"""
VoiceVerse AI — TTS Module.

Primary:  Qwen3-TTS via HF Inference API
Fallback: Edge-TTS (CPU, no key needed)

Voice + audio style per mode:
  Summary   — neutral female voice, normal rate
  Podcast   — HOST_1 female (AriaNeural) / HOST_2 male (GuyNeural)
  Rap       — male voice, faster rate (+40%), bass boost via pydub
  Song      — female voice, normal rate
  Debate    — DEBATER_A female (JennyNeural, +5%) / DEBATER_B male (DavisNeural, -5%)
  Story     — female voice, slow rate (-30%), long silence gaps between sentences
"""

import os
import re
import asyncio
from utils import logger, get_temp_filepath

QWEN_TTS_MODEL = "Qwen/Qwen3-TTS"
TTS_MAX_CHARS  = 3000

# ── Voice assignments ─────────────────────────────────────────────────────────
# Summary / Song / Story — single female voice
EDGE_VOICE_FEMALE        = "en-US-AriaNeural"

# Podcast
EDGE_VOICE_HOST_FEMALE   = "en-US-AriaNeural"    # HOST_1 — female
EDGE_VOICE_HOST_MALE     = "en-US-GuyNeural"     # HOST_2 — male

# Rap — male voice reads the rap
EDGE_VOICE_RAP           = "en-US-GuyNeural"
RAP_RATE                 = "+40%"                 # fast delivery

# Debate
EDGE_VOICE_DEBATER_A     = "en-US-JennyNeural"   # female, pro — assertive
EDGE_VOICE_DEBATER_B     = "en-US-DavisNeural"   # male, con  — skeptical
DEBATE_RATE_A            = "+8%"                  # slightly faster
DEBATE_RATE_B            = "-5%"                  # slightly slower, deliberate

# Story — slow, warm delivery
EDGE_VOICE_STORY         = "en-US-AriaNeural"
STORY_RATE               = "-30%"                 # noticeably slower


# ══════════════════════════════════════════════════════════════════════════════
# Low-level TTS helpers
# ══════════════════════════════════════════════════════════════════════════════

def _qwen_tts(text: str) -> str | None:
    token = os.environ.get("HF_TOKEN")
    if not token:
        return None
    try:
        from huggingface_hub import InferenceClient
        client = InferenceClient(token=token)
        audio_bytes = client.text_to_speech(text=text[:TTS_MAX_CHARS], model=QWEN_TTS_MODEL)
        if not audio_bytes:
            return None
        path = get_temp_filepath(suffix=".wav")
        with open(path, "wb") as f:
            f.write(audio_bytes)
        logger.info("Qwen TTS: %s (%d bytes)", path, len(audio_bytes))
        return path
    except Exception as e:
        logger.warning("Qwen TTS failed: %s", e)
        return None


def _edge_tts(text: str, voice: str = EDGE_VOICE_FEMALE, rate: str = "+0%") -> str:
    """
    Generate audio via Edge-TTS.
    rate: SSML prosody rate string, e.g. "+40%" faster, "-30%" slower.
    """
    import edge_tts
    path = get_temp_filepath(suffix=".mp3")
    snippet = text[:TTS_MAX_CHARS]

    async def _run():
        communicate = edge_tts.Communicate(snippet, voice, rate=rate)
        await communicate.save(path)

    try:
        loop = asyncio.get_event_loop()
        if loop.is_running():
            import concurrent.futures
            with concurrent.futures.ThreadPoolExecutor() as pool:
                pool.submit(asyncio.run, _run()).result(timeout=120)
        else:
            loop.run_until_complete(_run())
    except RuntimeError:
        asyncio.run(_run())

    if os.path.getsize(path) == 0:
        raise RuntimeError("Edge-TTS produced an empty audio file.")
    logger.info("Edge-TTS: %s (voice=%s rate=%s)", path, voice, rate)
    return path


# ══════════════════════════════════════════════════════════════════════════════
# Audio post-processing
# ══════════════════════════════════════════════════════════════════════════════

def _apply_rap_fx(path: str) -> str:
    """
    Apply bass boost to a rap audio file using pydub.
    Low-frequency boost makes it sound punchier and more rap-like.
    Returns path to processed file (new file).
    """
    try:
        from pydub import AudioSegment
        from pydub.effects import low_pass_filter

        audio = AudioSegment.from_file(path)

        # Split into bass (low) and mid/high frequencies
        bass  = low_pass_filter(audio, 200)          # frequencies below 200 Hz
        highs = audio - low_pass_filter(audio, 200)  # everything above

        # Boost bass by 6 dB, keep highs as-is, combine
        boosted = (bass + 6).overlay(highs)

        out = get_temp_filepath(suffix=".mp3")
        boosted.export(out, format="mp3")
        logger.info("Rap bass boost applied → %s", out)
        return out
    except Exception as e:
        logger.warning("Rap FX failed (%s) — returning original audio", e)
        return path


def _concat(paths: list[str], silence_ms: int = 300) -> str:
    """Concatenate audio files with silence between each segment."""
    if len(paths) == 1:
        return paths[0]
    try:
        from pydub import AudioSegment
        combined = AudioSegment.empty()
        silence  = AudioSegment.silent(duration=silence_ms)
        for p in paths:
            combined += AudioSegment.from_file(p) + silence
        out = get_temp_filepath(suffix=".mp3")
        combined.export(out, format="mp3")
        logger.info("Concatenated %d segments → %s", len(paths), out)
        return out
    except Exception as e:
        logger.warning("pydub concat failed (%s) — returning first segment", e)
        return paths[0]


def _add_story_gaps(path: str) -> str:
    """
    Insert longer silence gaps between sentences in story audio.
    Gives the warm, unhurried feel of a storyteller.
    """
    try:
        from pydub import AudioSegment
        audio   = AudioSegment.from_file(path)
        gap     = AudioSegment.silent(duration=600)   # 600 ms between sentences
        # Split on natural pauses (every ~5 seconds of audio) and re-join with gaps
        chunk_ms = 5000
        chunks   = [audio[i:i + chunk_ms] for i in range(0, len(audio), chunk_ms)]
        combined = AudioSegment.empty()
        for chunk in chunks:
            combined += chunk + gap
        out = get_temp_filepath(suffix=".mp3")
        combined.export(out, format="mp3")
        logger.info("Story gaps applied → %s", out)
        return out
    except Exception as e:
        logger.warning("Story gap insertion failed (%s) — returning original", e)
        return path


# ══════════════════════════════════════════════════════════════════════════════
# Dialogue script parser
# ══════════════════════════════════════════════════════════════════════════════

def _parse_dialogue(script: str, tag_a: str, tag_b: str) -> list[tuple[str, str]]:
    """Parse a HOST_X / DEBATER_X tagged script into (speaker, text) segments."""
    segments: list[tuple[str, str]] = []
    prefix_a = f"{tag_a}:"
    prefix_b = f"{tag_b}:"

    for line in script.splitlines():
        line = line.strip()
        if line.startswith(prefix_a):
            text = line[len(prefix_a):].strip()
            if text:
                if segments and segments[-1][0] == tag_a:
                    segments[-1] = (tag_a, segments[-1][1] + " " + text)
                else:
                    segments.append((tag_a, text))
        elif line.startswith(prefix_b):
            text = line[len(prefix_b):].strip()
            if text:
                if segments and segments[-1][0] == tag_b:
                    segments[-1] = (tag_b, segments[-1][1] + " " + text)
                else:
                    segments.append((tag_b, text))
    return segments


# ══════════════════════════════════════════════════════════════════════════════
# Per-mode audio generators
# ══════════════════════════════════════════════════════════════════════════════

def generate_audio_podcast(script: str) -> tuple[str, str]:
    """
    Podcast: HOST_1 = female (AriaNeural), HOST_2 = male (GuyNeural).
    Normal conversational rate, 300 ms silence between turns.
    """
    segments = _parse_dialogue(script, "HOST_1", "HOST_2")
    if not segments:
        logger.warning("No HOST tags — falling back to single voice")
        return generate_audio(script)

    voice_map = {
        "HOST_1": (EDGE_VOICE_HOST_FEMALE, "+0%"),
        "HOST_2": (EDGE_VOICE_HOST_MALE,   "+0%"),
    }
    paths = []
    for speaker, text in segments:
        voice, rate = voice_map[speaker]
        try:
            paths.append(_edge_tts(text, voice=voice, rate=rate))
        except Exception as e:
            logger.warning("Podcast segment failed %s: %s", speaker, e)

    if not paths:
        raise RuntimeError("All podcast segments failed.")
    return _concat(paths, silence_ms=300), "Edge-TTS (Podcast)"


def generate_audio_debate(script: str) -> tuple[str, str]:
    """
    Debate: DEBATER_A = female (JennyNeural, assertive +8%),
            DEBATER_B = male   (DavisNeural, deliberate -5%).
    400 ms silence between turns for debate feel.
    """
    segments = _parse_dialogue(script, "DEBATER_A", "DEBATER_B")
    if not segments:
        logger.warning("No DEBATER tags — falling back to single voice")
        return generate_audio(script)

    voice_map = {
        "DEBATER_A": (EDGE_VOICE_DEBATER_A, DEBATE_RATE_A),
        "DEBATER_B": (EDGE_VOICE_DEBATER_B, DEBATE_RATE_B),
    }
    paths = []
    for speaker, text in segments:
        voice, rate = voice_map[speaker]
        try:
            paths.append(_edge_tts(text, voice=voice, rate=rate))
        except Exception as e:
            logger.warning("Debate segment failed %s: %s", speaker, e)

    if not paths:
        raise RuntimeError("All debate segments failed.")
    return _concat(paths, silence_ms=400), "Edge-TTS (Debate)"


def generate_audio_rap(script: str) -> tuple[str, str]:
    """
    Rap: male voice, fast rate (+40%), then bass boost applied via pydub.
    """
    path = _edge_tts(script, voice=EDGE_VOICE_RAP, rate=RAP_RATE)
    path = _apply_rap_fx(path)
    return path, "Edge-TTS (Rap)"


def generate_audio_story(script: str) -> tuple[str, str]:
    """
    Story: female voice, slow rate (-30%), then sentence gaps widened via pydub.
    """
    path = _edge_tts(script, voice=EDGE_VOICE_STORY, rate=STORY_RATE)
    path = _add_story_gaps(path)
    return path, "Edge-TTS (Story)"


# ══════════════════════════════════════════════════════════════════════════════
# Unified public interface
# ══════════════════════════════════════════════════════════════════════════════

def generate_audio(text: str, voice_id: str | None = None) -> tuple[str, str]:
    """Single-voice TTS for Summary and Song modes. Tries Qwen first."""
    if not text or not text.strip():
        raise ValueError("No text provided for audio generation.")
    path = _qwen_tts(text)
    if path and os.path.exists(path):
        return path, "Qwen3-TTS"
    return _edge_tts(text, voice=voice_id or EDGE_VOICE_FEMALE), "Edge-TTS"