♻️(summary) clean up code and unify logging in preparation for testing

Refactor the summary service to better separate concerns, making components
easier to isolate and test. Unify logging logic to ensure consistent
behavior and reduce duplication across the service layer. These changes
set up the codebase for granular testing.

src/summary/summary/core/analytics.py

@@ -112,19 +112,16 @@ class MetadataManager:
         if self._is_disabled or self.has_task_id(task_id):
             return
 
-        initial_metadata = {
-            "start_time": time.time(),
-            "asr_model": settings.whisperx_asr_model,
-            "retries": 0,
-        }
-
         _, filename, email, _, received_at, *_ = task_args
 
+        start_time = time.time()
         initial_metadata = {
-            **initial_metadata,
+            "start_time": start_time,
+            "asr_model": settings.whisperx_asr_model,
+            "retries": 0,
             "filename": filename,
             "email": email,
-            "queuing_time": round(initial_metadata["start_time"] - received_at, 2),
+            "queuing_time": round(start_time - received_at, 2),
         }
 
         self._save_metadata(task_id, initial_metadata)

src/summary/summary/core/celery_worker.py

@@ -10,9 +10,7 @@ import openai
 import sentry_sdk
 from celery import Celery, signals
 from celery.utils.log import get_task_logger
-from requests import Session, exceptions
+from requests import exceptions
-from requests.adapters import HTTPAdapter
-from urllib3.util import Retry
 
 from summary.core.analytics import MetadataManager, get_analytics
 from summary.core.config import get_settings
@@ -30,6 +28,7 @@ from summary.core.prompt import (
     PROMPT_USER_PART,
 )
 from summary.core.transcript_formatter import TranscriptFormatter
+from summary.core.webhook_service import submit_content
 
 settings = get_settings()
 analytics = get_analytics()
@@ -56,103 +55,17 @@ if settings.sentry_dsn and settings.sentry_is_enabled:
         sentry_sdk.init(dsn=settings.sentry_dsn, enable_tracing=True)
 
 
-file_service = FileService(logger=logger)
+file_service = FileService()
 
 
-def create_retry_session():
+def transcribe_audio(task_id, filename, language):
-    """Create an HTTP session configured with retry logic."""
+    """Transcribe an audio file using WhisperX.
-    session = Session()
-    retries = Retry(
-        total=settings.webhook_max_retries,
-        backoff_factor=settings.webhook_backoff_factor,
-        status_forcelist=settings.webhook_status_forcelist,
-        allowed_methods={"POST"},
-    )
-    session.mount("https://", HTTPAdapter(max_retries=retries))
-    return session
 
+    Downloads the audio from MinIO, sends it to WhisperX for transcription,
+    and tracks metadata throughout the process.
 
-def format_actions(llm_output: dict) -> str:
+    Returns the transcription object, or None if the file could not be retrieved.
-    """Format the actions from the LLM output into a markdown list.
-
-    fomat:
-    - [ ] Action title Assignée à : assignee1, assignee2, Échéance : due_date
     """
-    lines = []
-    for action in llm_output.get("actions", []):
-        title = action.get("title", "").strip()
-        assignees = ", ".join(action.get("assignees", [])) or "-"
-        due_date = action.get("due_date") or "-"
-        line = f"- [ ] {title} Assignée à : {assignees}, Échéance : {due_date}"
-        lines.append(line)
-    if lines:
-        return "### Prochaines étapes\n\n" + "\n".join(lines)
-    return ""
-
-
-def post_with_retries(url, data):
-    """Send POST request with automatic retries."""
-    session = create_retry_session()
-    session.headers.update(
-        {"Authorization": f"Bearer {settings.webhook_api_token.get_secret_value()}"}
-    )
-    try:
-        response = session.post(url, json=data)
-        response.raise_for_status()
-        return response
-    finally:
-        session.close()
-
-
-@celery.task(
-    bind=True,
-    autoretry_for=[exceptions.HTTPError],
-    max_retries=settings.celery_max_retries,
-    queue=settings.transcribe_queue,
-)
-def process_audio_transcribe_summarize_v2(
-    self,
-    owner_id: str,
-    filename: str,
-    email: str,
-    sub: str,
-    received_at: float,
-    room: Optional[str],
-    recording_date: Optional[str],
-    recording_time: Optional[str],
-    language: Optional[str],
-    download_link: Optional[str],
-    context_language: Optional[str] = None,
-):
-    """Process an audio file by transcribing it and generating a summary.
-
-    This Celery task performs the following operations:
-    1. Retrieves the audio file from MinIO storage
-    2. Transcribes the audio using WhisperX model
-    3. Sends the results via webhook
-
-    Args:
-        self: Celery task instance (passed on with bind=True)
-        owner_id: Unique identifier of the recording owner.
-        filename: Name of the audio file in MinIO storage.
-        email: Email address of the recording owner.
-        sub: OIDC subject identifier of the recording owner.
-        received_at: Unix timestamp when the recording was received.
-        room: room name where the recording took place.
-        recording_date: Date of the recording (localized display string).
-        recording_time: Time of the recording (localized display string).
-        language: ISO 639-1 language code for transcription.
-        download_link: URL to download the original recording.
-        context_language: ISO 639-1 language code of the meeting summary context text.
-    """
-    logger.info(
-        "Notification received | Owner: %s | Room: %s",
-        owner_id,
-        room,
-    )
-
-    task_id = self.request.id
-
     logger.info("Initiating WhisperX client")
     whisperx_client = openai.OpenAI(
         api_key=settings.whisperx_api_key.get_secret_value(),
@@ -162,9 +75,7 @@ def process_audio_transcribe_summarize_v2(
 
     # Transcription
     try:
-        with (
+        with file_service.prepare_audio_file(filename) as (audio_file, metadata):
-            file_service.prepare_audio_file(filename) as (audio_file, metadata),
-        ):
             metadata_manager.track(task_id, {"audio_length": metadata["duration"]})
 
             if language is None:
@@ -195,16 +106,32 @@ def process_audio_transcribe_summarize_v2(
 
     except FileServiceException:
         logger.exception("Unexpected error for filename: %s", filename)
-        return
+        return None
 
     metadata_manager.track_transcription_metadata(task_id, transcription)
+    return transcription
 
-    # For locale of context, use in decreasing priority context_language,
+
-    # language (of meeting), default context language
+def format_transcript(
+    transcription,
+    context_language,
+    language,
+    room,
+    recording_date,
+    recording_time,
+    download_link,
+):
+    """Format a transcription into readable content with a title.
+
+    Resolves the locale from context_language / language, then uses
+    TranscriptFormatter to produce markdown content and a title.
+
+    Returns a (content, title) tuple.
+    """
     locale = get_locale(context_language, language)
     formatter = TranscriptFormatter(locale)
 
-    content, title = formatter.format(
+    return formatter.format(
         transcription,
         room=room,
         recording_date=recording_date,
@@ -212,32 +139,90 @@ def process_audio_transcribe_summarize_v2(
         download_link=download_link,
     )
 
-    data = {
-        "title": title,
-        "content": content,
-        "email": email,
-        "sub": sub,
-    }
 
-    logger.debug("Submitting webhook to %s", settings.webhook_url)
+def format_actions(llm_output: dict) -> str:
-    logger.debug("Request payload: %s", json.dumps(data, indent=2))
+    """Format the actions from the LLM output into a markdown list.
 
-    response = post_with_retries(settings.webhook_url, data)
+    fomat:
+    - [ ] Action title Assignée à : assignee1, assignee2, Échéance : due_date
+    """
+    lines = []
+    for action in llm_output.get("actions", []):
+        title = action.get("title", "").strip()
+        assignees = ", ".join(action.get("assignees", [])) or "-"
+        due_date = action.get("due_date") or "-"
+        line = f"- [ ] {title} Assignée à : {assignees}, Échéance : {due_date}"
+        lines.append(line)
+    if lines:
+        return "### Prochaines étapes\n\n" + "\n".join(lines)
+    return ""
 
-    try:
-        response_data = response.json()
-        document_id = response_data.get("id", "N/A")
-    except (json.JSONDecodeError, AttributeError):
-        document_id = "Unable to parse response"
-        response_data = response.text
 
+@celery.task(
+    bind=True,
+    autoretry_for=[exceptions.HTTPError],
+    max_retries=settings.celery_max_retries,
+    queue=settings.transcribe_queue,
+)
+def process_audio_transcribe_summarize_v2(
+    self,
+    owner_id: str,
+    filename: str,
+    email: str,
+    sub: str,
+    received_at: float,
+    room: Optional[str],
+    recording_date: Optional[str],
+    recording_time: Optional[str],
+    language: Optional[str],
+    download_link: Optional[str],
+    context_language: Optional[str] = None,
+):
+    """Process an audio file by transcribing it and generating a summary.
+
+    This Celery task orchestrates:
+    1. Audio transcription via WhisperX
+    2. Transcript formatting
+    3. Webhook submission
+    4. Conditional summarization queuing
+
+    Args:
+        self: Celery task instance (passed on with bind=True)
+        owner_id: Unique identifier of the recording owner.
+        filename: Name of the audio file in MinIO storage.
+        email: Email address of the recording owner.
+        sub: OIDC subject identifier of the recording owner.
+        received_at: Unix timestamp when the recording was received.
+        room: room name where the recording took place.
+        recording_date: Date of the recording (localized display string).
+        recording_time: Time of the recording (localized display string).
+        language: ISO 639-1 language code for transcription.
+        download_link: URL to download the original recording.
+        context_language: ISO 639-1 language code of the meeting summary context text.
+    """
     logger.info(
-        "Webhook success | Document %s submitted (HTTP %s)",
+        "Notification received | Owner: %s | Room: %s",
-        document_id,
+        owner_id,
-        response.status_code,
+        room,
     )
-    logger.debug("Full response: %s", response_data)
 
+    task_id = self.request.id
+
+    transcription = transcribe_audio(task_id, filename, language)
+    if transcription is None:
+        return
+
+    content, title = format_transcript(
+        transcription,
+        context_language,
+        language,
+        room,
+        recording_date,
+        recording_time,
+        download_link,
+    )
+
+    submit_content(content, title, email, sub)
     metadata_manager.capture(task_id, settings.posthog_event_success)
 
     # LLM Summarization
@@ -306,12 +291,11 @@ def summarize_transcription(
     # a singleton client. This is a performance trade-off we accept to ensure per-user
     # privacy controls in observability traces.
     llm_observability = LLMObservability(
-        logger=logger,
         user_has_tracing_consent=user_has_tracing_consent,
         session_id=self.request.id,
         user_id=owner_id,
     )
-    llm_service = LLMService(llm_observability=llm_observability, logger=logger)
+    llm_service = LLMService(llm_observability=llm_observability)
 
     tldr = llm_service.call(PROMPT_SYSTEM_TLDR, transcript, name="tldr")
 
@@ -354,20 +338,9 @@ def summarize_transcription(
     logger.info("Summary cleaned")
 
     summary = tldr + "\n\n" + cleaned_summary + "\n\n" + next_steps
+    summary_title = settings.summary_title_template.format(title=title)
 
-    data = {
+    submit_content(summary, summary_title, email, sub)
-        "title": settings.summary_title_template.format(title=title),
-        "content": summary,
-        "email": email,
-        "sub": sub,
-    }
-
-    logger.debug("Submitting webhook to %s", settings.webhook_url)
-
-    response = post_with_retries(settings.webhook_url, data)
-
-    logger.info("Webhook submitted successfully. Status: %s", response.status_code)
-    logger.debug("Response body: %s", response.text)
 
     llm_observability.flush()
     logger.debug("LLM observability flushed")

src/summary/summary/core/file_service.py

@@ -1,5 +1,6 @@
 """File service to encapsulate files' manipulations."""
 
+import logging
 import os
 import subprocess
 import tempfile
@@ -15,6 +16,9 @@ from summary.core.config import get_settings
 settings = get_settings()
 
 
+logger = logging.getLogger(__name__)
+
+
 class FileServiceException(Exception):
     """Base exception for file service operations."""
 
@@ -24,10 +28,8 @@ class FileServiceException(Exception):
 class FileService:
     """Service for downloading and preparing files from MinIO storage."""
 
-    def __init__(self, logger):
+    def __init__(self):
         """Initialize FileService with MinIO client and configuration."""
-        self._logger = logger
-
         endpoint = (
             settings.aws_s3_endpoint_url.removeprefix("https://")
             .removeprefix("http://")
@@ -53,16 +55,16 @@ class FileService:
         The file is downloaded to a temporary location for local manipulation
         such as validation, conversion, or processing before being used.
         """
-        self._logger.info("Download recording | object_key: %s", remote_object_key)
+        logger.info("Download recording | object_key: %s", remote_object_key)
 
         if not remote_object_key:
-            self._logger.warning("Invalid object_key '%s'", remote_object_key)
+            logger.warning("Invalid object_key '%s'", remote_object_key)
             raise ValueError("Invalid object_key")
 
         extension = Path(remote_object_key).suffix.lower()
 
         if extension not in self._allowed_extensions:
-            self._logger.warning("Invalid file extension '%s'", extension)
+            logger.warning("Invalid file extension '%s'", extension)
             raise ValueError(f"Invalid file extension '{extension}'")
 
         response = None
@@ -81,8 +83,8 @@ class FileService:
                 tmp.flush()
                 local_path = Path(tmp.name)
 
-                self._logger.info("Recording successfully downloaded")
+                logger.info("Recording successfully downloaded")
-                self._logger.debug("Recording local file path: %s", local_path)
+                logger.debug("Recording local file path: %s", local_path)
 
                 return local_path
 
@@ -100,7 +102,7 @@ class FileService:
         file_metadata = mutagen.File(local_path).info
         duration = file_metadata.length
 
-        self._logger.info(
+        logger.info(
             "Recording file duration: %.2f seconds",
             duration,
         )
@@ -109,14 +111,14 @@ class FileService:
             error_msg = "Recording too long. Limit is %.2fs seconds" % (
                 self._max_duration,
             )
-            self._logger.error(error_msg)
+            logger.error(error_msg)
             raise ValueError(error_msg)
 
         return duration
 
     def _extract_audio_from_video(self, video_path: Path) -> Path:
         """Extract audio from video file (e.g., MP4) and save as audio file."""
-        self._logger.info("Extracting audio from video file: %s", video_path)
+        logger.info("Extracting audio from video file: %s", video_path)
 
         with tempfile.NamedTemporaryFile(
             suffix=".m4a", delete=False, prefix="audio_extract_"
@@ -140,16 +142,16 @@ class FileService:
                 command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True
             )
 
-            self._logger.info("Audio successfully extracted to: %s", output_path)
+            logger.info("Audio successfully extracted to: %s", output_path)
             return output_path
 
         except FileNotFoundError as e:
-            self._logger.error("ffmpeg not found. Please install ffmpeg.")
+            logger.error("ffmpeg not found. Please install ffmpeg.")
             if output_path.exists():
                 os.remove(output_path)
             raise RuntimeError("ffmpeg is not installed or not in PATH") from e
         except subprocess.CalledProcessError as e:
-            self._logger.error("Audio extraction failed: %s", e.stderr.decode())
+            logger.error("Audio extraction failed: %s", e.stderr.decode())
             if output_path.exists():
                 os.remove(output_path)
             raise RuntimeError("Failed to extract audio.") from e
@@ -173,7 +175,7 @@ class FileService:
             extension = downloaded_path.suffix.lower()
 
             if extension in settings.recording_video_extensions:
-                self._logger.info("Video file detected, extracting audio...")
+                logger.info("Video file detected, extracting audio...")
                 extracted_audio_path = self._extract_audio_from_video(downloaded_path)
                 processed_path = extracted_audio_path
             else:
@@ -194,8 +196,6 @@ class FileService:
 
                 try:
                     os.remove(path)
-                    self._logger.debug("Temporary file removed: %s", path)
+                    logger.debug("Temporary file removed: %s", path)
                 except OSError as e:
-                    self._logger.warning(
+                    logger.warning("Failed to remove temporary file %s: %s", path, e)
-                        "Failed to remove temporary file %s: %s", path, e
-                    )

src/summary/summary/core/llm_service.py

@@ -1,5 +1,6 @@
 """LLM service to encapsulate LLM's calls."""
 
+import logging
 from typing import Any, Mapping, Optional
 
 import openai
@@ -10,6 +11,9 @@ from summary.core.config import get_settings
 settings = get_settings()
 
 
+logger = logging.getLogger(__name__)
+
+
 class LLMObservability:
     """Manage observability and tracing for LLM calls using Langfuse.
 
@@ -21,13 +25,11 @@ class LLMObservability:
 
     def __init__(
         self,
-        logger,
         session_id: str,
         user_id: str,
         user_has_tracing_consent: bool = False,
     ):
         """Initialize the LLMObservability client."""
-        self._logger = logger
         self._observability_client: Optional[Langfuse] = None
         self.session_id = session_id
         self.user_id = user_id
@@ -75,7 +77,7 @@ class LLMObservability:
         }
 
         if not self.is_enabled:
-            self._logger.debug("Using regular OpenAI client (observability disabled)")
+            logger.debug("Using regular OpenAI client (observability disabled)")
             return openai.OpenAI(**base_args)
 
         # Langfuse's OpenAI wrapper is imported here to avoid triggering client
@@ -83,7 +85,7 @@ class LLMObservability:
         # is missing. Conditional import ensures Langfuse only initializes when enabled.
         from langfuse.openai import openai as langfuse_openai  # noqa: PLC0415
 
-        self._logger.debug("Using LangfuseOpenAI client (observability enabled)")
+        logger.debug("Using LangfuseOpenAI client (observability enabled)")
         return langfuse_openai.OpenAI(**base_args)
 
     def flush(self):
@@ -99,11 +101,10 @@ class LLMException(Exception):
 class LLMService:
     """Service for performing calls to the LLM configured in the settings."""
 
-    def __init__(self, llm_observability, logger):
+    def __init__(self, llm_observability):
         """Init the LLMService once."""
         self._client = llm_observability.get_openai_client()
         self._observability = llm_observability
-        self._logger = logger
 
     def call(
         self,
@@ -140,5 +141,5 @@ class LLMService:
             return response.choices[0].message.content
 
         except Exception as e:
-            self._logger.exception("LLM call failed: %s", e)
+            logger.exception("LLM call failed: %s", e)
             raise LLMException(f"LLM call failed: {e}") from e

src/summary/summary/core/webhook_service.py

@@ -0,0 +1,73 @@
+"""Service for delivering content to external destinations."""
+
+import json
+import logging
+
+from requests import Session
+from requests.adapters import HTTPAdapter
+from urllib3.util import Retry
+
+from summary.core.config import get_settings
+
+settings = get_settings()
+
+logger = logging.getLogger(__name__)
+
+
+def _create_retry_session():
+    """Create an HTTP session configured with retry logic."""
+    session = Session()
+    retries = Retry(
+        total=settings.webhook_max_retries,
+        backoff_factor=settings.webhook_backoff_factor,
+        status_forcelist=settings.webhook_status_forcelist,
+        allowed_methods={"POST"},
+    )
+    session.mount("https://", HTTPAdapter(max_retries=retries))
+    return session
+
+
+def _post_with_retries(url, data):
+    """Send POST request with automatic retries."""
+    session = _create_retry_session()
+    session.headers.update(
+        {"Authorization": f"Bearer {settings.webhook_api_token.get_secret_value()}"}
+    )
+    try:
+        response = session.post(url, json=data)
+        response.raise_for_status()
+        return response
+    finally:
+        session.close()
+
+
+def submit_content(content, title, email, sub):
+    """Submit content to the configured webhook destination.
+
+    Builds the payload, sends it with retries, and logs the outcome.
+    """
+    data = {
+        "title": title,
+        "content": content,
+        "email": email,
+        "sub": sub,
+    }
+
+    logger.debug("Submitting to %s", settings.webhook_url)
+    logger.debug("Request payload: %s", json.dumps(data, indent=2))
+
+    response = _post_with_retries(settings.webhook_url, data)
+
+    try:
+        response_data = response.json()
+        document_id = response_data.get("id", "N/A")
+    except (json.JSONDecodeError, AttributeError):
+        document_id = "Unable to parse response"
+        response_data = response.text
+
+    logger.info(
+        "Delivery success | Document %s submitted (HTTP %s)",
+        document_id,
+        response.status_code,
+    )
+    logger.debug("Full response: %s", response_data)