hatchet: restore zullip report

server/reflector/hatchet/run_workers.py

@@ -30,9 +30,13 @@ def main() -> None:
         debug=settings.HATCHET_DEBUG,
     )
 
-    # Import workflows to register them
-    from reflector.hatchet.client import HatchetClientManager
-    from reflector.hatchet.workflows import diarization_pipeline, track_workflow
+    # Import here (not top-level) - workflow imports trigger HatchetClientManager.get_client()
+    # which requires HATCHET_CLIENT_TOKEN; must validate settings first
+    from reflector.hatchet.client import HatchetClientManager  # noqa: PLC0415
+    from reflector.hatchet.workflows import (  # noqa: PLC0415
+        diarization_pipeline,
+        track_workflow,
+    )
 
     hatchet = HatchetClientManager.get_client()
 

server/reflector/hatchet/workflows/diarization_pipeline.py

@@ -10,13 +10,17 @@ import functools
 import tempfile
 from contextlib import asynccontextmanager
 from datetime import timedelta
+from fractions import Fraction
 from pathlib import Path
 from typing import Callable
 
 import av
+import httpx
+from av.audio.resampler import AudioResampler
 from hatchet_sdk import Context
 from pydantic import BaseModel
 
+from reflector.dailyco_api.client import DailyApiClient
 from reflector.hatchet.client import HatchetClientManager
 from reflector.hatchet.progress import emit_progress_async
 from reflector.hatchet.workflows.models import (
@@ -36,6 +40,23 @@ from reflector.hatchet.workflows.models import (
 )
 from reflector.hatchet.workflows.track_processing import TrackInput, track_workflow
 from reflector.logger import logger
+from reflector.pipelines import topic_processing
+from reflector.processors import AudioFileWriterProcessor
+from reflector.processors.types import (
+    TitleSummary,
+    Word,
+)
+from reflector.processors.types import (
+    Transcript as TranscriptType,
+)
+from reflector.settings import settings
+from reflector.storage.storage_aws import AwsStorage
+from reflector.utils.audio_waveform import get_audio_waveform
+from reflector.utils.daily import (
+    filter_cam_audio_tracks,
+    parse_daily_recording_filename,
+)
+from reflector.zulip import post_transcript_notification
 
 # Audio constants
 OPUS_STANDARD_SAMPLE_RATE = 48000
@@ -74,7 +95,6 @@ async def fresh_db_connection():
     import databases
 
     from reflector.db import _database_context
-    from reflector.settings import settings
 
     _database_context.set(None)
     db = databases.Database(settings.DATABASE_URL)
@@ -116,9 +136,6 @@ async def set_workflow_error_status(transcript_id: str) -> bool:
 
 def _get_storage():
     """Create fresh storage instance."""
-    from reflector.settings import settings
-    from reflector.storage.storage_aws import AwsStorage
-
     return AwsStorage(
         aws_bucket_name=settings.TRANSCRIPT_STORAGE_AWS_BUCKET_NAME,
         aws_region=settings.TRANSCRIPT_STORAGE_AWS_REGION,
@@ -198,9 +215,6 @@ async def get_recording(input: PipelineInput, ctx: Context) -> RecordingResult:
                 transcript_id=input.transcript_id,
             )
 
-    from reflector.dailyco_api.client import DailyApiClient
-    from reflector.settings import settings
-
     if not input.recording_id:
         # No recording_id in reprocess path - return minimal data
         await emit_progress_async(
@@ -257,13 +271,6 @@ async def get_participants(input: PipelineInput, ctx: Context) -> ParticipantsRe
     recording_data = _to_dict(ctx.task_output(get_recording))
     mtg_session_id = recording_data.get("mtg_session_id")
 
-    from reflector.dailyco_api.client import DailyApiClient
-    from reflector.settings import settings
-    from reflector.utils.daily import (
-        filter_cam_audio_tracks,
-        parse_daily_recording_filename,
-    )
-
     # Get transcript and reset events/topics/participants
     async with fresh_db_connection():
         from reflector.db.transcripts import (
@@ -488,12 +495,6 @@ async def mixdown_tracks(input: PipelineInput, ctx: Context) -> MixdownResult:
             padded_urls.append(url)
 
     # Use PipelineMainMultitrack.mixdown_tracks which uses PyAV filter graph
-    from fractions import Fraction
-
-    from av.audio.resampler import AudioResampler
-
-    from reflector.processors import AudioFileWriterProcessor
-
     valid_urls = [url for url in padded_urls if url]
     if not valid_urls:
         raise ValueError("No valid padded tracks to mixdown")
@@ -688,10 +689,7 @@ async def generate_waveform(input: PipelineInput, ctx: Context) -> WaveformResul
         input.transcript_id, "generate_waveform", "in_progress", ctx.workflow_run_id
     )
 
-    import httpx
-
     from reflector.db.transcripts import TranscriptWaveform, transcripts_controller
-    from reflector.utils.audio_waveform import get_audio_waveform
 
     # Cleanup temporary padded S3 files (deferred until after mixdown)
     track_data = _to_dict(ctx.task_output(process_tracks))
@@ -779,12 +777,9 @@ async def detect_topics(input: PipelineInput, ctx: Context) -> TopicsResult:
     target_language = track_data.get("target_language", "en")
 
     from reflector.db.transcripts import TranscriptTopic, transcripts_controller
-    from reflector.pipelines import topic_processing
     from reflector.processors.types import (
         TitleSummaryWithId as TitleSummaryWithIdProcessorType,
     )
-    from reflector.processors.types import Transcript as TranscriptType
-    from reflector.processors.types import Word
 
     # Convert word dicts to Word objects
     word_objects = [Word(**w) for w in words]
@@ -850,8 +845,6 @@ async def generate_title(input: PipelineInput, ctx: Context) -> TitleResult:
         TranscriptFinalTitle,
         transcripts_controller,
     )
-    from reflector.pipelines import topic_processing
-    from reflector.processors.types import TitleSummary
 
     topic_objects = [TitleSummary(**t) for t in topics]
 
@@ -913,8 +906,6 @@ async def generate_summary(input: PipelineInput, ctx: Context) -> SummaryResult:
         TranscriptFinalShortSummary,
         transcripts_controller,
     )
-    from reflector.pipelines import topic_processing
-    from reflector.processors.types import TitleSummary
 
     topic_objects = [TitleSummary(**t) for t in topics]
 
@@ -1100,8 +1091,6 @@ async def post_zulip(input: PipelineInput, ctx: Context) -> ZulipResult:
         input.transcript_id, "post_zulip", "in_progress", ctx.workflow_run_id
     )
 
-    from reflector.settings import settings
-
     if not settings.ZULIP_REALM:
         logger.info("[Hatchet] post_zulip skipped (Zulip not configured)")
         await emit_progress_async(
@@ -1109,8 +1098,6 @@ async def post_zulip(input: PipelineInput, ctx: Context) -> ZulipResult:
         )
         return ZulipResult(zulip_message_id=None, skipped=True)
 
-    from reflector.zulip import post_transcript_notification
-
     async with fresh_db_connection():
         from reflector.db.transcripts import transcripts_controller
 
@@ -1155,8 +1142,6 @@ async def send_webhook(input: PipelineInput, ctx: Context) -> WebhookResult:
         transcript = await transcripts_controller.get_by_id(input.transcript_id)
 
         if room and room.webhook_url and transcript:
-            import httpx
-
             webhook_payload = {
                 "event": "transcript.completed",
                 "transcript_id": input.transcript_id,

server/reflector/services/transcript_process.py

@@ -15,7 +15,8 @@ from hatchet_sdk.clients.rest.exceptions import ApiException
 from hatchet_sdk.clients.rest.models import V1TaskStatus
 
 from reflector.db.recordings import recordings_controller
-from reflector.db.transcripts import Transcript
+from reflector.db.rooms import rooms_controller
+from reflector.db.transcripts import Transcript, transcripts_controller
 from reflector.hatchet.client import HatchetClientManager
 from reflector.logger import logger
 from reflector.pipelines.main_file_pipeline import task_pipeline_file_process
@@ -180,9 +181,6 @@ async def dispatch_transcript_processing(
 
     Returns AsyncResult for Celery tasks, None for Hatchet workflows.
     """
-    from reflector.db.rooms import rooms_controller
-    from reflector.db.transcripts import transcripts_controller
-
     if isinstance(config, MultitrackProcessingConfig):
         # Check if room has use_hatchet=True (overrides env vars)
         room_forces_hatchet = False

server/reflector/tools/process_transcript.py

@@ -17,7 +17,9 @@ from typing import Callable
 from celery.result import AsyncResult
 from hatchet_sdk.clients.rest.models import V1TaskStatus
 
+from reflector.db import get_database
 from reflector.db.transcripts import Transcript, transcripts_controller
+from reflector.hatchet.client import HatchetClientManager
 from reflector.services.transcript_process import (
     FileProcessingConfig,
     MultitrackProcessingConfig,
@@ -55,8 +57,6 @@ async def process_transcript(
         sync: If True, wait for task completion. If False, dispatch and exit.
         force: If True, cancel old workflow and start new (latest code). If False, replay failed workflow.
     """
-    from reflector.db import get_database
-
     database = get_database()
     await database.connect()
 
@@ -96,8 +96,6 @@ async def process_transcript(
         if result is None:
             # Hatchet workflow dispatched
             if sync:
-                from reflector.hatchet.client import HatchetClientManager
-
                 # Re-fetch transcript to get workflow_run_id
                 transcript = await transcripts_controller.get_by_id(transcript_id)
                 if not transcript or not transcript.workflow_run_id:

server/reflector/worker/process.py

@@ -24,6 +24,7 @@ from reflector.db.transcripts import (
     SourceKind,
     transcripts_controller,
 )
+from reflector.hatchet.client import HatchetClientManager
 from reflector.pipelines.main_file_pipeline import task_pipeline_file_process
 from reflector.pipelines.main_live_pipeline import asynctask
 from reflector.pipelines.main_multitrack_pipeline import (
@@ -298,8 +299,6 @@ async def _process_multitrack_recording_inner(
         )
 
     if use_hatchet:
-        from reflector.hatchet.client import HatchetClientManager  # noqa: PLC0415
-
         workflow_id = await HatchetClientManager.start_workflow(
             workflow_name="DiarizationPipeline",
             input_data={

server/reflector/zulip.py

@@ -3,7 +3,8 @@ from urllib.parse import urlparse
 
 import httpx
 
-from reflector.db.transcripts import Transcript
+from reflector.db.rooms import rooms_controller
+from reflector.db.transcripts import Transcript, transcripts_controller
 from reflector.settings import settings
 
 
@@ -113,6 +114,48 @@ def get_zulip_message(transcript: Transcript, include_topics: bool):
     return message
 
 
+async def post_transcript_notification(transcript: Transcript) -> int | None:
+    """Post or update transcript notification in Zulip.
+
+    Uses transcript.room_id directly (Hatchet flow).
+    Celery's pipeline_post_to_zulip uses recording→meeting→room path instead.
+    """
+    if not transcript.room_id:
+        return None
+
+    room = await rooms_controller.get_by_id(transcript.room_id)
+    if not room or not room.zulip_stream or not room.zulip_auto_post:
+        return None
+
+    message = get_zulip_message(transcript=transcript, include_topics=True)
+    message_updated = False
+
+    if transcript.zulip_message_id:
+        try:
+            await update_zulip_message(
+                transcript.zulip_message_id,
+                room.zulip_stream,
+                room.zulip_topic,
+                message,
+            )
+            message_updated = True
+        except Exception:
+            pass
+
+    if not message_updated:
+        response = await send_message_to_zulip(
+            room.zulip_stream, room.zulip_topic, message
+        )
+        message_id = response.get("id")
+        if message_id:
+            await transcripts_controller.update(
+                transcript, {"zulip_message_id": message_id}
+            )
+        return message_id
+
+    return transcript.zulip_message_id
+
+
 def extract_domain(url: str) -> str:
     return urlparse(url).netloc