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/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)