feat: add celery & redis for background data processing

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)