Add MCP-style API server (OpenAPI) alongside WebUI

- New mcp_server.py: FastAPI app for LLMs to upload audio and get transcript JSON.
- Added process_mcp_transcribe_task Celery task.
- Updated __main__.py: WebUI always runs; MCP server runs in parallel when MCP_SERVER_ENABLED=true.

scraibe/__main__.py

@@ -3,10 +3,40 @@ Entrypoint for running ScrAIbe as a module:
 
     python -m scraibe
 
-Always launches the Web GUI (Gradio), never the CLI.
+Always launches the Web GUI (Gradio).
+Optionally launches an MCP-style API server in parallel.
 """
 
+import os
+import threading
+
 from .webui import create_app
 
+
+def _run_mcp_server():
+    """
+    Run MCP server in a separate thread.
+    """
+    import uvicorn
+    from . import mcp_server
+
+    host = os.getenv("MCP_SERVER_HOST", "0.0.0.0")
+    port = int(os.getenv("MCP_SERVER_PORT", "8000"))
+
+    uvicorn.run(
+        mcp_server.app,
+        host=host,
+        port=port,
+        log_level="info",
+    )
+
+
 if __name__ == "__main__":
+    # Optionally start MCP server in background
+    mcp_enabled = os.getenv("MCP_SERVER_ENABLED", "false").strip().lower() in ("true", "1", "yes")
+    if mcp_enabled:
+        t = threading.Thread(target=_run_mcp_server, daemon=True)
+        t.start()
+
+    # Always start WebUI (Gradio)
     create_app()

scraibe/mcp_server.py

@@ -0,0 +1,205 @@
+"""
+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,
+        }
+    )

scraibe/tasks.py

@@ -504,3 +504,68 @@ def process_transcription_task(
         if audio_path:
             _remove_file(audio_path)
         logger.info("Cleanup completed for job %s.", task_id)
+
+
+@celery_app.task(
+    name="scraibe.tasks.process_mcp_transcribe_task",
+    bind=True,
+    max_retries=1,
+    task_time_limit=14400,
+    task_soft_time_limit=13500,
+)
+def process_mcp_transcribe_task(
+    self,
+    audio_path: str,
+    job_id: str,
+    language: str,
+    num_speakers: int,
+):
+    """
+    Async task used by MCP-style API:
+    - Transcribe audio
+    - Store transcript + segments in shared MCP job store
+    - Clean up temporary file
+    """
+    from .mcp_server import _mcp_jobs
+
+    log_level = os.getenv("LOG_LEVEL", "INFO")
+    setup_logging(level=log_level)
+
+    # Initialize status
+    _mcp_jobs.setdefault(
+        job_id,
+        {
+            "status": "processing",
+            "message": "Transcription started (async).",
+            "file_path": audio_path,
+        },
+    )
+
+    try:
+        scraibe = Scraibe(verbose=True)
+        result = scraibe.transcribe(
+            audio_file=audio_path,
+            language=language or None,
+            num_speakers=int(num_speakers) if num_speakers else None,
+            verbose=True,
+            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."
+
+        logger.info("MCP job %s completed.", job_id)
+
+    except Exception as e:
+        logger.error("MCP job %s failed: %s", job_id, e, exc_info=True)
+        _mcp_jobs[job_id]["status"] = "error"
+        _mcp_jobs[job_id]["message"] = f"Transcription error: {e}"
+
+    finally:
+        _remove_file(audio_path)
+        logger.info("MCP job %s cleanup completed.", job_id)