"""
Department 2 — Transcriber
Primary  : Groq API (Whisper large-v3 on H100) — free tier 14,400 s/day
Fallback : faster-whisper large-v3 int8 (local CPU) if Groq fails or limit reached

✅ UPGRADED:
  - Chunking support — splits long audio into 60s pieces automatically
  - Groq limit is 25MB per file, chunking handles large files
  - Chunks rejoined seamlessly into full transcript
"""

import os
import time
import logging
import subprocess
import tempfile
import shutil

logger = logging.getLogger(__name__)

LANG_TO_WHISPER = {
    "auto": None,
    "en":   "en",
    "te":   "te",
    "hi":   "hi",
    "ta":   "ta",
    "kn":   "kn",
}

CHUNK_DURATION_SEC = 60  # Groq max is 25MB — 60s chunks stay safe


class Transcriber:
    def __init__(self):
        self.groq_key     = os.environ.get("GROQ_API_KEY", "")
        self._groq_client = None
        self._local_model = None

        if self.groq_key:
            print("[Transcriber] Groq API key found — primary = Groq Whisper large-v3")
            self._init_groq()
        else:
            print("[Transcriber] No GROQ_API_KEY — local Whisper loads on first use")

    def transcribe(self, audio_path: str, language: str = "auto"):
        lang_hint = LANG_TO_WHISPER.get(language, None)
        duration  = self._get_duration(audio_path)
        print(f"[Transcriber] Audio duration: {duration:.1f}s")

        if duration <= CHUNK_DURATION_SEC:
            return self._transcribe_single(audio_path, lang_hint)

        print(f"[Transcriber] Long audio — splitting into {CHUNK_DURATION_SEC}s chunks")
        return self._transcribe_chunked(audio_path, lang_hint, duration)

    def _transcribe_chunked(self, audio_path, language, duration):
        tmp_dir = tempfile.mkdtemp()
        chunks  = []
        start   = 0
        index   = 0

        while start < duration:
            chunk_path = os.path.join(tmp_dir, f"chunk_{index:03d}.wav")
            subprocess.run([
                "ffmpeg", "-y", "-i", audio_path,
                "-ss", str(start), "-t", str(CHUNK_DURATION_SEC),
                "-acodec", "pcm_s16le", "-ar", "16000", "-ac", "1",
                chunk_path
            ], capture_output=True)
            if os.path.exists(chunk_path):
                chunks.append(chunk_path)
            start += CHUNK_DURATION_SEC
            index += 1

        print(f"[Transcriber] Processing {len(chunks)} chunks...")
        transcripts   = []
        detected_lang = language or "en"
        method        = "unknown"

        for i, chunk in enumerate(chunks):
            print(f"[Transcriber] Chunk {i+1}/{len(chunks)}...")
            try:
                text, lang, m = self._transcribe_single(chunk, language)
                transcripts.append(text.strip())
                detected_lang = lang
                method        = m
            except Exception as e:
                logger.warning(f"Chunk {i+1} failed: {e}")

        shutil.rmtree(tmp_dir, ignore_errors=True)
        full = " ".join(t for t in transcripts if t)
        print(f"[Transcriber] Done — {len(full)} chars total")
        return full, detected_lang, f"{method} (chunked {len(chunks)}x)"

    def _transcribe_single(self, audio_path, language):
        if self._groq_client is not None:
            try:
                return self._transcribe_groq(audio_path, language)
            except Exception as e:
                logger.warning(f"Groq failed ({e}), falling back to local")
                if self._local_model is None:
                    self._init_local()
        return self._transcribe_local(audio_path, language)

    def _init_groq(self):
        try:
            from groq import Groq
            self._groq_client = Groq(api_key=self.groq_key)
            print("[Transcriber] Groq client initialised")
        except Exception as e:
            logger.warning(f"Groq init failed: {e}")
            self._groq_client = None

    def _transcribe_groq(self, audio_path, language=None):
        t0 = time.time()
        with open(audio_path, "rb") as f:
            kwargs = dict(file=f, model="whisper-large-v3",
                          response_format="verbose_json", temperature=0.0)
            if language:
                kwargs["language"] = language
            resp = self._groq_client.audio.transcriptions.create(**kwargs)
        transcript    = resp.text.strip()
        detected_lang = self._normalise_lang(getattr(resp, "language", language or "en") or "en")
        logger.info(f"Groq done in {time.time()-t0:.2f}s, lang={detected_lang}")
        return transcript, detected_lang, "Groq Whisper large-v3"

    def _init_local(self):
        try:
            from faster_whisper import WhisperModel
            print("[Transcriber] Loading faster-whisper large-v3 int8...")
            self._local_model = WhisperModel("large-v3", device="cpu", compute_type="int8")
            print("[Transcriber] faster-whisper ready")
        except Exception as e:
            logger.error(f"Local Whisper init failed: {e}")
            self._local_model = None

    def _transcribe_local(self, audio_path, language=None):
        t0 = time.time()
        if self._local_model is None:
            self._init_local()
        if self._local_model is None:
            raise RuntimeError("No transcription engine available.")
        segments, info = self._local_model.transcribe(
            audio_path, language=language, beam_size=5,
            vad_filter=True, vad_parameters=dict(min_silence_duration_ms=500))
        transcript    = " ".join(seg.text.strip() for seg in segments).strip()
        detected_lang = info.language or language or "en"
        logger.info(f"Local done in {time.time()-t0:.2f}s")
        return transcript, detected_lang, "faster-whisper large-v3 int8 (local)"

    def _get_duration(self, audio_path):
        try:
            result = subprocess.run([
                "ffprobe", "-v", "error",
                "-show_entries", "format=duration",
                "-of", "default=noprint_wrappers=1:nokey=1",
                audio_path
            ], capture_output=True, text=True)
            return float(result.stdout.strip())
        except Exception:
            return 0.0

    @staticmethod
    def _normalise_lang(raw):
        mapping = {"english":"en","telugu":"te","hindi":"hi",
                   "tamil":"ta","kannada":"kn","spanish":"es",
                   "french":"fr","german":"de","japanese":"ja","chinese":"zh"}
        return mapping.get(raw.lower(), raw[:2].lower() if len(raw) >= 2 else raw)