ -17,18 +37,119 @@ def _headers() -> dict: } -def send_sms(phone: str, body: str, media_url: str = None) -> str: +def _download_image(url: str) -> str | None: + """Download a remote image to a temp file so iMessage can attach the real PNG + instead of pasting a link.""" + if not url: + return None + try: + import tempfile + + r = requests.get(url, timeout=15) + r.raise_for_status() + ext = ".png" if ".png" in url.lower() else ".jpg" + path = tempfile.mktemp(suffix=ext) + with open(path, "wb") as f: + f.write(r.content) + return path + except Exception as e: + log.warning("image download failed: %s", e) + return None + + +# Send FROM this iMessage account (an Apple ID / email) so Pegasus shows up as a +# SEPARATE sender instead of "you". Empty = use the first iMessage account (which, +# if it's the same Apple ID as your phone, will show as yourself). +IMESSAGE_FROM = os.getenv("IMESSAGE_FROM", "").strip() + + +def _send_imessage(phone: str, body: str, attachment: str = None) -> str | None: + """Send via the Mac's Messages app (free, local). When `attachment` is given + it sends the actual image FILE (not a link). Sends from IMESSAGE_FROM if set + (so Pegasus is a distinct sender). Returns 'imessage_local' on success.""" + if platform.system() != "Darwin" or not phone: + return None + target = phone.replace('"', "") + sends = [] + if attachment and os.path.exists(attachment): + sends.append(f'send (POSIX file "{attachment}") to bud') + if body: + text = body.replace("\\", "\\\\").replace('"', '\\"').replace("\n", " ") + sends.append(f'send "{text}" to bud') + if not sends: + return None + + if IMESSAGE_FROM: + acct = IMESSAGE_FROM.replace('"', "") + svc_lines = ( + " set svc to missing value\n" + " repeat with s in (every service whose service type is iMessage)\n" + f' if (id of s) contains "{acct}" or (name of s) contains "{acct}" then\n' + " set svc to s\n exit repeat\n end if\n end repeat\n" + " if svc is missing value then set svc to 1st service whose service type is iMessage\n" + ) + else: + svc_lines = " set svc to 1st service whose service type is iMessage\n" + + script = ( + 'tell application "Messages"\n' + + svc_lines + + f' set bud to participant "{target}" of svc\n' + + " " + "\n ".join(sends) + "\n" + + "end tell" + ) + try: + r = subprocess.run(["osascript", "-e", script], capture_output=True, text=True, timeout=30) + if r.returncode == 0: + return "imessage_local" + log.warning("iMessage send failed: %s", (r.stderr or "").strip()[:160]) + except Exception as e: + log.warning("iMessage error: %s", e) + return None + + +def _send_bloo(phone: str, body: str, media_url: str = None) -> str | None: + """Send via Bloo.io. Returns the message id, or None on failure / no key.""" + if not os.getenv("BLOOIO_API_KEY"): + return None chat_id = quote(phone, safe="") payload = {"text": body} - resp = requests.post( - f"{BLOOIO_BASE}/chats/{chat_id}/messages", - headers=_headers(), - json=payload, - timeout=15, - ) - resp.raise_for_status() - data = resp.json() - return data.get("message_id", "sent") + if media_url: + payload["attachments"] = [media_url] + try: + resp = requests.post(f"{BLOOIO_BASE}/chats/{chat_id}/messages", + headers=_headers(), json=payload, timeout=15) + resp.raise_for_status() + return resp.json().get("message_id", "sent") + except Exception as e: + log.warning("Bloo.io send failed: %s", e) + return None + + +def send_sms(phone: str, body: str, media_url: str = None) -> str: + """Deliver a message. Tries Bloo.io (iMessage/SMS gateway) first, then falls + back to the Mac's own iMessage so messages still reach the phone for free when + Bloo.io is unavailable (e.g. free-trial credits exhausted). Never raises.""" + _SENT.append((body.strip(), time.time())) # so the inbound poller skips our echo + if not PREFER_IMESSAGE: + sid = _send_bloo(phone, body, media_url) + if sid: + return sid + + # iMessage fallback — download + attach the ACTUAL image, not a link. + attach = _download_image(media_url) if media_url else None + local = _send_imessage(phone, body, attach) + if local: + return local + + if PREFER_IMESSAGE: # iMessage was first choice and failed — try Bloo as backup + sid = _send_bloo(phone, body, media_url) + if sid: + return sid + + if not os.getenv("BLOOIO_API_KEY"): + return "noop_no_api_key" + return "payment_required: Bloo.io credits exhausted — add credits, or sign into iMessage on this Mac." def send_alert_sms(user_id: str, score: int, intervention: str) -> str: diff --git a/signals/main.py b/signals/main.py index c70219a..9c2f3f9 100644 --- a/signals/main.py +++ b/signals/main.py @@ -178,15 +178,51 @@ async def send_checkin(req: CheckinRequest): raise HTTPException(500, str(e)) +class ReminderRequest(BaseModel): + user_id: str + text: str + + +@app.post("/schedule-reminder") +def schedule_reminder_route(req: ReminderRequest): + """Parse a reminder from free text ('text me in 15 min to take a break') and + schedule an SMS to the user's registered phone. Used by the voice call.""" + from sms_bot.reminders import parse_reminder, schedule_reminder, human_delay + rem = parse_reminder(req.text) + if not rem: + return {"scheduled": False, "reason": "no_reminder"} + phone = USER_PHONES.get(req.user_id) + if not phone: + return {"scheduled": False, "reason": "no_phone"} + delay, task = rem + schedule_reminder(phone, delay, task) + return {"scheduled": True, "delay": delay, "task": task, "human": human_delay(delay)} + + @app.post("/sms/webhook") async def sms_webhook(request: Request): - """Bloo.io inbound SMS webhook — register this URL in Bloo.io dashboard.""" + """Bloo.io inbound webhook. Payload is a flat WebhookEventPayload with + `event`, `sender` (phone), and `text`. We only act on genuine inbound + messages — ignore our own outbound echoes + delivery/status events so we + don't score messages Pegasus itself sent.""" payload = await request.json() - from_number = payload.get("from") or payload.get("sender") or payload.get("phone", "") + event = (payload.get("event") or "").lower() + if event and event not in ("message.received", "message.inbound"): + return {"status": "ignored", "event": event} + from_number = payload.get("sender") or payload.get("from") or payload.get("phone", "") body = payload.get("text") or payload.get("body") or payload.get("message", "") + if not from_number or not body: + return {"status": "ignored", "reason": "missing sender/text"} return await handle_incoming_sms(from_number, body) +@app.on_event("startup") +async def _start_imessage_inbound(): + """Read the user's iMessage replies locally (Bloo.io webhook replacement).""" + from sms_bot.imessage_poller import start + start() + + if __name__ == "__main__": import uvicorn uvicorn.run("main:app", host="0.0.0.0", port=8002, reload=True) diff --git a/signals/sms_bot/bot.py b/signals/sms_bot/bot.py index 0ad39d5..bb54876 100644 --- a/signals/sms_bot/bot.py +++ b/signals/sms_bot/bot.py @@ -1,25 +1,95 @@ import os +import json import time +import random +import asyncio +from pathlib import Path + import httpx + from alerts.twilio_sms import send_sms -from alerts.templates import daily_stimulus -BACKEND = os.getenv("BACKEND_URL", "http://localhost:8001") +ML_URL = os.getenv("ML_URL", "http://localhost:8003").rstrip("/") + +# Canonical stimulus catalog (shared/stimuli.json). We select from it directly so +# the stimulus_id we record matches the catalog the ML TRIBE baseline resolves +# against (ml/tribe_client.py reads the same file by id), and so we have the media +# `url` on hand for the MMS send. +STIMULI = Path(__file__).resolve().parents[2] / "shared" / "stimuli.json" + +# Evocative media: image/video stimuli in the "activating" or "calm" categories +# drive divergent stress responses — the whole point of the pulse check. +MEDIA_TYPES = {"image", "video"} +EVOCATIVE_CATEGORIES = {"activating", "calm"} + +# Short prompt sent as the MMS text body alongside the media stimulus. +MEDIA_PROMPT = "Quick check-in 📷 — look at this for a moment, then reply with the first feeling that comes up." + +SENT_STIMULI: dict[str, dict] = {} # phone -> {user_id, stimulus_id, sent_at, type} + +# Alternate calm/activating across sends so the same user sees divergent stimuli. +_last_category: str | None = None + + +def _load_stimuli() -> list[dict]: + data = json.loads(STIMULI.read_text()) + return data.get("stimuli", []) if isinstance(data, dict) else data -SENT_STIMULI: dict[str, dict] = {} # phone -> {user_id, stimulus_id, sent_at} + +def select_stimulus() -> dict: + """Pick an emotionally-evocative media stimulus. + + Prefers image/video in the activating|calm categories, alternating category + between sends. Falls back to any text stimulus if no media exists. + """ + global _last_category + stimuli = _load_stimuli() + + media = [s for s in stimuli + if s.get("type") in MEDIA_TYPES and s.get("category") in EVOCATIVE_CATEGORIES] + if not media: + # No media available — fall back to any text stimulus so the check-in still goes out. + text = [s for s in stimuli if s.get("type") == "text"] or stimuli + return random.choice(text) + + # Alternate calm <-> activating when both are present. + want = "activating" if _last_category == "calm" else "calm" + pool = [s for s in media if s.get("category") == want] or media + chosen = random.choice(pool) + _last_category = chosen.get("category") + return chosen async def send_daily_pulse_check(user_id: str, phone: str) -> str: - async with httpx.AsyncClient(timeout=10) as client: - res = await client.get(f"{BACKEND}/stimulus/today/{user_id}") - stimulus = res.json() + stimulus = select_stimulus() + media_url = stimulus.get("url") or None - body = daily_stimulus(stimulus["prompt"]) - sid = send_sms(phone, body) + # Media stimulus → short evocative prompt as the body; plain text stimulus → + # use the stimulus' own prompt (no media to look at). + body = MEDIA_PROMPT if media_url else stimulus.get("prompt", MEDIA_PROMPT) + sid = send_sms(phone, body, media_url=media_url) SENT_STIMULI[phone] = { "user_id": user_id, "stimulus_id": stimulus["id"], "sent_at": time.time(), + "type": stimulus.get("type", "text"), } + + # Warm the Meta TRIBE v2 baseline for this stimulus in the background (it's a + # cold GPU call on first use). By the time they reply, scoring is fast — so + # the webhook responds quickly and Bloo.io never retries (no duplicate texts). + try: + asyncio.create_task(_warm_baseline(stimulus["id"])) + except RuntimeError: + pass # no running loop (e.g. called synchronously) — skip warmup + return sid + + +async def _warm_baseline(stimulus_id: str) -> None: + try: + async with httpx.AsyncClient(timeout=150) as client: + await client.get(f"{ML_URL}/baseline/{stimulus_id}") + except Exception: + pass diff --git a/signals/sms_bot/imessage_poller.py b/signals/sms_bot/imessage_poller.py new file mode 100644 index 0000000..7ab8855 --- /dev/null +++ b/signals/sms_bot/imessage_poller.py @@ -0,0 +1,121 @@ +"""Inbound iMessage reader — the local stand-in for Bloo.io's webhook. + +Bloo.io delivered the user's replies to us via webhook. Without it (free trial +out of credits), we instead poll the Mac's Messages database +(~/Library/Messages/chat.db) for the user's incoming replies and route them +through the SAME scoring + companion pipeline (handle_incoming_sms); responses +go back out over the Mac's iMessage. + +Needs Full Disk Access for this process (System Settings → Privacy & Security → +Full Disk Access → enable your terminal). Degrades to a logged no-op without it. +We distinguish the user's replies from Pegasus's own outbound by excluding text +we just sent (see alerts.twilio_sms.recently_sent), so it works whether the Mac +is on the same Apple ID or a separate "Pegasus" account. +""" +from __future__ import annotations + +import asyncio +import logging +import os +import sqlite3 +from pathlib import Path + +from sms_bot.webhook import handle_incoming_sms + +log = logging.getLogger("signals.imessage") + +DB = str(Path.home() / "Library" / "Messages" / "chat.db") +POLL_SECS = float(os.getenv("IMESSAGE_POLL_SECS", "3")) + +_last_rowid = 0 +_started = False + + +def _digits(s: str) -> str: + return "".join(c for c in (s or "") if c.isdigit())[-10:] + + +def _connect(): + return sqlite3.connect(f"file:{DB}?mode=ro&immutable=1", uri=True, timeout=3) + + +def available() -> bool: + try: + con = _connect() + con.execute("SELECT 1 FROM message LIMIT 1") + con.close() + return True + except Exception: + return False + + +def _max_rowid() -> int: + try: + con = _connect() + row = con.execute("SELECT COALESCE(MAX(ROWID), 0) FROM message").fetchone() + con.close() + return int(row[0]) if row else 0 + except Exception: + return 0 + + +def _fetch_new(registered: set) -> list: + """New text messages (since the last poll) in chats with a registered phone.""" + global _last_rowid + out = [] + try: + con = _connect() + cur = con.execute( + "SELECT m.ROWID, m.text, h.id " + "FROM message m LEFT JOIN handle h ON m.handle_id = h.ROWID " + "WHERE m.ROWID > ? ORDER BY m.ROWID ASC", + (_last_rowid,), + ) + for rowid, text, handle in cur.fetchall(): + _last_rowid = max(_last_rowid, rowid) + if not text or not text.strip(): + continue + if _digits(handle) not in registered: + continue + out.append((handle or "", text.strip())) + con.close() + except Exception as e: + log.warning("chat.db read failed: %s", e) + return out + + +async def _loop(): + global _last_rowid + _last_rowid = _max_rowid() # only act on messages that arrive from now on + log.info("iMessage inbound poller running (from ROWID %s).", _last_rowid) + from alerts.twilio_sms import USER_PHONES, recently_sent + + while True: + await asyncio.sleep(POLL_SECS) + try: + registered = {_digits(p) for p in USER_PHONES.values() if p} + if not registered: + continue + for handle, text in _fetch_new(registered): + if recently_sent(text): + continue # our own outbound, echoed in the thread + log.info("inbound iMessage from %s: %r", handle, text[:60]) + await handle_incoming_sms(handle, text) + except Exception as e: + log.warning("poller loop error: %s", e) + + +def start() -> None: + """Start the background poller if chat.db is reachable.""" + global _started + if _started: + return + if not available(): + log.info("iMessage inbound poller DISABLED — no chat.db access " + "(grant Full Disk Access to read incoming replies).") + return + _started = True + try: + asyncio.create_task(_loop()) + except RuntimeError: + _started = False # no running loop yet diff --git a/signals/sms_bot/reminders.py b/signals/sms_bot/reminders.py new file mode 100644 index 0000000..e595122 --- /dev/null +++ b/signals/sms_bot/reminders.py @@ -0,0 +1,75 @@ +"""Conversational reminders. When the user tells the agent "remind me to take a +break in 15 minutes" (by text or in the voice call), we parse the delay + task +and schedule an SMS that actually fires after that delay. + +Lives in signals because this is where SMS sending happens. Never raises. +""" +from __future__ import annotations + +import asyncio +import re +from typing import Optional, Tuple + +from alerts.twilio_sms import send_sms + +_UNIT_SECONDS = { + "second": 1, "seconds": 1, "sec": 1, "secs": 1, + "minute": 60, "minutes": 60, "min": 60, "mins": 60, + "hour": 3600, "hours": 3600, "hr": 3600, "hrs": 3600, +} +_NUM_WORDS = { + "a": 1, "an": 1, "one": 1, "two": 2, "three": 3, "four": 4, "five": 5, + "ten": 10, "fifteen": 15, "twenty": 20, "thirty": 30, "forty-five": 45, "sixty": 60, +} + +_INTENT_RE = re.compile(r"\b(?:remind|text|message|ping|nudge)\s+me\b", re.I) +_TIME_RE = re.compile( + r"\bin\s+(\d+|a|an|one|two|three|four|five|ten|fifteen|twenty|thirty|forty-five|sixty)\s*" + r"(seconds?|secs?|sec|minutes?|mins?|min|hours?|hrs?|hr)\b", + re.I, +) +_TASK_RE = re.compile(r"\bto\s+(.+?)(?:\s+in\s+\S+\s+(?:sec|min|hour|hr)|[.!?]|$)", re.I) + + +def parse_reminder(text: str) -> Optional[Tuple[int, str]]: + """(delay_seconds, task) if `text` asks for a reminder, else None.""" + if not text or not _INTENT_RE.search(text): + return None + tm = _TIME_RE.search(text) + if not tm: + return None + num_raw, unit = tm.group(1).lower(), tm.group(2).lower() + num = int(num_raw) if num_raw.isdigit() else _NUM_WORDS.get(num_raw, 1) + secs = _UNIT_SECONDS.get(unit) or _UNIT_SECONDS.get(unit.rstrip("s")) or 60 + delay = max(10, num * secs) + + tk = _TASK_RE.search(text) + task = (tk.group(1).strip() if tk else "take a break").rstrip(".!?, ") + if not task or len(task) > 80: + task = "take a break" + return delay, task + + +def human_delay(delay: int) -> str: + if delay >= 3600: + h = delay / 3600 + h = int(h) if h == int(h) else round(h, 1) + return f"{h} hour" + ("s" if h != 1 else "") + if delay >= 60: + m = round(delay / 60) + return f"{m} minute" + ("s" if m != 1 else "") + return f"{delay} seconds" + + +async def _fire(phone: str, delay: int, task: str) -> None: + await asyncio.sleep(delay) + send_sms(phone, f"⏰ Reminder: {task}. You asked me to nudge you — take a moment for yourself. 🌱") + + +def schedule_reminder(phone: str, delay: int, task: str) -> bool: + """Fire-and-forget an SMS after `delay` seconds. True if scheduled.""" + try: + asyncio.create_task(_fire(phone, delay, task)) + return True + except RuntimeError: + return False diff --git a/signals/sms_bot/stimulus_sender.py b/signals/sms_bot/stimulus_sender.py index c6f0066..d762cc7 100644 --- a/signals/sms_bot/stimulus_sender.py +++ b/signals/sms_bot/stimulus_sender.py @@ -3,4 +3,11 @@ async def trigger_checkin(user_id: str, phone: str) -> dict: sid = await send_daily_pulse_check(user_id, phone) - return {"sent": True, "sid": sid} + ok = not sid.startswith(("send_failed", "payment_required", "noop")) + detail = ( + "Bloo.io credits exhausted (free trial) — add credits to send texts." + if sid.startswith("payment_required") else + "SMS provider unavailable." if sid.startswith("send_failed") else + "No BLOOIO_API_KEY set." if sid.startswith("noop") else None + ) + return {"sent": ok, "sid": sid, **({"detail": detail} if detail else {})} diff --git a/signals/sms_bot/webhook.py b/signals/sms_bot/webhook.py index f87cb7f..ca8b874 100644 --- a/signals/sms_bot/webhook.py +++ b/signals/sms_bot/webhook.py @@ -5,48 +5,149 @@ from alerts.twilio_sms import send_sms from sms_bot.bot import SENT_STIMULI +ML_URL = os.getenv("ML_URL", "http://localhost:8003").rstrip("/") BACKEND = os.getenv("BACKEND_URL", "http://localhost:8001") +# Per-sender conversation memory so the SMS companion stays coherent across turns. +_history: dict[str, list] = {} # phone(last10) -> recent [{role,content}] turns +_HISTORY_MAX = 10 +# Exact-duplicate dedup: Bloo.io retries inbound webhooks if our handler is slow. +# We swallow a re-delivery of the SAME text within a short window; distinct +# messages are always processed. +_recent_msg: dict[str, tuple] = {} # phone(last10) -> (text, ts) +_DEDUP_SECS = 90 + + +def _digits(phone: str) -> str: + """Last 10 digits — tolerant phone match (Bloo.io may format `sender` + differently from how we stored it: +1XXXXXXXXXX vs 1XXXXXXXXXX vs spaces).""" + return "".join(c for c in (phone or "") if c.isdigit())[-10:] + + +def _lookup(from_number: str): + """Find the pending stimulus for a sender, matching on the last 10 digits.""" + if from_number in SENT_STIMULI: + return from_number, SENT_STIMULI[from_number] + target = _digits(from_number) + for key, val in SENT_STIMULI.items(): + if _digits(key) == target: + return key, val + return None, None + async def handle_incoming_sms(from_number: str, body: str) -> JSONResponse: - sent = SENT_STIMULI.get(from_number) + body = (body or "").strip() + if not body: + return JSONResponse({"status": "ignored", "reason": "empty"}) - if not sent: - send_sms(from_number, "Hey! I'll send you a pulse check soon. 🌱") - return JSONResponse({"status": "ok"}) + key, sent = _lookup(from_number) + digits = _digits(from_number) + now = time.time() - latency_ms = int((time.time() - sent["sent_at"]) * 1000) - score = await _get_score(sent, body, latency_ms) + # Swallow Bloo.io retries / duplicate deliveries of the SAME text only. + prev = _recent_msg.get(digits) + if prev and prev[0] == body and now - prev[1] < _DEDUP_SECS: + return JSONResponse({"status": "duplicate_ignored"}) + _recent_msg[digits] = (body, now) - level_emoji = {"green": "🟢", "yellow": "🟡", "red": "🔴"} - emoji = level_emoji.get(score.get("level", "green"), "🟢") - points = score.get("score", 50) - hint = score.get("intervention", "") - send_sms(from_number, f"{emoji} Pulse: {points}/100\n\n{hint}") + # Reminder request? "remind me to take a break in 15 minutes" → actually + # schedule an SMS that fires after the delay, and confirm. + from sms_bot.reminders import parse_reminder, schedule_reminder, human_delay - del SENT_STIMULI[from_number] - return JSONResponse({"status": "ok"}) + rem = parse_reminder(body) + if rem: + delay, task = rem + schedule_reminder(from_number, delay, task) + send_sms(from_number, f"Got it — I'll text you in {human_delay(delay)} to {task}. 👍") + return JSONResponse({"status": "ok", "mode": "reminder", "delay": delay, "task": task}) + + if sent: + # Reply to a pending check-in → score it against that stimulus. Claim it + # atomically (no await before the pop) so a retry can't double-score. + SENT_STIMULI.pop(key, None) + latency_ms = int((now - sent["sent_at"]) * 1000) + score = await _get_score(sent, body, latency_ms) + ai_line = await _companion_reply( + digits, sent["user_id"], body, score.get("score"), score.get("level") + ) + emoji = {"green": "🟢", "yellow": "🟡", "red": "🔴"}.get(score.get("level", "green"), "🟢") + points = score.get("score", 50) + hint = score.get("intervention", "") + msg = (f"{ai_line}\n\n{emoji} Pulse: {points}/100" + if ai_line else f"{emoji} Pulse: {points}/100\n\n{hint}") + send_sms(from_number, msg) + return JSONResponse({"status": "ok", "mode": "checkin", "score": points, "level": score.get("level")}) + + # No pending check-in → just talk. Pegasus is a companion you can message + # anytime, not a vending machine that only replies to its own prompts. + reply = await _companion_reply(digits, "sms_" + digits, body, None, None) + send_sms(from_number, reply or "I'm here with you. What's on your mind?") + return JSONResponse({"status": "ok", "mode": "chat"}) async def _get_score(sent: dict, body: str, latency_ms: int) -> dict: + """Score the reply against the stimulus. Primary path is the ML service + (real Meta TRIBE v2 on Modal + HF sentiment + RAG intervention) — TRIBE can + be slow the first time a stimulus is seen, so we allow a generous timeout. + Falls back to the backend orchestrator, then to a local heuristic.""" + # Over SMS we have no real keystroke biometrics, so we DON'T send typing + # signals — the scorer then fuses only the real streams (what they wrote + + # TRIBE), instead of letting fake typing defaults dilute a strong reply. + signals = { + "response_text": body, + "response_time_ms": min(latency_ms, 60_000), + "response_latency_ms": latency_ms, + } + # 1) ML service — TRIBE v2 + HF + RAG. + try: + async with httpx.AsyncClient(timeout=150) as client: + res = await client.post(f"{ML_URL}/score", json={ + "user_id": sent["user_id"], + "stimulus_id": sent["stimulus_id"], + "signals": signals, + }) + res.raise_for_status() + return res.json() + except Exception: + pass + # 2) Backend orchestrator (its schema wants typing fields — give neutral ones). try: - async with httpx.AsyncClient(timeout=10) as client: + async with httpx.AsyncClient(timeout=150) as client: res = await client.post(f"{BACKEND}/response/submit", json={ - "user_id": sent["user_id"], - "stimulus_id": sent["stimulus_id"], - "response_text": body, - "response_time_ms": min(latency_ms, 60_000), - "response_latency_ms": latency_ms, - "typing_wpm": 40, - "error_rate": 0, - "source": "sms", + "user_id": sent["user_id"], "stimulus_id": sent["stimulus_id"], + "response_text": body, "source": "sms", + "typing_wpm": 40, "error_rate": 0, **signals, }) res.raise_for_status() return res.json() except Exception: + # 3) Local heuristic — last resort so a reply is never dropped. return _local_score(body, latency_ms) +async def _companion_reply(digits: str, user_id: str, body: str, + score, level) -> str: + """One conversational companion turn (HF chat + RAG), grounded in the burnout + score/level when we have one, and remembering the recent conversation so it + stays coherent across texts. Returns '' on failure.""" + history = _history.get(digits, []) + messages = history + [{"role": "user", "content": body}] + reply = "" + try: + async with httpx.AsyncClient(timeout=60) as client: + res = await client.post(f"{ML_URL}/chat", json={ + "user_id": user_id, "messages": messages, "score": score, "level": level, + }) + res.raise_for_status() + reply = (res.json().get("reply") or "").strip() + except Exception: + reply = "" + # Persist trimmed history so the next text continues the thread. + if reply: + _history[digits] = (messages + [{"role": "assistant", "content": reply}])[-_HISTORY_MAX:] + return reply + + def _local_score(body: str, latency_ms: int) -> dict: from analyzers.sentiment import analyze_sentiment from analyzers.linguistic import analyze_linguistic diff --git a/video/facial_hf.py b/video/facial_hf.py new file mode 100644 index 0000000..0d1a5dd --- /dev/null +++ b/video/facial_hf.py @@ -0,0 +1,94 @@ +"""HF facial-affect analysis (Rishith) — compares your face to a Hugging Face +model that knows what a healthy, regulated expression looks like. + +A ViT face-expression classifier (default ``trpakov/vit-face-expression``, FER +labels: angry, disgust, fear, happy, neutral, sad, surprise) runs on sampled +frames via ``InferenceClient.image_classification``. We aggregate the emotion +distribution across frames and score burnout/stress as how far the affect +DEVIATES from a healthy (neutral/positive) profile — i.e. distress affect mass +vs. healthy affect mass. + +No Anthropic. Degrades gracefully: returns ``None`` if HF is unavailable so the +caller can fall back to the geometric (MediaPipe) analyzer. +""" +from __future__ import annotations + +import os +from collections import defaultdict +from typing import Dict, List, Optional + +HF_TOKEN = os.getenv("HF_TOKEN") or os.getenv("HF_API_KEY") +FACE_MODEL = os.getenv("HF_FACE_MODEL", "trpakov/vit-face-expression") +MAX_FRAMES = int(os.getenv("HF_FACE_MAX_FRAMES", "6")) + +# A healthy, regulated face reads mostly neutral / positive. Distress & +# burnout show elevated negative affect — that's what the stress score weights. +_HEALTHY = {"happy": 1.0, "neutral": 0.85, "surprise": 0.55} +_STRESS = {"sad": 1.0, "fear": 1.0, "angry": 0.9, "disgust": 0.8} + + +class HFFacialModel: + def __init__(self) -> None: + self._client = None + + def _hf(self): + if self._client is None: + from huggingface_hub import InferenceClient + + self._client = InferenceClient(token=HF_TOKEN) + return self._client + + def _classify(self, jpg_bytes: bytes) -> Optional[Dict[str, float]]: + try: + res = self._hf().image_classification(jpg_bytes, model=FACE_MODEL) + return {r.label.lower(): float(r.score) for r in res} + except Exception: + return None + + def analyze_frames(self, frames: List) -> Optional[Dict]: + """frames: list of BGR numpy arrays (as decoded by OpenCV). + Returns the affect analysis, or None if HF/frames are unavailable.""" + if not HF_TOKEN or not frames: + return None + import cv2 # local — keeps module import light + + step = max(1, len(frames) // MAX_FRAMES) + sampled = frames[::step][:MAX_FRAMES] + + agg: Dict[str, float] = defaultdict(float) + n = 0 + for f in sampled: + ok, buf = cv2.imencode(".jpg", f) + if not ok: + continue + dist = self._classify(buf.tobytes()) + if not dist: + continue + for label, score in dist.items(): + agg[label] += score + n += 1 + if n == 0: + return None + + emotions = {k: round(v / n, 3) for k, v in agg.items()} + stress_mass = sum(emotions.get(k, 0.0) * w for k, w in _STRESS.items()) + healthy_mass = sum(emotions.get(k, 0.0) * w for k, w in _HEALTHY.items()) + total = stress_mass + healthy_mass + 1e-6 + + stress_score = int(round(min(stress_mass / total, 1.0) * 100)) + dominant = max(emotions, key=emotions.get) if emotions else "neutral" + affect = ( + "positive" if dominant in ("happy", "surprise") + else "negative" if dominant in ("sad", "fear", "angry", "disgust") + else "neutral" + ) + return { + "facial_stress_score": stress_score, # 0 healthy .. 100 distress + "health_alignment": round(healthy_mass / total, 3), # 1.0 = fully healthy affect + "emotion_distribution": emotions, + "dominant_affect": dominant, + "overall_affect": affect, + "frames_analyzed": n, + "model": FACE_MODEL, + "source": "huggingface", + } diff --git a/video/main.py b/video/main.py index 6cc2be0..5a138b4 100644 --- a/video/main.py +++ b/video/main.py @@ -13,17 +13,37 @@ """ from __future__ import annotations +import base64 +import json import os import tempfile +import threading +import urllib.request +from typing import Optional -from fastapi import FastAPI, File, UploadFile +try: # load NVIDIA keys + ML_URL from video/.env before the analyzers import + from dotenv import load_dotenv + + load_dotenv() +except Exception: + pass + +from fastapi import FastAPI, File, Form, UploadFile from fastapi.middleware.cors import CORSMiddleware +from pydantic import BaseModel + +# The ML service hosts the companion brain (/chat). On a phone both run on the +# laptop; override with ML_URL if they live elsewhere. +ML_URL = os.getenv("ML_URL", "http://127.0.0.1:8003").rstrip("/") +SIGNALS_URL = os.getenv("SIGNALS_URL", "http://127.0.0.1:8002").rstrip("/") +TTS_SAMPLE_RATE = int(os.getenv("NVIDIA_TTS_SAMPLE_RATE", "44100")) app = FastAPI(title="Pegasus Video") app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"]) _facial = None _voice = None +_hf_facial = None def _get_facial(): @@ -44,9 +64,107 @@ def _get_voice(): return _voice +def _get_hf_facial(): + global _hf_facial + if _hf_facial is None: + from facial_hf import HFFacialModel + + _hf_facial = HFFacialModel() + return _hf_facial + + +def _to_wav16k(src_path: str) -> str: + """Transcode any uploaded clip to 16k mono WAV for robust ASR. Returns the + wav path (falls back to the source if ffmpeg isn't available).""" + wav = src_path + ".16k.wav" + if os.system(f"ffmpeg -i {src_path} -vn -ar 16000 -ac 1 {wav} -y -loglevel quiet") == 0 and os.path.exists(wav): + return wav + return src_path + + +def _chat_reply(user_id: str, messages: list, score: Optional[int], level: Optional[str]) -> str: + """Ask the ML companion for the next turn. Warm fallback if it's unreachable.""" + body = json.dumps( + {"user_id": user_id, "messages": messages, "score": score, "level": level} + ).encode() + req = urllib.request.Request( + f"{ML_URL}/chat", data=body, headers={"Content-Type": "application/json"}, method="POST" + ) + try: + with urllib.request.urlopen(req, timeout=90) as r: + return (json.loads(r.read().decode()).get("reply") or "").strip() or _safe_reply() + except Exception: + return _safe_reply() + + +def _safe_reply() -> str: + return "I'm right here with you. Take your time — what's been weighing on you?" + + +def _update_wellness(user_id: str, transcript: str) -> None: + """Fire-and-forget: derive a wellness reading from what the user just said so + the app's live score actually moves with the conversation.""" + if not transcript: + return + try: + body = json.dumps({ + "user_id": user_id, "stimulus_id": "conversation", + "signals": {"response_text": transcript}, + }).encode() + req = urllib.request.Request( + f"{ML_URL}/score", data=body, + headers={"Content-Type": "application/json"}, method="POST", + ) + urllib.request.urlopen(req, timeout=60).read() + except Exception: + pass + + +def _try_reminder(user_id: str, text: str) -> Optional[str]: + """If the user asked for a reminder, schedule it via signals and return a + confirmation line; else None (fall through to normal conversation).""" + if not text: + return None + try: + body = json.dumps({"user_id": user_id, "text": text}).encode() + req = urllib.request.Request( + f"{SIGNALS_URL}/schedule-reminder", data=body, + headers={"Content-Type": "application/json"}, method="POST", + ) + with urllib.request.urlopen(req, timeout=10) as r: + d = json.loads(r.read().decode()) + if d.get("scheduled"): + return f"Done — I'll text you in {d.get('human', 'a bit')} to {d.get('task', 'take a break')}." + except Exception: + pass + return None + + +def _speak_b64(text: str) -> Optional[str]: + """Synthesize `text` with Chatterbox TTS and return base64 WAV (or None).""" + if not text: + return None + import tts as tts_mod + + out = tempfile.mktemp(suffix=".wav") + try: + path = tts_mod.speak(text, out) + if not path or not os.path.exists(path): + return None + with open(path, "rb") as fh: + return base64.b64encode(fh.read()).decode("ascii") + except Exception: + return None + finally: + try: + os.unlink(out) + except OSError: + pass + + @app.get("/health") def health(): - return {"status": "video running"} + return {"status": "video running", "ml_url": ML_URL} @app.post("/analyze/video") @@ -70,11 +188,34 @@ async def analyze_video(video: UploadFile = File(...)): i += 1 cap.release() + # Facial structure (MediaPipe 468-landmark geometry) — brow/eye/jaw tension. try: facial_result = _get_facial().analyze_video(frames) if frames else {"error": "no frames", "facial_stress_score": 0} except Exception as e: facial_result = {"error": f"facial failed: {e}", "facial_stress_score": 0} + # HF affect: compare the face to a model that knows healthy expression. + # This drives the headline stress score (deviation from healthy affect); + # the MediaPipe geometry above stays as the structural detail. + try: + hf = _get_hf_facial().analyze_frames(frames) if frames else None + except Exception: + hf = None + if hf: + facial_result.pop("error", None) + facial_result["emotion_distribution"] = hf["emotion_distribution"] + facial_result["dominant_affect"] = hf["dominant_affect"] + facial_result["health_alignment"] = hf["health_alignment"] + facial_result["facial_model"] = hf["model"] + facial_result.setdefault("facial_indicators", {})["overall_affect"] = hf["overall_affect"] + geo = facial_result.get("facial_stress_score") or 0 + # HF affect leads (0.65); geometric tension supports (0.35). + facial_result["facial_stress_score"] = ( + int(round(hf["facial_stress_score"] * 0.65 + geo * 0.35)) if geo + else hf["facial_stress_score"] + ) + facial_result["source"] = "hf+mediapipe" if geo else "huggingface" + try: os.system(f"ffmpeg -i {path} -vn -ar 16000 {audio} -y -loglevel quiet") voice_result = _get_voice().analyze_audio(audio) @@ -96,6 +237,88 @@ async def analyze_video(video: UploadFile = File(...)): pass +class TtsReq(BaseModel): + text: str + + +@app.post("/tts") +def tts(req: TtsReq): + """Speak arbitrary text (used for Pegasus's opening line in the video call).""" + return {"audio_b64": _speak_b64(req.text), "sample_rate": TTS_SAMPLE_RATE} + + +@app.post("/converse") +async def converse( + audio: UploadFile = File(...), + history: str = Form("[]"), + user_id: str = Form("demo_user"), + score: Optional[int] = Form(None), + level: Optional[str] = Form(None), +): + """One spoken turn of the video-call conversation: + user audio -> parakeet STT (+ voice-stress) -> ML companion reply -> + Chatterbox TTS. Returns the transcript, the reply text, the reply audio + (base64 WAV), and the per-turn voice-stress read for mental-health tracking. + Degrades gracefully: if STT/TTS are down you still get a text reply.""" + with tempfile.NamedTemporaryFile(suffix=".upload", delete=False) as tmp: + tmp.write(await audio.read()) + src = tmp.name + wav = _to_wav16k(src) + try: + try: + voice_result = _get_voice().analyze_audio(wav) + except Exception as e: + voice_result = {"transcript": "", "error": f"voice failed: {e}"} + + transcript = (voice_result.get("transcript") or "").strip() + if transcript: + threading.Thread(target=_update_wellness, args=(user_id, transcript), daemon=True).start() + + try: + msgs = json.loads(history) if history else [] + if not isinstance(msgs, list): + msgs = [] + except Exception: + msgs = [] + if transcript: + msgs = msgs + [{"role": "user", "content": transcript}] + + # "Remind me / text me in N minutes…" → schedule it and confirm. + reply = _try_reminder(user_id, transcript) or _chat_reply(user_id, msgs, score, level) + return { + "transcript": transcript, + "reply": reply, + "voice": voice_result, + "audio_b64": _speak_b64(reply), + "sample_rate": TTS_SAMPLE_RATE, + } + finally: + for p in (src, wav): + try: + if p != src or os.path.exists(p): + os.unlink(p) + except OSError: + pass + + +@app.post("/facial-frame") +async def facial_frame(image: UploadFile = File(...)): + """Single-frame facial affect via the HF model — fast, for the live video + call's periodic read (no 60s record + heavy analyze).""" + import cv2 + import numpy as np + + raw = await image.read() + frame = cv2.imdecode(np.frombuffer(raw, dtype=np.uint8), cv2.IMREAD_COLOR) + if frame is None: + return {"facial_stress_score": 0, "overall_affect": "neutral", "emotion_distribution": {}} + try: + hf = _get_hf_facial().analyze_frames([frame]) + except Exception: + hf = None + return hf or {"facial_stress_score": 0, "overall_affect": "neutral", "emotion_distribution": {}} + + if __name__ == "__main__": import uvicorn diff --git a/video/tts.py b/video/tts.py index 13efbc8..0ac5ee0 100644 --- a/video/tts.py +++ b/video/tts.py @@ -1,15 +1,32 @@ -# NVIDIA Chatterbox Text-to-Speech (Rishith) — speak the intervention aloud. +# NVIDIA Text-to-Speech (Rishith) — speak Pegasus's replies aloud. # Hosted NVIDIA Riva over gRPC (grpc.nvcf.nvidia.com:443) via nvidia-riva-client. -# Default function-id = Resemble.AI Chatterbox multilingual TTS. +# Default function-id = Magpie TTS Multilingual (the Chatterbox function isn't +# available to this account; Magpie is and sounds natural). import os import wave +import numpy as np + NVIDIA_TTS_KEY = os.getenv("NVIDIA_TTS_KEY") RIVA_SERVER = os.getenv("NVIDIA_RIVA_SERVER", "grpc.nvcf.nvidia.com:443") -# Chatterbox multilingual TTS (build.nvidia.com/resembleai/chatterbox-multilingual-tts). -TTS_FUNCTION_ID = os.getenv("NVIDIA_TTS_FUNCTION_ID", "ddacc747-1269-4fab-bfd9-8f593dead106") +# ai-magpie-tts-multilingual on build.nvidia.com. +TTS_FUNCTION_ID = os.getenv("NVIDIA_TTS_FUNCTION_ID", "877104f7-e885-42b9-8de8-f6e4c6303969") TTS_VOICE = os.getenv("NVIDIA_TTS_VOICE", "") # empty -> model default SAMPLE_RATE = int(os.getenv("NVIDIA_TTS_SAMPLE_RATE", "44100")) +# Peak-normalize the voice so it's clearly audible on a phone speaker (loud). +TTS_GAIN_CAP = float(os.getenv("NVIDIA_TTS_GAIN_CAP", "12")) +TTS_TARGET_PEAK = float(os.getenv("NVIDIA_TTS_TARGET_PEAK", "31000")) + + +def _boost(audio: bytes) -> bytes: + """Peak-normalize raw PCM16 toward full scale so the reply plays loud.""" + try: + s = np.frombuffer(audio, dtype=np.int16).astype(np.float32) + peak = float(np.max(np.abs(s))) or 1.0 + gain = min(TTS_TARGET_PEAK / peak, TTS_GAIN_CAP) + return np.clip(s * gain, -32768, 32767).astype(np.int16).tobytes() + except Exception: + return audio def speak(text: str, out_path: str = "intervention.wav"): @@ -39,7 +56,7 @@ def speak(text: str, out_path: str = "intervention.wav"): w.setnchannels(1) w.setsampwidth(2) w.setframerate(SAMPLE_RATE) - w.writeframes(resp.audio) + w.writeframes(_boost(resp.audio)) return out_path except Exception: return None diff --git a/video/voice_analyzer.py b/video/voice_analyzer.py index 14ee21d..a518e88 100644 --- a/video/voice_analyzer.py +++ b/video/voice_analyzer.py @@ -3,13 +3,13 @@ # transcript / zeros) so a check-in never hard-fails. import os -import librosa import numpy as np NVIDIA_STT_KEY = os.getenv("NVIDIA_STT_KEY") RIVA_SERVER = os.getenv("NVIDIA_RIVA_SERVER", "grpc.nvcf.nvidia.com:443") -# Parakeet CTC 1.1B ASR (en-US) hosted function. -ASR_FUNCTION_ID = os.getenv("NVIDIA_ASR_FUNCTION_ID", "1598d209-5e27-4d3c-8079-4751568b1081") +# parakeet-tdt-0.6b-v2 — supports OFFLINE recognize (the 1.1b CTC function is +# streaming-only and rejects offline_recognize). +ASR_FUNCTION_ID = os.getenv("NVIDIA_ASR_FUNCTION_ID", "d3fe9151-442b-4204-a70d-5fcc597fd610") class VoiceStressAnalyzer: @@ -28,12 +28,16 @@ def transcribe_nvidia(self, audio_path: str) -> str: ], ) asr = riva.client.ASRService(auth) + # The /converse + /analyze paths feed a 16 kHz mono WAV. Set the + # config explicitly (add_audio_file_specs picked params the model + # wouldn't serve). config = riva.client.RecognitionConfig( + encoding=riva.client.AudioEncoding.LINEAR_PCM, + sample_rate_hertz=16000, language_code="en-US", max_alternatives=1, enable_automatic_punctuation=True, ) - riva.client.add_audio_file_specs_to_config(config, audio_path) with open(audio_path, "rb") as f: data = f.read() resp = asr.offline_recognize(data, config) @@ -46,6 +50,8 @@ def transcribe_nvidia(self, audio_path: str) -> str: def analyze_audio(self, audio_path: str) -> dict: transcript = self.transcribe_nvidia(audio_path) try: + import librosa # lazy — voice stress is optional; transcript already done + y, sr = librosa.load(audio_path) pitches, mags = librosa.piptrack(y=y, sr=sr) vals = [ From 8a5ecb8eb4063187e7d73c98d5b5473a17077f47 Mon Sep 17 00:00:00 2001 From: jasonca2023 Date: Sun, 14 Jun 2026 13:59:35 -0700 Subject: [PATCH 10/10] docs: rewrite README with full feature documentation and MIT license --- README.md | 168 +++++++++++++++++++++++++++++++++++++++++------------- 1 file changed, 127 insertions(+), 41 deletions(-) diff --git a/README.md b/README.md index d4540bc..ccec9fb 100644 --- a/README.md +++ b/README.md @@ -1,55 +1,141 @@ -# pegasus +# Pegasus -A mental-health **check engine light**. A daily stimulus goes to the user → -they respond (text + typing/timing) → **TRIBE v2** predicts how a *healthy* -brain responds to that stimulus → we score the deviation from the user's actual -behavioral signal → 🟢/🟡/🔴 + a Claude intervention → SMS alert if red. +Pegasus is a multimodal burnout detection and mental wellness platform. It works like a check engine light for the mind — passively collecting behavioral signals throughout the day, fusing them into a single wellness score, and surfacing a grounded, personalized intervention when that score drops. -``` -frontend (Wesley) React :3000 ── talks only to backend -backend (Jason) FastAPI+SQLite :8001 ── orchestrator, fans out ↓ -signals (Dhruva) FastAPI :8002 ── sentiment + typing + Twilio -ml (Rishith) FastAPI+TRIBE :8003 ── predict · combined 4-stream score -video (Rishith) MediaPipe+Whisper :8004 ── facial + voice stress (check-in) -imessage (TBD) AppleScript + chat.db ── optional iMessage delivery (macOS) -``` +## Architecture -## Quickstart +Pegasus is composed of four services that communicate over HTTP: -```bash -./dev.sh # sets up venvs + node_modules on first run, then starts all 4 -# → http://localhost:3000 -``` +| Service | Port | Stack | Owner | +|---|---|---|---| +| Backend orchestrator | 8001 | FastAPI, SQLite | Jason | +| Signals processor | 8002 | FastAPI, HuggingFace | Dhruva | +| ML / TRIBE scorer | 8003 | FastAPI, Modal, HuggingFace | Rishith | +| Video analyzer | 8004 | FastAPI | Rishith | + +The Expo React Native mobile app connects to the backend at port 8001. The SMS bot runs alongside the signals service and polls iMessage via a local AppleScript bridge. + +## Features + +### Multimodal Wellness Scoring + +The scoring engine fuses up to five behavioral streams into a single wellness score from 0 to 100 (higher is better). Each stream contributes a burnout sub-score that is weighted and normalized depending on which signals are actually present in a given check-in. + +| Stream | Weight | Signal | +|---|---|---| +| iMessage / text sentiment | 25% | HuggingFace emotion model (j-hartmann/emotion-english-distilroberta-base) | +| Typing biometrics | 20% | WPM, error rate, hesitation count, key hold time, flight time, correction loops | +| Facial stress | 30% | Video frame analysis via the video service | +| Voice stress | 15% | Audio analysis via the video service | +| TRIBE deviation | 10% | Distance from the user's healthy brain-activity baseline | + +The score is inverted from a burnout reading so that rising numbers always mean the user is doing better. Scores above 70 are green, 40-69 are yellow, and below 40 are red. + +### TRIBE Baseline Model + +TRIBE (Temporal Representation of Integrated Brain Events) v2 is a deployed Modal endpoint that models healthy brain activation patterns across six macro-regions: Attention, Auditory, Emotion, Language, Motor, and Visual. For each daily stimulus, Pegasus calls TRIBE once to establish the user's expected healthy-state baseline, then measures deviation from that baseline as a behavioral burnout signal. TRIBE results are cached per stimulus to avoid repeated GPU calls. + +### Crisis and Masking Detection + +The scoring engine applies a keyword override layer on top of the emotion model to catch signals the model tends to under-read: + +- Crisis terms (suicidal ideation, self-harm language) force the iMessage sub-score to 100 regardless of the emotion model output. +- Concern terms (hopeless, burned out, can't cope, etc.) floor the sub-score proportionally to the number of terms present. +- Masking language (clustered over-reassurance such as "I'm fine, totally fine, don't worry") is detected as a tell and pulls the score out of the green even when the emotion model reads positive. + +When the overall score reaches red, the backend routes the user to human support rather than an automated intervention. + +### RAG Intervention Engine + +When the wellness score drops, Pegasus generates a single, warm, specific recommended action using a retrieval-augmented generation pipeline: + +1. A mental health coping corpus is embedded using sentence-transformers/all-MiniLM-L6-v2. +2. The most relevant snippet is retrieved using cosine similarity. +3. A HuggingFace chat model (Qwen/Qwen2.5-7B-Instruct by default) generates a grounded response using only the retrieved content. + +The pipeline degrades gracefully at every step: if HuggingFace is unreachable, the raw retrieved snippet is returned; if the corpus is unavailable, a level-appropriate canned response is used. The intervention call never raises. + +### Signal Collection -Optional iMessage channel (macOS only): see [`imessage/README.md`](imessage/README.md). +The signals service collects and analyzes behavioral data from multiple sources: -API contract: [`shared/contract.md`](shared/contract.md). Agent rules: -[`AGENTS.md`](AGENTS.md) (root) + one per folder. Dispatchable subagents live in -`.claude/agents/`. +- **iMessage polling** — an AppleScript bridge reads the local Messages database and extracts response text, response time, and typing metadata. +- **Keystroke biometrics** — a JavaScript collector (collector.js) captures WPM, error rate, hesitation count, key hold duration, inter-key flight time, burst pattern, and correction loops from in-app typing sessions. +- **Sentiment analysis** — HuggingFace Inference API replaces any Claude-based sentiment pipeline; the model degrades to a neutral 0.5 score if the API is unreachable. +- **Linguistic analysis** — structural and lexical features of response text. +- **Temporal analysis** — time-of-day and response latency patterns. -## Branching model +The signals service maintains a rolling in-memory history of the last 30 readings per user and exposes them to the backend via a typed JSON contract. + +### SMS Bot + +The SMS bot provides a low-friction check-in channel for users who are not on the mobile app: + +- Outbound check-in prompts are sent via Bloo.io SMS. +- Inbound replies are received via a webhook and routed through the full signal analysis and scoring pipeline. +- Automated reminders run on a configurable cadence. +- Stimulus prompts are sent on the same schedule as the in-app experience. + +### Video Check-in + +The mobile app includes a video check-in flow: + +1. The user records a short clip via the in-app camera (60-second maximum, with a countdown ring). +2. The clip is submitted to the video service, which extracts facial stress and voice stress scores. +3. Results are fused into the wellness score and displayed alongside the brain visualization immediately after analysis. + +### Mobile App + +The Expo React Native app is the primary user-facing interface: + +- **Home screen** — an animated breathing orb displays the current wellness score in the user's level color (green, yellow, red), along with the top behavioral indicators and the current intervention suggestion. A red reading triggers a haptic alert and a full-screen support prompt. +- **Check-in screen** — live camera with face-oval overlay, rotating wellness prompts, recording controls, and a post-analysis results view. +- **Brain screen** — a 3D brain visualization that highlights the TRIBE regions currently flagged as elevated. +- **Metrics screen** — historical wellness score charts and breakdown by signal stream. +- **History screen** — a chronological log of past check-ins and score readings. +- **Chat / Talk screen** — a conversational interface for in-app text check-ins. + +The UI uses a liquid glass visual language with level-reactive accent colors throughout. + +### Backend API + +The FastAPI backend at port 8001 is the single point of integration for the mobile app and SMS bot. It persists all readings to SQLite with WAL mode for concurrency, handles first-time-user race conditions, and exposes the following route groups: + +| Route | Purpose | +|---|---| +| `/stimulus` | Deliver the daily check-in prompt | +| `/response` | Accept a text or keystroke-biometric check-in response | +| `/score` | Return the latest wellness score for a user | +| `/brain` | Return TRIBE brain region data for the 3D visualization | +| `/history` | Return the user's check-in history | +| `/metrics` | Return aggregated score and breakdown data for charts | +| `/video` | Accept a video submission and return facial/voice analysis | + +### Infrastructure + +A launchd supervisor script keeps all four services, the iMessage poller, and the ngrok tunnel alive, restarting any process that exits. The sync script handles safe periodic state synchronization without reverting or deleting data. + +## Branch Structure ``` -main (protected — NEVER push directly) - └── dev (integration branch — PRs only) - ├── feat/rishith-ml → /ml/* - ├── feat/wesley-ui → /frontend/* - ├── feat/jason-api → /backend/*, /shared/* - └── feat/dhruva-sig → /signals/* +main + └── dev + ├── feat/jason-api backend/, shared/ + ├── feat/wesley-ui frontend/ + ├── feat/rishith-ml ml/ + └── feat/dhruva-sig signals/ ``` -## Ownership +`feat/integration` and `feat/scaffold` are integration and scaffolding branches used during active development. + +## License + +MIT License + +Copyright (c) 2024 Pegasus Contributors -| Branch | Owner | Scope | -| ----------------- | ------- | ---------------------- | -| `feat/rishith-ml` | Rishith | `ml/` | -| `feat/wesley-ui` | Wesley | `frontend/` | -| `feat/jason-api` | Jason | `backend/`, `shared/` | -| `feat/dhruva-sig` | Dhruva | `signals/` | +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: -## Rules +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. -- `main` is protected. No direct pushes. Releases only via PR from `dev`. -- `dev` is the integration branch. Feature branches PR into `dev`. -- Stay in your lane: only touch the directories listed for your branch. -- Rebase or merge `dev` into your feature branch regularly to stay current. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.