"""
MCP-style HTTP server for ScrAIbe.

- Exposes an OpenAPI-compliant endpoint for external LLMs to:
  - Upload audio
  - Receive transcript JSON (no summary)
- WebUI remains always enabled; this is additive.

Configuration (env):
- MCP_SERVER_ENABLED: "true"/"false" (default: false)
- MCP_SERVER_HOST: bind address (default: 0.0.0.0)
- MCP_SERVER_PORT: port (default: 8000)
- MCP_USE_CELERY: "true"/"false" (default: true)
  - If true, uses Celery tasks; if false, runs synchronously.
"""

import os
import time
import uuid
import json
import logging
from typing import Optional

from fastapi import FastAPI, UploadFile, File, Form, HTTPException
from fastapi.responses import JSONResponse

from .autotranscript import Scraibe

logger = logging.getLogger("scraibe.mcp_server")

app = FastAPI(
    title="ScrAIbe MCP Transcription API",
    version="0.1.0",
    description=(
        "MCP-style HTTP API for ScrAIbe. "
        "Allows external LLMs to upload audio and receive transcript JSON."
    ),
)

# In-memory job store for MCP (simple; can be replaced with Redis later)
_mcp_jobs: dict = {}


def _job_id() -> str:
    return str(uuid.uuid4())


@app.get("/health")
async def health():
    return {"status": "ok"}


@app.post("/transcribe")
async def transcribe(
    file: UploadFile = File(...),
    language: Optional[str] = Form(None),
    num_speakers: Optional[int] = Form(None),
):
    """
    Upload audio and start transcription.

    Returns:
      {
        "job_id": "<id>",
        "status": "queued" | "processing" | "completed" | "error",
        "message": "..."
      }

    Use GET /transcribe/{job_id}/status and /json to retrieve results.
    """
    use_celery = os.getenv("MCP_USE_CELERY", "true").strip().lower() in ("true", "1", "yes")

    # Save uploaded file temporarily
    try:
        import tempfile
        from pathlib import Path

        upload_dir = Path(os.getenv("SCRAIBE_UPLOAD_DIR", "/tmp/scraibe_uploads"))
        upload_dir.mkdir(parents=True, exist_ok=True)

        ext = Path(file.filename or "file").suffix or ".wav"
        ts = time.strftime("%Y%m%d%H%M%S")
        tmp_name = f"mcp_upload_{ts}_{uuid.uuid4().hex[:8]}{ext}"
        file_path = upload_dir / tmp_name

        content = await file.read()
        file_path.write_bytes(content)
    except Exception as e:
        logger.error("Error saving MCP upload: %s", e)
        raise HTTPException(status_code=500, detail=f"Error saving file: {e}")

    job_id = _job_id()

    if use_celery:
        try:
            from .tasks import process_mcp_transcribe_task
        except ImportError:
            # Fallback: run synchronously
            use_celery = False

        if use_celery:
            try:
                process_mcp_transcribe_task.delay(
                    audio_path=str(file_path),
                    job_id=job_id,
                    language=language or None,
                    num_speakers=int(num_speakers) if num_speakers else None,
                )
            except Exception as e:
                logger.error("Error enqueuing MCP job: %s", e)
                _mcp_jobs[job_id] = {
                    "status": "error",
                    "message": f"Error enqueuing job: {e}",
                    "file_path": str(file_path),
                }
                return {
                    "job_id": job_id,
                    "status": "error",
                    "message": _mcp_jobs[job_id]["message"],
                }

            _mcp_jobs[job_id] = {
                "status": "queued",
                "message": "Job queued for processing.",
                "file_path": str(file_path),
            }
            return {
                "job_id": job_id,
                "status": "queued",
                "message": _mcp_jobs[job_id]["message"],
            }

    # Synchronous path
    _mcp_jobs[job_id] = {
        "status": "processing",
        "message": "Transcription started (synchronous).",
        "file_path": str(file_path),
    }

    def _run_sync():
        try:
            scraibe = Scraibe(verbose=False)
            result = scraibe.transcribe(
                audio_file=str(file_path),
                language=language or None,
                num_speakers=int(num_speakers) if num_speakers else None,
                verbose=False,
                for_export=True,
            )
            transcript_text = result.get("transcript", "")
            segments = result.get("segments", [])
            _mcp_jobs[job_id]["status"] = "completed"
            _mcp_jobs[job_id]["transcript"] = transcript_text
            _mcp_jobs[job_id]["segments"] = segments
            _mcp_jobs[job_id]["message"] = "Transcription completed."
        except Exception as e:
            logger.error("MCP sync transcription error: %s", e)
            _mcp_jobs[job_id]["status"] = "error"
            _mcp_jobs[job_id]["message"] = f"Transcription error: {e}"

    import threading
    t = threading.Thread(target=_run_sync, daemon=True)
    t.start()

    return {
        "job_id": job_id,
        "status": "processing",
        "message": _mcp_jobs[job_id]["message"],
    }


@app.get("/transcribe/{job_id}/status")
async def get_status(job_id: str):
    job = _mcp_jobs.get(job_id)
    if not job:
        raise HTTPException(status_code=404, detail="Job not found")
    return {
        "job_id": job_id,
        "status": job["status"],
        "message": job.get("message", ""),
    }


@app.get("/transcribe/{job_id}/json")
async def get_json(job_id: str):
    job = _mcp_jobs.get(job_id)
    if not job:
        raise HTTPException(status_code=404, detail="Job not found")

    if job["status"] != "completed":
        raise HTTPException(
            status_code=400,
            detail=f"Job not completed. Current status: {job['status']}",
        )

    transcript_text = job.get("transcript", "")
    segments = job.get("segments", [])

    return JSONResponse(
        content={
            "job_id": job_id,
            "transcript": transcript_text,
            "segments": segments,
        }
    )