Implement async processing with Celery, Redis, and queue-based email notifications

Dockerfile

@@ -9,9 +9,9 @@ LABEL description="Scraibe: LocalAI-backed transcription and diarization client
                     Sends audio to a LocalAI server running vibevoice.cpp and uses a second LLM for summarization."
 LABEL url="https://git.optimex.systems/admin/scribe"
 
-# Install system dependencies (ffmpeg required)
+# Install system dependencies (ffmpeg, redis)
 RUN apt update -y && \
-    apt install -y --no-install-recommends ffmpeg && \
+    apt install -y --no-install-recommends ffmpeg redis-server && \
     apt clean && \
     rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
 
@@ -31,6 +31,11 @@ ENV SUMMARIZER_MODEL=qwen3-14b
 # Gradio / Web GUI
 ENV GRADIO_SERVER_NAME=0.0.0.0
 
+# Async processing (Celery + Redis)
+ENV CELERY_BROKER_URL=redis://localhost:6379/0
+ENV CELERY_RESULT_BACKEND=redis://localhost:6379/0
+ENV SCRAIBE_UPLOAD_DIR=/tmp/scraibe_uploads
+
 # Copy and install Python dependencies
 COPY requirements.txt /app/src/requirements.txt
 RUN pip install --no-cache-dir -r requirements.txt
@@ -41,9 +46,8 @@ COPY scraibe /app/src/scraibe
 # Copy custom Web GUI assets (header, footer, templates, logos, config)
 COPY misc /app/src/misc
 
-# Expose port (for Web GUI)
+# Expose ports
 EXPOSE 7860
 
-# Run the Web GUI by default (never the CLI)
-# Use python -m scraibe so __main__.py forces web GUI.
-CMD ["python3", "-m", "scraibe"]
+# Run the Web GUI and Celery worker (with Redis) by default
+CMD ["/bin/bash", "-c", "redis-server --daemonize yes && celery -A scraibe.celery_app worker -Q transcription -l info & python3 -m scraibe"]

requirements.txt

@@ -3,3 +3,5 @@ numpy>=1.26.4
 httpx>=0.28.0
 gradio>=5.0.0
 PyYAML>=6.0
+celery[redis]>=5.3.0
+redis>=5.0.0

scraibe/celery_app.py

@@ -0,0 +1,28 @@
+"""
+Celery application for async transcription jobs.
+"""
+
+import os
+from celery import Celery
+
+broker_url = os.getenv("CELERY_BROKER_URL", "redis://redis:6379/0")
+result_backend = os.getenv("CELERY_RESULT_BACKEND", "redis://redis:6379/0")
+
+celery_app = Celery(
+    "scraibe",
+    broker=broker_url,
+    backend=result_backend,
+)
+
+celery_app.conf.update(
+    task_routes={
+        "scraibe.tasks.process_transcription_task": {"queue": "transcription"},
+    },
+    task_serializer="json",
+    result_serializer="json",
+    accept_content=["json"],
+    timezone="UTC",
+    enable_utc=True,
+)
+
+celery_app.autodiscover_tasks(["scraibe.tasks"])

scraibe/tasks.py

@@ -0,0 +1,249 @@
+"""
+Celery tasks for async transcription, diarization, and email notifications.
+"""
+
+import os
+import json
+import logging
+import tempfile
+from datetime import datetime
+
+from .celery_app import celery_app
+from .autotranscript import Scraibe
+from .misc import setup_logging
+from .email_sender import send_email, EmailError
+
+logger = logging.getLogger("scraibe.tasks")
+
+
+def get_queue_position(task_id: str) -> int:
+    """
+    Estimate the job's position in the queue.
+    This is a simple count of ready/started tasks.
+    """
+    try:
+        inspect = celery_app.control.inspect()
+        ready = inspect.active() or {}
+        reserved = inspect.reserved() or {}
+        count = 0
+        for _, tasks in list(ready.values()) + list(reserved.values()):
+            for t in tasks:
+                if t.get("id") == task_id:
+                    break
+                count += 1
+        return max(count + 1, 1)
+    except Exception:
+        return -1
+
+
+def send_initial_email(to: str, queue_pos: int):
+    """
+    Send initial confirmation email with queue position.
+    """
+    subject = "ScrAIbe: Your transcription request has been received"
+    body = (
+        "Hello,\n\n"
+        "We have received your audio file for transcription.\n"
+    )
+
+    if queue_pos > 0:
+        body += f"Your request is currently number {queue_pos} in the queue.\n"
+    else:
+        body += "Your request has been queued for processing.\n"
+
+    body += (
+        "\n"
+        "You will receive an email with your transcript (and summary, if requested) "
+        "once processing is complete.\n\n"
+        "This is an automated message from ScrAIbe.\n"
+    )
+
+    try:
+        send_email(to=to, subject=subject, body=body, attachments=[])
+        logger.info("Initial confirmation email sent to %s", to)
+    except EmailError as e:
+        logger.error("Failed to send initial email to %s: %s", to, e)
+
+
+def send_success_email(
+    to: str,
+    transcript_text: str,
+    summary_text: str,
+    attachments: list,
+    task_id: str,
+):
+    """
+    Send final email with transcript and attachments.
+    """
+    subject = "ScrAIbe: Your transcript is ready"
+
+    body = (
+        "Hello,\n\n"
+        "Your transcription is ready.\n\n"
+        "Please find the transcript and JSON files attached.\n"
+    )
+
+    if summary_text:
+        body += (
+            "\n"
+            "SUMMARY\n"
+            "-------\n"
+            f"{summary_text}\n"
+        )
+
+    body += (
+        "\n"
+        "Job ID: " + str(task_id) + "\n\n"
+        "This is an automated message from ScrAIbe.\n"
+    )
+
+    try:
+        send_email(
+            to=to,
+            subject=subject,
+            body=body,
+            attachments=attachments,
+        )
+        logger.info("Success email sent to %s for job %s", to, task_id)
+    except EmailError as e:
+        logger.error("Failed to send success email to %s for job %s: %s", to, task_id, e)
+
+
+def send_error_email(to: str, error_message: str, task_id: str):
+    """
+    Send error notification email.
+    """
+    subject = "ScrAIbe: Error with your transcription request"
+
+    body = (
+        "Hello,\n\n"
+        "We encountered an error while processing your transcription request.\n\n"
+        f"Details: {error_message}\n\n"
+        "Job ID: " + str(task_id) + "\n\n"
+        "Please contact your administrator if the problem persists.\n\n"
+        "This is an automated message from ScrAIbe.\n"
+    )
+
+    try:
+        send_email(to=to, subject=subject, body=body, attachments=[])
+        logger.info("Error email sent to %s for job %s", to, task_id)
+    except EmailError as e:
+        logger.error("Failed to send error email to %s for job %s: %s", to, task_id, e)
+
+
+@celery_app.task(
+    name="scraibe.tasks.process_transcription_task",
+    bind=True,
+    max_retries=1,
+)
+def process_transcription_task(
+    self,
+    audio_path: str,
+    task_type: str,
+    language: str,
+    num_speakers: int,
+    email_to: str,
+    email_cc: str,
+    include_summary: bool,
+):
+    """
+    Async task: transcribe audio, optionally summarize, then email results.
+    """
+    task_id = self.request.id
+
+    # Ensure logging
+    log_level = os.getenv("LOG_LEVEL", "INFO")
+    setup_logging(level=log_level)
+
+    # 1) Determine queue position and send initial email
+    queue_pos = get_queue_position(task_id)
+    send_initial_email(to=email_to, queue_pos=queue_pos)
+
+    # 2) Initialize Scraibe
+    try:
+        scraibe = Scraibe(verbose=True)
+    except Exception as e:
+        send_error_email(
+            to=email_to,
+            error_message=f"Failed to initialize transcription service: {e}",
+            task_id=task_id,
+        )
+        raise
+
+    try:
+        # 3) Perform transcription
+        if task_type == "transcript_and_summarize":
+            result = scraibe.transcript_and_summarize(
+                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", "")
+            summary_text = result.get("summary", "")
+            segments = result.get("segments", [])
+            raw_result = result.get("raw_result")
+        else:
+            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", "")
+            summary_text = ""
+            segments = result.get("segments", [])
+            raw_result = result.get("raw_result")
+
+        # 4) Prepare files for email
+        attachments = []
+
+        # TXT transcript
+        txt_path = tempfile.mktemp(suffix=".txt")
+        with open(txt_path, "w", encoding="utf-8") as f:
+            f.write(transcript_text)
+        attachments.append(txt_path)
+
+        # JSON with diarization
+        json_data = {
+            "task": task_type,
+            "transcript": transcript_text,
+            "segments": segments,
+            "metadata": {
+                "timestamp": datetime.utcnow().isoformat(),
+                "job_id": task_id,
+            },
+        }
+
+        if summary_text:
+            json_data["summary"] = summary_text
+
+        if raw_result is not None:
+            json_data["raw_result"] = raw_result
+
+        json_path = tempfile.mktemp(suffix=".json")
+        with open(json_path, "w", encoding="utf-8") as f:
+            json.dump(json_data, f, indent=2, ensure_ascii=False)
+        attachments.append(json_path)
+
+        # 5) Send success email
+        send_success_email(
+            to=email_to,
+            transcript_text=transcript_text,
+            summary_text=summary_text if include_summary else "",
+            attachments=attachments,
+            task_id=task_id,
+        )
+
+        logger.info("Job %s completed successfully.", task_id)
+
+    except Exception as e:
+        logger.error("Error processing job %s: %s", task_id, e, exc_info=True)
+        send_error_email(
+            to=email_to,
+            error_message=str(e),
+            task_id=task_id,
+        )
+        raise e

scraibe/webui.py

@@ -1,26 +1,28 @@
 """
-ScrAIbe Web GUI (Gradio)
-------------------------
+ScrAIbe Web GUI (Gradio) - Async Mode
+-------------------------------------
 
 Runs the Web GUI that:
 - Accepts audio uploads
-- Sends audio to LocalAI for transcription + diarization
-- Optionally sends transcript to a second LLM for summarization
-- Returns transcript (and summary) in the browser
-- Optionally emails transcript files (TXT + JSON)
+- Enqueues transcription jobs asynchronously via Celery
+- Backend worker:
+  - Transcribes (with diarization)
+  - Optionally summarizes
+  - Emails the user:
+    - Immediately: confirmation + queue position
+    - On success: transcript + JSON (+ summary if requested)
+    - On error: error details
 
 This is the default entrypoint when running in Docker.
 """
 
 import os
-import json
 import logging
-import tempfile
+import shutil
 from datetime import datetime
 
 import gradio as gr
 
-from .autotranscript import Scraibe
 from .misc import setup_logging
 
 logger = logging.getLogger("scraibe.webui")
@@ -45,7 +47,7 @@ def load_config():
 
 def create_app():
     """
-    Create and launch the Gradio Web GUI.
+    Create and launch the Gradio Web GUI (async mode).
     """
 
     # Logging
@@ -57,185 +59,11 @@ def create_app():
     layout_cfg = config.get("layout", {})
     launch_cfg = config.get("launch", {})
 
-    logger.info("Starting ScrAIbe Web GUI.")
+    logger.info("Starting ScrAIbe Web GUI (async mode).")
 
-    # Initialize Scraibe (LocalAI-backed)
-    # If LocalAI is unreachable at startup, still launch the UI
-    # and let individual transcription calls fail with a clear message.
-    scraibe = None
-    try:
-        scraibe = Scraibe(verbose=True)
-    except Exception as e:
-        logger.warning(
-            "Failed to initialize Scraibe at startup (LocalAI may be down): %s. "
-            "Web GUI will start; transcription will fail until LocalAI is reachable.",
-            e,
-        )
-
-    # Helper: run transcription via LocalAI API
-    def run_transcribe(
-        audio_path,
-        task,
-        language,
-        num_speakers,
-        send_email_flag,
-        email_to,
-        email_cc,
-        email_subject,
-    ):
-        if not audio_path:
-            raise ValueError("No audio file provided.")
-
-        email_status = ""
-        attachments = []
-
-        # Ensure we use rich export mode (for JSON with diarization)
-        try:
-            if task == "transcript_and_summarize":
-                result = scraibe.transcript_and_summarize(
-                    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", "")
-                summary_text = result.get("summary", "")
-                segments = result.get("segments", [])
-                raw_result = result.get("raw_result")
-
-                # Save as .md (transcript + summary)
-                md_path = tempfile.mktemp(suffix=".md")
-                with open(md_path, "w", encoding="utf-8") as f:
-                    f.write("# Transcript\n\n")
-                    f.write(transcript_text)
-                    f.write("\n\n# Summary\n\n")
-                    f.write(summary_text)
-
-                # Save as .txt (plain transcript)
-                txt_path = tempfile.mktemp(suffix=".txt")
-                with open(txt_path, "w", encoding="utf-8") as f:
-                    f.write(transcript_text)
-
-                # Save as .json (diarization + transcript + summary)
-                json_data = {
-                    "task": "transcript_and_summarize",
-                    "transcript": transcript_text,
-                    "summary": summary_text,
-                    "segments": segments,
-                    "metadata": {
-                        "timestamp": datetime.utcnow().isoformat()
-                    },
-                }
-                if raw_result is not None:
-                    json_data["raw_result"] = raw_result
-
-                json_path = tempfile.mktemp(suffix=".json")
-                with open(json_path, "w", encoding="utf-8") as f:
-                    json.dump(json_data, f, indent=2, ensure_ascii=False)
-
-                # Prepare attachments for email
-                if send_email_flag:
-                    attachments = [txt_path, json_path]
-
-                status_msg = "Transcription and summarization completed."
-
-            else:
-                # transcribe only (with diarization)
-                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", [])
-                raw_result = result.get("raw_result")
-
-                # Save as .txt (plain transcript)
-                txt_path = tempfile.mktemp(suffix=".txt")
-                with open(txt_path, "w", encoding="utf-8") as f:
-                    f.write(transcript_text)
-
-                # Save as .json (diarization + transcript)
-                json_data = {
-                    "task": "transcribe",
-                    "transcript": transcript_text,
-                    "segments": segments,
-                    "metadata": {
-                        "timestamp": datetime.utcnow().isoformat()
-                    },
-                }
-                if raw_result is not None:
-                    json_data["raw_result"] = raw_result
-
-                json_path = tempfile.mktemp(suffix=".json")
-                with open(json_path, "w", encoding="utf-8") as f:
-                    json.dump(json_data, f, indent=2, ensure_ascii=False)
-
-                # Prepare attachments for email
-                if send_email_flag:
-                    attachments = [txt_path, json_path]
-
-                status_msg = "Transcription completed."
-
-        except Exception as e:
-            logger.error("Error during transcription: %s", e)
-            return (
-                "",
-                "",
-                None,
-                f"Error: {e}",
-                "",
-            )
-
-        # Handle email after successful transcription
-        if send_email_flag and attachments:
-            try:
-                from .email_sender import send_email, EmailError
-            except ImportError:
-                email_status = "Email feature unavailable (email_sender not found)."
-            else:
-                to = (email_to or "").strip()
-                cc = (email_cc or "").strip()
-                subject = (email_subject or "").strip()
-
-                if not to:
-                    email_status = "Email not sent: 'To' address is empty."
-                else:
-                    if not subject:
-                        subject = f"ScrAIbe Transcript - {datetime.utcnow().strftime('%Y-%m-%d %H:%M UTC')}"
-
-                    body = (
-                        "Please find the transcription files attached.\n"
-                        "This message was generated by ScrAIbe.\n"
-                    )
-
-                    try:
-                        send_email(
-                            to=to,
-                            cc=cc or None,
-                            subject=subject,
-                            body=body,
-                            attachments=attachments,
-                        )
-                        email_status = "Transcript files sent via email."
-                    except EmailError as e:
-                        email_status = f"Email failed: {e}"
-                    except Exception as e:
-                        email_status = f"Email failed: {e}"
-
-        # Use md_path for file_output in transcript_and_summarize, else txt_path
-        file_path = md_path if task == "transcript_and_summarize" else txt_path
-
-        return (
-            transcript_text,
-            summary_text if task == "transcript_and_summarize" else "",
-            file_path,
-            status_msg,
-            email_status,
-        )
+    # Ensure upload directory exists
+    upload_dir = os.getenv("SCRAIBE_UPLOAD_DIR", "/tmp/scraibe_uploads")
+    os.makedirs(upload_dir, exist_ok=True)
 
     # Load header/footer HTML if present
     header_path = layout_cfg.get("header", "/app/src/misc/header.html")
@@ -253,7 +81,6 @@ def create_app():
             footer_html = f.read()
 
     # Build Gradio interface
-    # In Gradio 6.0+, css must be passed to launch(), not Blocks()
     with gr.Blocks(
         title="A.P.Strom Transcription",
     ) as app:
@@ -289,57 +116,25 @@ def create_app():
                         precision=0,
                     )
 
-                # Email options
-                send_email_checkbox = gr.Checkbox(
-                    label="Send transcript files via email"
+                # Email is required in async mode
+                email_to = gr.Textbox(
+                    label="Your email address (required)",
+                    placeholder="e.g. your.name@example.com",
                 )
 
-                with gr.Group(visible=False) as email_group:
-                    email_to = gr.Textbox(
-                        label="To (comma-separated)",
-                        placeholder="e.g. name@example.com",
-                    )
                 email_cc = gr.Textbox(
                     label="CC (optional, comma-separated)",
                     placeholder="e.g. manager@example.com",
                 )
-                    email_subject = gr.Textbox(
-                        label="Subject (optional)",
-                        placeholder="Default: ScrAIbe Transcript - <date>",
-                    )
 
-                send_email_checkbox.change(
-                    fn=lambda v: gr.update(visible=v),
-                    inputs=[send_email_checkbox],
-                    outputs=[email_group],
-                )
-
-                transcribe_btn = gr.Button("Start", variant="primary")
+                submit_btn = gr.Button("Submit for transcription", variant="primary")
 
             with gr.Column(scale=3):
-                output_text = gr.Textbox(
-                    label="Transcript",
-                    lines=10,
-                    interactive=False,
-                )
-                summary_text = gr.Textbox(
-                    label="Summary",
-                    lines=10,
-                    interactive=False,
-                    visible=False,
-                )
-                file_output = gr.File(
-                    label="Download transcript/summary",
-                )
                 status_text = gr.Textbox(
                     label="Status",
+                    lines=6,
                     interactive=False,
                 )
-                email_status_text = gr.Textbox(
-                    label="Email status",
-                    interactive=False,
-                    visible=True,
-                )
 
         # Footer
         if footer_html:
@@ -348,73 +143,83 @@ def create_app():
         # Events
 
         def on_task_change(value):
-            show_summary = (value == "transcript_and_summarize")
-            return gr.update(visible=show_summary)
+            # No special UI changes needed; both modes handled in backend
+            return
 
         task_choice.change(
             fn=on_task_change,
             inputs=[task_choice],
-            outputs=[summary_text],
+            outputs=[],
         )
 
-        def on_transcribe(
+        def on_submit(
             audio,
             task,
             language,
             num_speakers,
-            send_email_flag,
             email_to_val,
             email_cc_val,
-            email_subject_val,
         ):
             if not audio:
+                return "Please upload or record audio."
+
+            email_to_val = (email_to_val or "").strip()
+            if not email_to_val:
+                return "Please enter your email address."
+
+            # Copy uploaded file to a stable location
+            try:
+                ext = os.path.splitext(audio)[1] or ".wav"
+                ts = datetime.utcnow().strftime("%Y%m%d%H%M%S%f")
+                new_name = f"upload_{ts}{ext}"
+                dest_path = os.path.join(upload_dir, new_name)
+                shutil.copy2(audio, dest_path)
+            except Exception as e:
+                logger.error("Error copying uploaded file: %s", e)
+                return f"Error saving your file: {e}"
+
+            # Import Celery task
+            try:
+                from .tasks import process_transcription_task
+            except ImportError:
                 return (
-                    "",
-                    "",
-                    None,
-                    "Please upload or record audio.",
-                    "",
+                    "Error: async processing is not available (Celery not configured)."
                 )
 
-            transcript, summary, file_path, status_msg, email_status = run_transcribe(
-                audio_path=audio,
-                task=task,
-                language=language,
-                num_speakers=num_speakers,
-                send_email_flag=bool(send_email_flag),
+            # Enqueue transcription job
+            try:
+                task_result = process_transcription_task.delay(
+                    audio_path=dest_path,
+                    task_type=task,
+                    language=language or None,
+                    num_speakers=int(num_speakers) if num_speakers else None,
                     email_to=email_to_val,
-                email_cc=email_cc_val,
-                email_subject=email_subject_val,
+                    email_cc=email_cc_val or None,
+                    include_summary=(task == "transcript_and_summarize"),
                 )
+            except Exception as e:
+                logger.error("Error enqueuing job: %s", e)
+                return f"Error submitting your file: {e}"
 
-            show_summary = bool(summary)
             return (
-                transcript,
-                summary,
-                file_path if file_path else None,
-                status_msg,
-                email_status,
+                "Your audio file has been received and added to the queue.\n"
+                "We have sent a confirmation email to you.\n"
+                "You will receive another email with your transcript (and summary, if requested) "
+                "once processing is complete.\n"
+                f"Job ID: {task_result.id}"
             )
 
-        transcribe_btn.click(
-            fn=on_transcribe,
+        submit_btn.click(
+            fn=on_submit,
             inputs=[
                 audio_input,
                 task_choice,
                 language_input,
                 num_speakers_input,
-                send_email_checkbox,
                 email_to,
                 email_cc,
-                email_subject,
-            ],
-            outputs=[
-                output_text,
-                summary_text,
-                file_output,
-                status_text,
-                email_status_text,
             ],
+            outputs=[status_text],
         )
 
     # Launch options