feat: add celery & redis for background data processing

Dockerfile

@@ -0,0 +1,19 @@
+# Use slim to reduce size
+FROM python:3.13-slim
+
+# Prevent Python from buffering stdout
+ENV PYTHONUNBUFFERED=1
+
+# System deps required for psycopg2 + torch
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    libpq-dev \
+    gcc \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY . .
+CMD ["python", "main.py"]

docker-compose.yml

@@ -11,5 +11,34 @@ services:
       - ./server/db/postgres_vol:/var/lib/postgresql/data
       - ./server/db/schema.sql:/docker-entrypoint-initdb.d/schema.sql
 
-volumes:
+  redis:
-  postgres_data:
+    image: redis:7
+    container_name: redis
+    restart: unless-stopped
+    ports:
+      - "6379:6379"
+
+  backend:
+    build: .
+    container_name: flask_backend
+    env_file:
+      - .env
+    ports:
+      - "5000:5000"
+    command: flask --app server.app run --host=0.0.0.0
+    depends_on:
+      - postgres
+      - redis
+
+  worker:
+    build: .
+    container_name: celery_worker
+    env_file:
+      - .env
+    command: >
+      celery -A server.queue.celery_app.celery worker
+      --loglevel=info
+      --pool=solo
+    depends_on:
+      - postgres
+      - redis

requirements.txt

@@ -1,13 +1,17 @@
 beautifulsoup4==4.14.3
+celery==5.6.2
+redis==7.2.1
 Flask==3.1.3
+Flask_Bcrypt==1.0.1
 flask_cors==6.0.2
+Flask_JWT_Extended==4.7.1
 google_api_python_client==2.188.0
 nltk==3.9.2
 numpy==2.4.2
 pandas==3.0.1
 psycopg2==2.9.11
 psycopg2_binary==2.9.11
-python-dotenv==1.2.1
+python-dotenv==1.2.2
 Requests==2.32.5
 sentence_transformers==5.2.2
 torch==2.10.0

server/app.py

@@ -21,6 +21,7 @@ from server.db.database import PostgresConnector
 from server.core.auth import AuthManager
 from server.core.datasets import DatasetManager
 from server.utils import get_request_filters
+from server.queue.tasks import process_dataset
 
 app = Flask(__name__)
 
@@ -129,19 +130,21 @@ def upload_data():
 
         posts_df = pd.read_json(post_file, lines=True, convert_dates=False)
         topics = json.load(topic_file)
-
-        processor = DatasetEnrichment(posts_df, topics)
-        enriched_df = processor.enrich()
         dataset_id = dataset_manager.save_dataset_info(current_user, f"dataset_{current_user}", topics)
-        dataset_manager.save_dataset_content(dataset_id, enriched_df)
+
+        process_dataset.delay(
+            dataset_id,
+            posts_df.to_dict(orient="records"),
+            topics
+        )
 
         return jsonify(
             {
-                "message": "File uploaded successfully",
+                "message": "Dataset queued for processing",
-                "event_count": len(enriched_df),
                 "dataset_id": dataset_id,
+                "status": "processing"
             }
-        ), 200
+        ), 202
     except ValueError as e:
         return jsonify({"error": f"Failed to read JSONL file: {str(e)}"}), 400
     except Exception as e:

server/queue/celery_app.py

@@ -0,0 +1,16 @@
+from celery import Celery
+
+def create_celery():
+    celery = Celery(
+        "ethnograph",
+        broker="redis://redis:6379/0",
+        backend="redis://redis:6379/0",
+    )
+    celery.conf.task_serializer = "json"
+    celery.conf.result_serializer = "json"
+    celery.conf.accept_content = ["json"]
+    return celery
+
+celery = create_celery()
+
+from server.queue import tasks

server/queue/tasks.py

@@ -0,0 +1,19 @@
+import pandas as pd
+
+from server.queue.celery_app import celery
+from server.analysis.enrichment import DatasetEnrichment
+
+@celery.task(bind=True, max_retries=3)
+def process_dataset(self, dataset_id: int, posts: list, topics: dict):
+    from server.db.database import PostgresConnector
+    from server.core.datasets import DatasetManager
+
+    db = PostgresConnector()
+    dataset_manager = DatasetManager(db)
+
+    df = pd.DataFrame(posts)
+
+    processor = DatasetEnrichment(df, topics)
+    enriched_df = processor.enrich()
+
+    dataset_manager.save_dataset_content(dataset_id, enriched_df)