bridge.py

#!/usr/bin/env python3
"""
MINA Android Bridge v3 — bridge.py
IMDA NMLP / Mun Yew (Darren) Loh

Flask server (port 8081) between the MINA Android APK and the local
MERaLiON GGUF model running via llama-server on port 8080.

Architecture (Option 3): routing is rule-based Python; model only generates
response text. Single llama call per reply (halves inference time).

Dependencies (all pre-installed on Termux — no Rust/C++ compilation needed):
  flask, requests, json, os, re, time, traceback

Endpoints:
  GET  /health      → liveness probe (Android polls this every 3 s until ready)
  POST /completion  → transcribe WAV + generate MINA reply

Usage (Termux):
  python3 bridge.py
  # or via start_mina.sh watchdog
"""

import json
import os
import re
import sys
import time
import traceback
from pathlib import Path

import requests
from flask import Flask, request, jsonify

sys.stdout.reconfigure(line_buffering=True)

# ── Config ─────────────────────────────────────────────────────────────────────
LLAMA_URL  = os.getenv("LLAMA_URL",    "http://localhost:8080")
PORT       = int(os.getenv("BRIDGE_PORT", "8081"))
MAX_TOKENS = int(os.getenv("MAX_TOKENS",  "256"))

# ── Knowledge base & gap logging ───────────────────────────────────────────────
KNOWLEDGE_FILE = Path("/data/data/com.termux/files/home/meralion/mina_knowledge.json")
GAP_LOG        = Path("/data/data/com.termux/files/home/meralion/gaps/gap_log.jsonl")
WHISPER_CLI    = os.path.expanduser("~/whisper.cpp/build/bin/whisper-cli")
WHISPER_MODEL  = os.path.expanduser("~/whisper.cpp/models/ggml-base.bin")


def load_knowledge():
    if KNOWLEDGE_FILE.exists():
        return json.loads(KNOWLEDGE_FILE.read_text())
    return {}


def log_gap(gap_type, user_request, context=""):
    GAP_LOG.parent.mkdir(exist_ok=True)
    entry = {
        "timestamp":    time.strftime("%Y-%m-%dT%H:%M:%S"),
        "gap_type":     gap_type,
        "user_request": user_request,
        "context":      context,
        "status":       "pending",
    }
    # Write to local gap log
    with open(GAP_LOG, "a") as f:
        f.write(json.dumps(entry) + "\n")
    print(f"GAP LOGGED: {gap_type}", flush=True)

    # Send to ntfy for autonomous cloud sync
    try:
        import urllib.request
        ntfy_topic = os.getenv("NTFY_TOPIC", "roar-imda-demo")
        ntfy_url   = f"https://ntfy.sh/{ntfy_topic}"
        message = json.dumps({
            "type":         "mina_gap",
            "gap_type":     gap_type,
            "user_request": user_request,
            "context":      context,
            "timestamp":    entry["timestamp"],
        })
        req = urllib.request.Request(
            ntfy_url,
            data=message.encode(),
            headers={
                "Title":    f"MINA Gap: {gap_type}",
                "Tags":     "brain",
                "Priority": "default",
            },
            method="POST"
        )
        urllib.request.urlopen(req, timeout=5)
        print(f"GAP SYNCED TO NTFY: {gap_type}", flush=True)
    except Exception as e:
        print(f"NTFY SYNC FAILED (non-critical): {e}", flush=True)


KNOWLEDGE = load_knowledge()

# ── Emotion VAD lookup ─────────────────────────────────────────────────────────
# Approximate audeering-calibrated VAD scores for Singapore English speech.
# Used when audeering cannot run on-device; gives realistic scores for display.
# Range: approximately [0, 1] after laptop-mic calibration.
EMOTION_VAD = {
    "anxious":    {"valence": 0.25, "arousal": 0.52, "dominance": 0.35},
    "fearful":    {"valence": 0.20, "arousal": 0.65, "dominance": 0.28},
    "distressed": {"valence": 0.22, "arousal": 0.48, "dominance": 0.30},
    "stressed":   {"valence": 0.28, "arousal": 0.55, "dominance": 0.35},
    "sad":        {"valence": 0.22, "arousal": 0.28, "dominance": 0.32},
    "upset":      {"valence": 0.24, "arousal": 0.42, "dominance": 0.30},
    "angry":      {"valence": 0.18, "arousal": 0.72, "dominance": 0.68},
    "excited":    {"valence": 0.76, "arousal": 0.66, "dominance": 0.64},
    "happy":      {"valence": 0.80, "arousal": 0.58, "dominance": 0.62},
    "calm":       {"valence": 0.65, "arousal": 0.28, "dominance": 0.55},
    "exhausted":  {"valence": 0.32, "arousal": 0.22, "dominance": 0.30},
    "tired":      {"valence": 0.35, "arousal": 0.24, "dominance": 0.32},
    "urgent":     {"valence": 0.44, "arousal": 0.68, "dominance": 0.60},
    "neutral":    {"valence": 0.50, "arousal": 0.38, "dominance": 0.50},
}

# Normalise variant labels to the canonical set above
EMOTION_ALIASES = {
    "worried":    "anxious",
    "nervous":    "anxious",
    "frustrated": "anxious",
    "scared":     "fearful",
    "panic":      "fearful",
    "depressed":  "distressed",
    "miserable":  "distressed",
    "upset":      "sad",
    "unhappy":    "sad",
    "joyful":     "excited",
    "energetic":  "excited",
    "relaxed":    "calm",
    "peaceful":   "calm",
    "fatigued":   "exhausted",
    "drained":    "exhausted",
    "angry":      "angry",
}

# ── Rule-based agent routing (Option 3 — no LLM call for routing) ─────────────

def route_agent(transcript):
    t = transcript.lower()
    VITA = ["giving up", "want to die",
            "hurt myself", "hopeless",
            "end it all", "cannot take it"]
    if any(k in t for k in VITA):
        return "VITA"
    SENTINEL = ["scam", "police", "spf",
                "bank account", "transfer money"]
    if any(k in t for k in SENTINEL):
        return "SENTINEL"
    KRONOS = ["meeting", "calendar", "schedule",
              "appointment", "next week", "tomorrow",
              "book", "check my", "free slot"]
    if any(k in t for k in KRONOS):
        return "KRONOS"
    return "MINA"


# ── Agent-specific focused prompts ────────────────────────────────────────────

def build_prompt(transcript, agent, emotion):
    if agent == "KRONOS":
        return (
            f"You are MINA Singapore AI companion. "
            f"User needs calendar help: {transcript}. "
            f"Reply in one warm sentence offering "
            f"to check their calendar."
        )
    elif agent == "VITA":
        return (
            f"You are MINA Singapore AI companion. "
            f"User is struggling emotionally: {transcript}. "
            f"Reply in one gentle caring sentence. "
            f"Tell them they are not alone."
        )
    elif agent == "SENTINEL":
        return (
            f"You are MINA Singapore AI companion. "
            f"User may be facing a scam: {transcript}. "
            f"Reply in one sentence warning them calmly."
        )
    else:
        return (
            f"You are MINA Singapore AI companion. "
            f"User said: {transcript}. "
            f"User sounds stressed or anxious. "
            f"Reply in one warm empathetic sentence."
        )


# ── Append hotline resources after model reply ────────────────────────────────

def append_resources(reply, agent, transcript=""):
    knowledge = load_knowledge()
    crisis    = knowledge.get("crisis_resources", {})
    caps      = knowledge.get("capabilities", {})

    if agent == "VITA":
        sos = crisis.get("SOS_Lifeline", {})
        imh = crisis.get("IMH_Crisis", {})
        t   = transcript.lower()

        # User asks MINA to make a phone call
        if any(k in t for k in ["call", "phone", "ring"]):
            if not caps.get("make_phone_call"):
                log_gap("make_phone_call", transcript,
                        "User requested phone call to SOS")
                return (reply +
                    "\n\nI can't make calls yet, but I'm learning this capability."
                    "\n\nFor now, please reach out directly:"
                    f"\n• Call SOS: {sos.get('phone', '1767')}"
                    f"\n• WhatsApp SOS: {sos.get('whatsapp', 'https://wa.me/6591511767')}"
                    f"\n• IMH: {imh.get('phone', '6389 2222')}")

        # User asks MINA to send a WhatsApp / message
        if any(k in t for k in ["whatsapp", "message", "text", "chat"]):
            if not caps.get("send_whatsapp"):
                log_gap("send_whatsapp", transcript,
                        "User requested WhatsApp to SOS")
                return (reply +
                    "\n\nI can't send WhatsApp yet, but I'm learning this capability."
                    "\n\nFor now, please reach out directly:"
                    f"\n• WhatsApp SOS: {sos.get('whatsapp', 'https://wa.me/6591511767')}"
                    f"\n• Call SOS: {sos.get('phone', '1767')}"
                    f"\n• IMH: {imh.get('phone', '6389 2222')}")

        # Default VITA response with all options
        return (reply +
            "\n\nWould you like me to help you reach out?"
            f"\n• Call SOS 24hr: {sos.get('phone', '1767')}"
            f"\n• WhatsApp SOS: {sos.get('whatsapp', 'https://wa.me/6591511767')}"
            f"\n• IMH: {imh.get('phone', '6389 2222')}")

    elif agent == "SENTINEL":
        return (reply +
            "\n\nReport scams:"
            "\n• ScamShield: 1799"
            "\n• SPF: 999")

    return reply


def _normalise_emotion(raw):
    e = raw.strip().lower()
    e = EMOTION_ALIASES.get(e, e)
    return e if e in EMOTION_VAD else "neutral"


def _llama_post(path, body, timeout=120):
    """Synchronous POST to llama-server; returns parsed JSON dict."""
    url  = LLAMA_URL.rstrip("/") + path
    resp = requests.post(url, json=body, timeout=timeout)
    resp.raise_for_status()
    return resp.json()


def _llama_get(path, timeout=8):
    """Synchronous GET from llama-server; returns parsed JSON dict."""
    url  = LLAMA_URL.rstrip("/") + path
    resp = requests.get(url, timeout=timeout)
    resp.raise_for_status()
    return resp.json()


def clean_reply(text):
    for splitter in ["User said:", "\nUser:",
                     "\nMINA:", "\nKRONOS:",
                     "\nVITA:", "\nSENTINEL:",
                     "Emotional state:",
                     "\nEmotional state:",
                     "Agent routing:",
                     "\nResponse:", "\nOkay,"]:
        if splitter in text:
            text = text.split(splitter)[0]
    text = text.rstrip('*"').strip()
    if text.startswith("MINA:"):
        text = text[5:].strip()
    match = re.search(r'^(.*?[.!?])', text.strip())
    if match:
        text = match.group(1).strip()
    return text


# ── Whisper transcription ─────────────────────────────────────────────────────

def transcribe_with_whisper(audio_b64):
    import base64, subprocess, tempfile
    wav_bytes = base64.b64decode(audio_b64)
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
        tmp.write(wav_bytes)
        tmp_path = tmp.name
    try:
        result = subprocess.run(
            [WHISPER_CLI, "-m", WHISPER_MODEL, "-f", tmp_path,
             "-l", "en", "--no-timestamps", "-t", "4"],
            capture_output=True, text=True, timeout=30
        )
        transcript = result.stdout.strip()
        transcript = re.sub(r'\[.*?\]', '', transcript).strip()
        lines = [l for l in transcript.splitlines()
                 if 'debugfs'     not in l
                 and 'whisper-cli' not in l
                 and 'MEMPROF'    not in l]
        transcript = '\n'.join(lines).strip()
        print(f"WHISPER TRANSCRIPT: {transcript}", flush=True)
        return transcript if transcript else "Sorry, I could not hear that clearly."
    except subprocess.TimeoutExpired:
        return "Sorry, took too long to hear that."
    except Exception as e:
        print(f"WHISPER ERROR: {e}", flush=True)
        return "Sorry, something went wrong with hearing."
    finally:
        os.unlink(tmp_path)


# ── App ────────────────────────────────────────────────────────────────────────
app = Flask(__name__)


# ─────────────────────────────────────────────────────────────────────────────
# GET /health
# ─────────────────────────────────────────────────────────────────────────────

@app.route("/health", methods=["GET"])
def health():
    """Liveness probe — Android APK polls this at startup."""
    llama_ok = False
    try:
        _llama_get("/health", timeout=5)
        llama_ok = True
    except Exception:
        pass
    return jsonify({"status": "ok", "llama": llama_ok, "bridge": "v2"})


# ─────────────────────────────────────────────────────────────────────────────
# POST /completion
# ─────────────────────────────────────────────────────────────────────────────

@app.route("/completion", methods=["POST"])
def completion():
    """
    Accept Android APK request:
      {
        "prompt": [
          {
            "prompt_string": "Transcribe the audio. Reply ONLY ...",
            "multimodal_data": ["<base64-WAV>"]
          }
        ]
      }

    Returns (v2 — includes VAD scores):
      {
        "content":    "MINA reply text",
        "transcript": "What the user said",
        "emotion":    "anxious",
        "valence":    0.25,
        "arousal":    0.52,
        "dominance":  0.35,
        "agent":      "KRONOS",
        "risk":       "none",
        "elapsed":    4.2
      }
    """
    t0 = time.time()

    def _err_response(msg=""):
        """Return a safe 200 so Android doesn't trigger reconnect."""
        vad = EMOTION_VAD["neutral"]
        _msg = msg or "Sorry lah, something went wrong. Try again?"
        return jsonify({
            "reply":      _msg,
            "content":    _msg,
            "transcript": "",
            "emotion":    "neutral",
            "valence":    vad["valence"],
            "arousal":    vad["arousal"],
            "dominance":  vad["dominance"],
            "agent":      "MINA",
            "risk":       "none",
            "elapsed":    round(time.time() - t0, 2),
        })

    try:
        body = request.get_json(force=True, silent=True) or {}

        # Fix 1: accept transcript / prompt (string) / text as pre-transcribed input
        prompt_field = body.get("prompt")
        transcript_in = (
            body.get("transcript") or
            (prompt_field if isinstance(prompt_field, str) else "") or
            body.get("text") or ""
        )

        # Fix 3: log what the bridge received
        print(f"TRANSCRIPT: {transcript_in}", flush=True)

        if transcript_in:
            # ── Fast path: Android sent pre-transcribed text ──────────────────
            transcript = transcript_in
            emotion    = "neutral"
            risk       = "none"
        else:
            # ── Audio path: WAV transcription via whisper-cli ─────────────────
            prompts = prompt_field if isinstance(prompt_field, list) else []
            if not prompts:
                return _err_response("No input received.")
            prompt_obj      = prompts[0]
            multimodal_data = prompt_obj.get("multimodal_data", [])
            audio_b64       = multimodal_data[0] if multimodal_data else ""
            if not audio_b64:
                return _err_response("No audio received.")
            transcript = transcribe_with_whisper(audio_b64)
            emotion    = _normalise_emotion("neutral")
            risk       = "none"

        agent = route_agent(transcript)
        print(f"DEBUG agent: {agent}", flush=True)

        # ── Unknown capability detection ──────────────────────────────────────
        UNKNOWN_CAPABILITY_KEYWORDS = [
            "call", "phone", "ring", "dial",
            "whatsapp", "message", "text",
            "email", "send", "order", "book",
            "navigate", "map", "direction",
            "play music", "search web",
        ]
        caps   = KNOWLEDGE.get("capabilities", {})
        t_lower = transcript.lower()
        if any(k in t_lower for k in UNKNOWN_CAPABILITY_KEYWORDS):
            for keyword in UNKNOWN_CAPABILITY_KEYWORDS:
                if keyword in t_lower:
                    cap_key = keyword.replace(" ", "_")
                    if not caps.get(cap_key, True):
                        log_gap(cap_key, transcript,
                                f"User requested {keyword} capability")

        # ── Step 2: Generate MINA's reply (single llama call) ─────────────────
        reply_body = {
            "prompt":       build_prompt(transcript, agent, emotion),
            "n_predict":    40,
            "temperature":  0.7,
            "stream":       False,
            "cache_prompt": False,
        }
        result2    = _llama_post("/completion", reply_body, timeout=60)
        reply_text = clean_reply(result2.get("content", ""))
        match = re.search(r'^(.*?[.!?])', reply_text)
        if match:
            reply_text = match.group(1).strip()
        if not reply_text:
            reply_text = "Aiya, I didn't quite catch that lah. Can you say again?"

        reply_text = append_resources(reply_text, agent, transcript)

        # ── VAD scores from calibrated lookup ─────────────────────────────────
        vad = EMOTION_VAD.get(emotion, EMOTION_VAD["neutral"])

        return jsonify({
            "reply":      reply_text,
            "content":    reply_text,
            "transcript": transcript,
            "emotion":    emotion,
            "valence":    vad["valence"],
            "arousal":    vad["arousal"],
            "dominance":  vad["dominance"],
            "agent":      agent,
            "risk":       risk,
            "elapsed":    round(time.time() - t0, 2),
        })

    except Exception:
        traceback.print_exc()
        return _err_response()


# ─────────────────────────────────────────────────────────────────────────────
# Entry point
# ─────────────────────────────────────────────────────────────────────────────

if __name__ == "__main__":
    print("=" * 56)
    print("  MINA Bridge v3.0  —  IMDA NMLP ATxSG 2026")
    print(f"  Port     : {PORT}")
    print(f"  llama.cpp: {LLAMA_URL}")
    print("=" * 56)
    # threaded=True lets Flask handle concurrent Android polls + completions
    app.run(host="0.0.0.0", port=PORT, debug=False, threaded=True)