# CoderAI - OpenAI-compatible API server
# Copyright (C) 2026 Stefy Lanza <stefy@nexlab.net>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.

"""
Audio transcription endpoint for the codai API.
"""

import asyncio
import io
import os
import tempfile

from fastapi import APIRouter, HTTPException, UploadFile, File, Form
from fastapi.responses import PlainTextResponse
from typing import Optional

# Maximum upload size: 100 MB
_MAX_AUDIO_BYTES = 100 * 1024 * 1024

# Safe audio extensions (user-supplied extension is NOT trusted for the suffix)
_SAFE_EXTENSIONS = {'.wav', '.mp3', '.ogg', '.flac', '.m4a', '.webm', '.mp4'}

# Import from codai modules
from codai.models.manager import multi_model_manager


# Global reference to be set by coderai
global_args = None


def set_global_args(args):
    """Set global args from coderai."""
    global global_args
    global_args = args


# =============================================================================
# Response formatting helpers
# =============================================================================

def _seconds_to_srt_time(s: float) -> str:
    h = int(s // 3600)
    m = int((s % 3600) // 60)
    sec = s % 60
    return f"{h:02d}:{m:02d}:{sec:06.3f}".replace('.', ',')


def _seconds_to_vtt_time(s: float) -> str:
    h = int(s // 3600)
    m = int((s % 3600) // 60)
    sec = s % 60
    return f"{h:02d}:{m:02d}:{sec:06.3f}"


def _format_response(fmt: str, text: str, segments: list):
    """Format a transcription result according to the requested response_format."""
    fmt = (fmt or "json").lower()

    if fmt == "text":
        return PlainTextResponse(text)

    if fmt == "srt":
        lines = []
        for i, seg in enumerate(segments, 1):
            start = _seconds_to_srt_time(seg.get("start", 0))
            end = _seconds_to_srt_time(seg.get("end", 0))
            lines.append(f"{i}\n{start} --> {end}\n{seg['text'].strip()}\n")
        srt_body = "\n".join(lines) if lines else f"1\n00:00:00,000 --> 00:00:00,000\n{text}\n"
        return PlainTextResponse(srt_body, media_type="text/plain")

    if fmt == "vtt":
        lines = ["WEBVTT\n"]
        for seg in segments:
            start = _seconds_to_vtt_time(seg.get("start", 0))
            end = _seconds_to_vtt_time(seg.get("end", 0))
            lines.append(f"{start} --> {end}\n{seg['text'].strip()}\n")
        if not segments:
            lines.append(f"00:00:00.000 --> 00:00:00.000\n{text}\n")
        return PlainTextResponse("\n".join(lines), media_type="text/vtt")

    if fmt == "verbose_json":
        return {
            "task": "transcribe",
            "language": "unknown",
            "duration": segments[-1].get("end", 0) if segments else 0,
            "text": text,
            "segments": [
                {
                    "id": i,
                    "start": s.get("start", 0),
                    "end": s.get("end", 0),
                    "text": s.get("text", "").strip(),
                }
                for i, s in enumerate(segments)
            ],
        }

    # Default: json
    return {"text": text}


# =============================================================================
# Router and Endpoints
# =============================================================================

router = APIRouter()


@router.post("/v1/audio/transcriptions")
async def create_transcription(
    model: str = Form(...),
    file: UploadFile = File(...),
    language: Optional[str] = Form(None),
    prompt: Optional[str] = Form(None),
    response_format: Optional[str] = Form("json"),
    temperature: Optional[float] = Form(0.0),
):
    """
    Audio transcription endpoint (OpenAI-compatible).
    """
    file_content = await file.read()
    if len(file_content) > _MAX_AUDIO_BYTES:
        raise HTTPException(status_code=413, detail="Audio file too large (max 100 MB)")

    # Check if the requested model maps to a configured whisper-server instance first.
    # Try alias round-robin resolution before direct ID lookup.
    whisper_model_id = multi_model_manager.resolve_whisper_alias_model_id(model)
    whisper_server = (
        multi_model_manager.whisper_servers.get(whisper_model_id)
        if whisper_model_id is not None
        else multi_model_manager.whisper_servers.get(model)
    )
    if whisper_server is not None:
        await asyncio.to_thread(
            multi_model_manager.request_model, requested_model=model, model_type="audio")
        if not whisper_server.is_running():
            whisper_server.start(
                getattr(whisper_server, "_model_path", None),
                gpu_device=getattr(whisper_server, "_gpu_device", 0),
            )
            if whisper_server.is_running():
                ws_key = f"audio:{whisper_model_id or model}"
                multi_model_manager.models[ws_key] = whisper_server
                multi_model_manager.active_in_vram = ws_key
                multi_model_manager.models_in_vram.add(ws_key)
        if not whisper_server.is_running():
            raise HTTPException(status_code=500, detail="whisper-server failed to start")
        result = whisper_server.transcribe(
            file_content,
            language=language,
            prompt=prompt
        )
        if "error" in result:
            raise HTTPException(status_code=500, detail=result["error"])
        return _format_response(response_format, result.get("text", ""), [])

    # Use the manager to resolve the model and manage VRAM
    model_info = await asyncio.to_thread(
        multi_model_manager.request_model,
        requested_model=model,
        model_type="audio"
    )

    # Check if the model was rejected as not allowed
    if model_info.get('error'):
        raise HTTPException(status_code=404, detail=model_info['error'])

    model_name = model_info['model_name']
    model_key = model_info['model_key']
    whisper_model = model_info['model_object']

    if not model_name:
        raise HTTPException(
            status_code=400,
            detail="Audio transcription not configured. Use --audio-model or --whisper-server."
        )

    # Determine a safe file extension from the upload's content-type or filename,
    # never trusting the raw user-supplied value for arbitrary suffixes.
    raw_ext = os.path.splitext(file.filename or '')[1].lower()
    safe_ext = raw_ext if raw_ext in _SAFE_EXTENSIONS else '.wav'

    # Save to temp file (needed for some backends)
    with tempfile.NamedTemporaryFile(delete=False, suffix=safe_ext) as tmp:
        tmp.write(file_content)
        tmp_path = tmp.name
    
    try:
        # Try faster-whisper first
        try:
            from faster_whisper import WhisperModel

            if whisper_model is None:
                whisper_model = WhisperModel(
                    model_name,
                    device="cpu",
                    compute_type="int8",
                )
                multi_model_manager.add_model(model_key, whisper_model)
                multi_model_manager.current_model_key = model_key

            raw_segments, _ = whisper_model.transcribe(
                tmp_path,
                language=language,
                initial_prompt=prompt,
                temperature=temperature,
            )
            # Materialise the generator so we have all segment data
            segments = [
                {"start": s.start, "end": s.end, "text": s.text}
                for s in raw_segments
            ]
            full_text = "".join(s["text"] for s in segments)
            return _format_response(response_format, full_text.strip(), segments)

        except ImportError:
            pass

        # Try whispercpp as fallback
        try:
            import whispercpp

            if whisper_model is None:
                whisper_model = whispercpp.Whisper.from_pretrained(model_name)
                multi_model_manager.add_model(model_key, whisper_model)
                multi_model_manager.current_model_key = model_key

            result = whisper_model.transcribe(tmp_path)

            text = ""
            if hasattr(result, 'text'):
                text = result.text
            elif isinstance(result, dict):
                text = result.get('text', '')
            elif isinstance(result, list):
                for segment in result:
                    if hasattr(segment, 'text'):
                        text += segment.text
                    elif isinstance(segment, dict):
                        text += segment.get('text', '')

            # whispercpp does not expose per-segment timestamps easily
            return _format_response(response_format, text.strip(), [])

        except ImportError as e:
            raise HTTPException(
                status_code=501,
                detail="Audio transcription not available. Install faster-whisper or whispercpp."
            )
            
    except Exception as e:
        print(f"Transcription error: {e}")
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=f"Transcription error: {str(e)}")
    finally:
        # Clean up temp file
        try:
            os.unlink(tmp_path)
        except Exception:
            pass
