#!/usr/bin/env python3
"""AVC optometry phone agent — the Pipecat pipeline for a single inbound call.

Same VAD -> STT -> LLM -> TTS loop as pipecat-run/bot.py, but the ends are swapped
for telephony: audio arrives/leaves as 8 kHz mu-law over a Twilio Media Stream
(WebSocket), decoded by TwilioFrameSerializer. STT runs on the GPU; the LLM is the
local `activeblue-avc` fine-tune via Ollama; TTS is local Kokoro.

This module just builds + runs the pipeline for one connected call. server.py owns
the FastAPI/TwiML/WebSocket side and calls run_call() once per call.
"""

import os

import re
import time

from loguru import logger

from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.frames.frames import (
    BotStoppedSpeakingFrame,
    EndFrame,
    EndTaskFrame,
    Frame,
    InputAudioRawFrame,
    LLMFullResponseEndFrame,
    LLMTextFrame,
    TTSSpeakFrame,
)
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
from pipecat.processors.audio.vad_processor import VADProcessor
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.serializers.twilio import TwilioFrameSerializer
from pipecat.services.anthropic.llm import AnthropicLLMService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.kokoro.tts import KokoroTTSService
from pipecat.services.ollama.llm import OLLamaLLMService
from pipecat.transports.websocket.fastapi import (
    FastAPIWebsocketParams,
    FastAPIWebsocketTransport,
)

from practice import practice_summary

# ── Config (env-overridable) ─────────────────────────────────────────────────
HERE = os.path.dirname(os.path.abspath(__file__))
# Reuse the Kokoro model files already downloaded by the pipecat-run project.
MODEL_DIR = os.environ.get("KOKORO_MODEL_DIR", "/home/tocmo0nlord/pipecat-run/models")

OLLAMA_MODEL = os.environ.get("OLLAMA_MODEL", "activeblue-avc:latest")
OLLAMA_URL = os.environ.get("OLLAMA_URL", "http://127.0.0.1:11434/v1")
# Swappable LLM provider: "ollama" (local) or "anthropic" (Claude API). Same universal
# LLMContext drives both — only the service construction differs (see build_llm_service).
LLM_PROVIDER = os.environ.get("LLM_PROVIDER", "ollama").lower()
ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY", "")
# Defaults to the most capable model. For low-latency PHONE voice, set ANTHROPIC_MODEL to
# claude-haiku-4-5 (fastest) or claude-sonnet-4-6 (balance) — see notes in build_llm_service.
ANTHROPIC_MODEL = os.environ.get("ANTHROPIC_MODEL", "claude-opus-4-8")
# In-call function-calling: AUTO by provider — ON for Claude (reliable tool calls → real-time
# Odoo booking), OFF for local Ollama (llama3.1:8b over-calls / leaks JSON). An explicit
# ENABLE_TOOLS env overrides the auto choice either way.
_enable_tools_env = os.environ.get("ENABLE_TOOLS")
ENABLE_TOOLS = (
    _enable_tools_env.lower() in ("1", "true", "yes")
    if _enable_tools_env is not None
    else (LLM_PROVIDER == "anthropic")
)
LLM_TEMPERATURE = float(os.environ.get("LLM_TEMPERATURE", "0.3"))
LLM_MAX_TOKENS = int(os.environ.get("LLM_MAX_TOKENS", "160"))
KOKORO_VOICE = os.environ.get("KOKORO_VOICE", "af_heart")
# Real-time STT is Deepgram Nova-2: end-of-utterance events in <300ms (vs Whisper's
# 1-3s of chunk buffering, the main cause of non-reply / repeat-yourself). Whisper
# large-v3 is retained for post-call transcription only (Phase 3).
DEEPGRAM_API_KEY = os.environ.get("DEEPGRAM_API_KEY", "")

# Twilio sends 8 kHz mu-law on the wire — we run the PIPELINE at 16 kHz and let
# TwilioFrameSerializer resample to/from the 8 kHz wire. (Silero VAD, Deepgram, and
# Kokoro are all happy at 16 kHz.)
WIRE_SAMPLE_RATE = 8000          # Twilio mu-law on the wire (serializer handles this)
PIPELINE_SAMPLE_RATE = 16000     # internal rate Whisper/VAD actually need

# VAD tuning. Defaults (confidence 0.7 / min_volume 0.6) are desktop-mic values that can
# miss short/quiet 8 kHz telephony utterances like "yes" — loosen them for the phone.
VAD_CONFIDENCE = float(os.environ.get("VAD_CONFIDENCE", "0.5"))
VAD_MIN_VOLUME = float(os.environ.get("VAD_MIN_VOLUME", "0.3"))
VAD_START_SECS = float(os.environ.get("VAD_START_SECS", "0.2"))
VAD_STOP_SECS = float(os.environ.get("VAD_STOP_SECS", "0.5"))

# Agent persona name — purely for warmth; change/remove freely.
AGENT_NAME = os.environ.get("AGENT_NAME", "Sofia")

SYSTEM_PROMPT = (
    f"You are {AGENT_NAME}, a warm, friendly receptionist for Advanced Vision Care, an "
    "optometry practice with eight offices in South Florida. You are on a real phone call, so "
    "talk like a helpful human being: natural, relaxed, and genuinely conversational — usually "
    "just one short sentence at a time. Speak in English. Say numbers, dates, and times as "
    "words a person would say.\n\n"
    "Your job is to answer callers' questions and to take appointment requests. To book a "
    "visit you need four things: which office or city, the reason for the visit, a preferred "
    "day and time, and their name. Gather these naturally as the conversation flows — don't "
    "interrogate, and never ask for something the caller already told you (people often give "
    "their name or reason in their first sentence). You already have their number from caller "
    "ID, so never ask for a phone number. When you have the details, repeat them back in one "
    "warm sentence to confirm, and let them know a staff member will call to finalize the time.\n\n"
    "Stay truthful and within your limits:\n"
    "- Use ONLY the facts below for addresses, phone numbers, insurance, and services. Never "
    "make any of these up.\n"
    "- To find the right office, ask what CITY or AREA is most convenient for the caller. Do "
    "NOT suggest or name a specific office yourself — you don't know where they are. Only after "
    "they tell you their area, name the matching office; and only list locations if they ask "
    "what offices exist.\n"
    "- You cannot see a calendar, so never say a time is open or available — take the time as "
    "a request that staff will confirm.\n"
    "- Insurance: only confirm a plan that is in the list below. For any plan that is not "
    "listed (UnitedHealthcare, Aetna, Cigna, and so on), don't say yes or no — say our staff "
    "will verify their coverage.\n"
    "- Hours are not published — say they vary by office and staff will confirm; never give "
    "specific hours.\n"
    "- You don't give medical advice and can't transfer calls. If the caller mentions an eye "
    "problem, just note it as the reason and say a staff member or doctor will follow up.\n"
    "- If you're not sure you heard something, simply ask them to repeat it.\n"
    "- When the caller is all set, give a brief, warm closing that ends with the word "
    "'Goodbye' — that ends the call, so only say it when you truly mean to.\n\n"
    "PRACTICE FACTS:\n" + practice_summary()
)


def _build_tools() -> ToolsSchema:
    # Only the booking action is a tool. Practice facts already live in the system prompt,
    # so no get_practice_info tool (avoids needless calls/latency). callback_number is NOT
    # required — we have the caller-ID and inject it in the handler.
    return ToolsSchema(
        standard_tools=[
            FunctionSchema(
                name="record_appointment_request",
                description=(
                    "Record the caller's appointment request once you have their name and at "
                    "least the office/city and reason. Call this when the caller wants to book "
                    "a visit; staff will call back to confirm the exact time."
                ),
                properties={
                    "patient_name": {"type": "string", "description": "Caller's full name"},
                    "location": {"type": "string", "description": "Which office/city the caller wants, e.g. Hialeah, Kendall, Tamarac"},
                    "reason": {"type": "string", "description": "Reason for the visit, e.g. annual exam, broken glasses, eye pain"},
                    "preferred_time": {"type": "string", "description": "Preferred day/time in the caller's words, if given"},
                },
                required=["patient_name"],
            ),
        ]
    )


class EndCallProcessor(FrameProcessor):
    """Lets Sofia hang up. MUST sit between the LLM and the TTS: there it sees her reply
    text (LLMTextFrame, flowing downstream) AND the upstream copy of BotStoppedSpeakingFrame
    the output transport emits. It accumulates each reply; if the finished reply contains a
    closing ('goodbye'/'adiós'), it waits until she's done speaking, then pushes EndTaskFrame
    upstream — the task ends and TwilioFrameSerializer (auto_hang_up) drops the call."""

    _CLOSINGS = ("goodbye", "good-bye", "good bye", "adiós", "adios", "hasta luego")

    def __init__(self):
        super().__init__()
        self._buf = ""
        self._should_end = False

    @classmethod
    def _is_closing(cls, text: str) -> bool:
        t = (text or "").lower()
        return any(c in t for c in cls._CLOSINGS)

    async def process_frame(self, frame: Frame, direction: FrameDirection):
        await super().process_frame(frame, direction)
        if isinstance(frame, LLMTextFrame):
            self._buf += frame.text
        elif isinstance(frame, LLMFullResponseEndFrame):
            if self._is_closing(self._buf):
                self._should_end = True
                logger.info("Sofia signalled closing -- will hang up after she finishes speaking")
            self._buf = ""
        elif isinstance(frame, BotStoppedSpeakingFrame) and self._should_end:
            self._should_end = False
            logger.info("Sofia closed the call -- ending task / hanging up")
            await self.push_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
        await self.push_frame(frame, direction)


class AudioHeartbeat(FrameProcessor):
    """Diagnostic: logs how many inbound audio frames arrive every ~5s. If this keeps
    ticking but VAD never fires, the issue is VAD/threshold; if it drops to 0 after a
    turn, inbound audio stalled at the transport. Cheap, leave it on while stabilizing."""

    def __init__(self):
        super().__init__()
        self._n = 0
        self._t = time.time()

    async def process_frame(self, frame: Frame, direction: FrameDirection):
        await super().process_frame(frame, direction)
        if isinstance(frame, InputAudioRawFrame):
            self._n += 1
            now = time.time()
            if now - self._t >= 5:
                logger.info(f"[audio-in] {self._n} frames in last {now - self._t:.0f}s")
                self._n = 0
                self._t = now
        await self.push_frame(frame, direction)


def build_llm_service():
    """Build the LLM service for the selected provider. The universal LLMContext +
    aggregators work with either, so only this construction differs (true A/B swap)."""
    if LLM_PROVIDER == "anthropic":
        if not ANTHROPIC_API_KEY:
            raise RuntimeError("LLM_PROVIDER=anthropic but ANTHROPIC_API_KEY is not set")
        logger.info(f"LLM provider: anthropic ({ANTHROPIC_MODEL})")
        # NOTE: Opus 4.8/4.7 reject temperature/top_p/top_k (HTTP 400), so we omit them —
        # this keeps the default Opus model working. For low-latency phone voice, prefer
        # claude-haiku-4-5 (fastest) or claude-sonnet-4-6 over Opus. enable_prompt_caching
        # caches the system prompt + growing conversation prefix (helps multi-turn cost/latency).
        return AnthropicLLMService(
            api_key=ANTHROPIC_API_KEY,
            settings=AnthropicLLMService.Settings(
                model=ANTHROPIC_MODEL,
                enable_prompt_caching=True,
                max_tokens=LLM_MAX_TOKENS,
            ),
        )
    logger.info(f"LLM provider: ollama ({OLLAMA_MODEL})")
    return OLLamaLLMService(
        settings=OLLamaLLMService.Settings(
            model=OLLAMA_MODEL,
            temperature=LLM_TEMPERATURE,
            max_tokens=LLM_MAX_TOKENS,
        ),
        base_url=OLLAMA_URL,
    )


async def run_agent(transport, caller_number=None, call_sid=None, do_capture=True):
    """Build + run the AVC voice agent on a given transport. Shared by the phone path
    (Twilio Media Stream) and the browser path (WebRTC) — same prompt, model, voice, and
    booking/hang-up logic; only the transport differs. do_capture writes the post-call
    appointment to Odoo (on for phone; off for browser testing so it doesn't make cards)."""
    stt = DeepgramSTTService(
        api_key=DEEPGRAM_API_KEY,
        settings=DeepgramSTTService.Settings(
            model="nova-2",
            language="en-US",
            smart_format=True,
            punctuate=True,
            interim_results=False,      # final transcripts only — avoids double-firing
            utterance_end_ms=1000,      # ms of silence before end-of-utterance fires
        ),
    )
    llm = build_llm_service()
    # In-call booking tool — only registered when ENABLE_TOOLS is on (auto: Claude yes,
    # local Ollama no, since llama3.1:8b over-calls/leaks). The handler is a closure so it
    # can stamp the verified caller-ID + call_sid onto the lead (the model never supplies a
    # phone number — we don't ask for one). With tools on, this writes the Odoo lead IN-CALL,
    # so the post-call extraction is skipped below to avoid a duplicate.
    if ENABLE_TOOLS:
        async def _record_appointment(params):
            args = params.arguments or {}
            if do_capture:
                from practice import persist_appointment
                persist_appointment({
                    "call_sid": call_sid,
                    "patient_name": args.get("patient_name"),
                    "callback_number": caller_number,  # verified caller-ID, not model-supplied
                    "location": args.get("location"),
                    "reason": args.get("reason"),
                    "preferred_time": args.get("preferred_time"),
                    "source": "in_call_tool",
                })
            else:
                logger.info(f"[capture off] would record appointment: {args.get('patient_name')} / {args.get('location')}")
            await params.result_callback(
                {"status": "recorded", "message": "Recorded — staff will call to confirm the time."}
            )

        llm.register_function("record_appointment_request", _record_appointment)

    tts = KokoroTTSService(
        model_path=os.path.join(MODEL_DIR, "kokoro-v1.0.onnx"),
        voices_path=os.path.join(MODEL_DIR, "voices-v1.0.bin"),
        settings=KokoroTTSService.Settings(voice=KOKORO_VOICE),
    )
    vad = VADProcessor(vad_analyzer=SileroVADAnalyzer(params=VADParams(
        confidence=VAD_CONFIDENCE,
        start_secs=VAD_START_SECS,
        stop_secs=VAD_STOP_SECS,
        min_volume=VAD_MIN_VOLUME,
    )))
    heartbeat = AudioHeartbeat()

    context_kwargs = {"messages": [{"role": "system", "content": SYSTEM_PROMPT}]}
    if ENABLE_TOOLS:
        context_kwargs["tools"] = _build_tools()
    context = LLMContext(**context_kwargs)
    agg = LLMContextAggregatorPair(context)
    endcall = EndCallProcessor()

    pipeline = Pipeline(
        [
            transport.input(),
            heartbeat,
            vad,
            stt,
            agg.user(),
            llm,
            endcall,
            tts,
            transport.output(),
            agg.assistant(),
        ]
    )

    task = PipelineTask(
        pipeline,
        params=PipelineParams(
            audio_in_sample_rate=PIPELINE_SAMPLE_RATE,
            audio_out_sample_rate=PIPELINE_SAMPLE_RATE,
            allow_interruptions=True,
        ),
    )

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        logger.info("Client connected -- greeting")
        await task.queue_frames(
            [TTSSpeakFrame(
                f"Thank you for calling Advanced Vision Care, this is {AGENT_NAME}. "
                "How can I help you today?"
            )]
        )

    @transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(transport, client):
        logger.info("Client disconnected -- ending task")
        await task.queue_frame(EndFrame())

    runner = PipelineRunner(handle_sigint=False)
    await runner.run(task)

    # Call is over. Post-call extraction is the capture path ONLY when in-call tools are
    # off (local Ollama). With tools on (Claude), the booking was already written in-call,
    # so skip extraction to avoid a duplicate lead.
    if do_capture and not ENABLE_TOOLS:
        try:
            from extract import extract_and_record

            await extract_and_record(
                context.messages, OLLAMA_URL, OLLAMA_MODEL,
                call_sid=call_sid, caller_number=caller_number,
            )
        except Exception:
            logger.exception("Post-call appointment extraction failed")


async def run_call(websocket, serializer: TwilioFrameSerializer, caller_number=None, call_sid=None):
    """Phone entrypoint: wrap the Twilio Media Stream in a transport, run the shared agent."""
    transport = FastAPIWebsocketTransport(
        websocket=websocket,
        params=FastAPIWebsocketParams(
            audio_in_enabled=True,
            audio_out_enabled=True,
            audio_in_sample_rate=PIPELINE_SAMPLE_RATE,
            audio_out_sample_rate=PIPELINE_SAMPLE_RATE,
            add_wav_header=False,
            serializer=serializer,
        ),
    )
    await run_agent(transport, caller_number=caller_number, call_sid=call_sid, do_capture=True)