Merge pull request 'Implement job queue for asynchronous NLP' (#6) from feat/implement-job-queue into main

Reviewed-on: #6

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.dev.yml

@@ -0,0 +1,60 @@
+services:
+  postgres:
+    image: postgres:16
+    container_name: crosspost_db
+    restart: unless-stopped
+    env_file:
+      - .env
+    ports:
+      - "5432:5432"
+    volumes:
+      - ./server/db/postgres_vol:/var/lib/postgresql/data
+      - ./server/db/schema.sql:/docker-entrypoint-initdb.d/schema.sql
+
+  redis:
+    image: redis:7
+    container_name: crosspost_redis
+    restart: unless-stopped
+    ports:
+      - "6379:6379"
+
+  backend:
+    build: .
+    container_name: crosspost_flask
+    volumes:
+      - .:/app
+      - model_cache:/models
+    env_file:
+      - .env
+    ports:
+      - "5000:5000"
+    command: flask --app server.app run --host=0.0.0.0 --debug
+    depends_on:
+      - postgres
+      - redis
+
+  worker:
+    build: .
+    volumes:
+      - .:/app
+      - model_cache:/models
+    container_name: crosspost_worker
+    env_file:
+      - .env
+    command: >
+      celery -A server.queue.celery_app.celery worker
+      --loglevel=info
+      --pool=solo
+    depends_on:
+      - postgres
+      - redis
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+
+volumes:
+  model_cache:

docker-compose.yml

@@ -1,7 +1,7 @@
 services:
   postgres:
     image: postgres:16
-    container_name: postgres_db
+    container_name: crosspost_db
     restart: unless-stopped
     env_file:
       - .env
@@ -11,5 +11,34 @@ services:
       - ./server/db/postgres_vol:/var/lib/postgresql/data
       - ./server/db/schema.sql:/docker-entrypoint-initdb.d/schema.sql
 
-volumes:
-  postgres_data:
+  redis:
+    image: redis:7
+    container_name: crosspost_redis
+    restart: unless-stopped
+    ports:
+      - "6379:6379"
+
+  backend:
+    build: .
+    container_name: crosspost_flask
+    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: crosspost_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/analysis/cultural.py

@@ -1,7 +1,6 @@
 import pandas as pd
 import re
 
-from collections import Counter
 from typing import Any
 
 
@@ -14,9 +13,6 @@ class CulturalAnalysis:
         df = original_df.copy()
         s = df[self.content_col].fillna("").astype(str).str.lower()
 
-        in_group_words = {"we", "us", "our", "ourselves"}
-        out_group_words = {"they", "them", "their", "themselves"}
-
         emotion_exclusions = {"emotion_neutral", "emotion_surprise"}
         emotion_cols = [
             c for c in df.columns
@@ -24,11 +20,13 @@ class CulturalAnalysis:
         ]
 
         # Tokenize per row
-        tokens_per_row = s.apply(lambda txt: re.findall(r"\b[a-z]{2,}\b", txt))
+        in_pattern = re.compile(r"\b(we|us|our|ourselves)\b")
+        out_pattern = re.compile(r"\b(they|them|their|themselves)\b")
+        token_pattern = re.compile(r"\b[a-z]{2,}\b")
 
-        total_tokens = int(tokens_per_row.map(len).sum())
-        in_hits = tokens_per_row.map(lambda toks: sum(t in in_group_words for t in toks)).astype(int)
-        out_hits = tokens_per_row.map(lambda toks: sum(t in out_group_words for t in toks)).astype(int)
+        in_hits = s.str.count(in_pattern)
+        out_hits = s.str.count(out_pattern)
+        total_tokens = s.str.count(token_pattern).sum()
 
         in_count = int(in_hits.sum())
         out_count = int(out_hits.sum())
@@ -62,33 +60,15 @@ class CulturalAnalysis:
     def get_stance_markers(self, df: pd.DataFrame) -> dict[str, Any]:
         s = df[self.content_col].fillna("").astype(str)
 
-        hedges = {
-            "maybe", "perhaps", "possibly", "probably", "likely", "seems", "seem",
-            "i think", "i feel", "i guess", "kind of", "sort of", "somewhat"
-        }
-        certainty = {
-            "definitely", "certainly", "clearly", "obviously", "undeniably", "always", "never"
-        }
+        hedge_pattern = re.compile(r"\b(maybe|perhaps|possibly|probably|likely|seems|seem|i think|i feel|i guess|kind of|sort of|somewhat)\b")
+        certainty_pattern = re.compile(r"\b(definitely|certainly|clearly|obviously|undeniably|always|never)\b")
+        deontic_pattern = re.compile(r"\b(must|should|need|needs|have to|has to|ought|required|require)\b")
+        permission_pattern = re.compile(r"\b(can|allowed|okay|ok|permitted)\b")
 
-        deontic = {
-            "must", "should", "need", "needs", "have to", "has to", "ought", "required", "require"
-        }
-
-        permission = {"can", "allowed", "okay", "ok", "permitted"}
-
-        def count_phrases(text: str, phrases: set[str]) -> int:
-            c = 0
-            for p in phrases:
-                if " " in p:
-                    c += len(re.findall(r"\b" + re.escape(p) + r"\b", text))
-                else:
-                    c += len(re.findall(r"\b" + re.escape(p) + r"\b", text))
-            return c
-
-        hedge_counts = s.apply(lambda t: count_phrases(t, hedges))
-        certainty_counts = s.apply(lambda t: count_phrases(t, certainty))
-        deontic_counts = s.apply(lambda t: count_phrases(t, deontic))
-        perm_counts = s.apply(lambda t: count_phrases(t, permission))
+        hedge_counts = s.str.count(hedge_pattern)
+        certainty_counts = s.str.count(certainty_pattern)
+        deontic_counts = s.str.count(deontic_pattern)
+        perm_counts = s.str.count(permission_pattern)
 
         token_counts = s.apply(lambda t: len(re.findall(r"\b[a-z]{2,}\b", t))).replace(0, 1)
 
@@ -108,44 +88,30 @@ class CulturalAnalysis:
             return {"entity_emotion_avg": {}}
 
         emotion_cols = [c for c in df.columns if c.startswith("emotion_")]
-        entity_counter = Counter()
 
-        for row in df["entities"].dropna():
-            if isinstance(row, list):
-                for ent in row:
-                    if isinstance(ent, dict):
-                        text = ent.get("text")
-                        if isinstance(text, str):
-                            text = text.strip()
-                            if len(text) >= 3:  # filter short junk
-                                entity_counter[text] += 1
+        entity_df = df[["entities"] + emotion_cols].explode("entities")
 
-        top_entities = entity_counter.most_common(top_n)
-
-        entity_emotion_avg = {}
-
-        for entity_text, _ in top_entities:
-            mask = df["entities"].apply(
-                lambda ents: isinstance(ents, list) and
-                any(isinstance(e, dict) and e.get("text") == entity_text for e in ents)
+        entity_df["entity_text"] = entity_df["entities"].apply(
+            lambda e: e.get("text").strip()
+            if isinstance(e, dict) and isinstance(e.get("text"), str) and len(e.get("text")) >= 3
+            else None
         )
 
-            post_count = int(mask.sum())
+        entity_df = entity_df.dropna(subset=["entity_text"])
+        entity_counts = entity_df["entity_text"].value_counts().head(top_n)
+        entity_emotion_avg = {}
 
-            if post_count >= min_posts:
+        for entity_text, count in entity_counts.items():
+            if count >= min_posts:
                 emo_means = (
-                    df.loc[mask, emotion_cols]
-                    .apply(pd.to_numeric, errors="coerce")
-                    .fillna(0.0)
+                    entity_df[entity_df["entity_text"] == entity_text][emotion_cols]
                     .mean()
                     .to_dict()
                 )
 
                 entity_emotion_avg[entity_text] = {
-                    "post_count": post_count,
-                    "emotion_avg": emo_means
+                    "post_count": int(count),
+                    "emotion_avg": emo_means,
                 }
 
-        return {
-            "entity_emotion_avg": entity_emotion_avg
-        }
+        return {"entity_emotion_avg": entity_emotion_avg}

server/analysis/enrichment.py

@@ -3,7 +3,7 @@ import pandas as pd
 from server.analysis.nlp import NLP
 
 class DatasetEnrichment:
-    def __init__(self, df, topics):
+    def __init__(self, df: pd.DataFrame, topics: dict):
         self.df = self._explode_comments(df)
         self.topics = topics
         self.nlp = NLP(self.df, "title", "content", self.topics)

server/app.py

@@ -16,11 +16,12 @@ from flask_jwt_extended import (
 
 from server.analysis.stat_gen import StatGen
 from server.analysis.enrichment import DatasetEnrichment
-from server.exceptions import NotAuthorisedException, NotExistentDatasetException
+from server.exceptions import NotAuthorisedException, NonExistentDatasetException
 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__)
 
@@ -125,53 +126,79 @@ def upload_data():
         ), 400
 
     try:
-        current_user = get_jwt_identity()
+        current_user = int(get_jwt_identity())
 
         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",
-                "event_count": len(enriched_df),
+                "message": "Dataset queued for processing",
                 "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:
         return jsonify({"error": f"An unexpected error occurred: {str(e)}"}), 500
 
-
 @app.route("/dataset/<int:dataset_id>", methods=["GET"])
 @jwt_required()
 def get_dataset(dataset_id):
     try:
-        user_id = get_jwt_identity()
-        dataset_content = dataset_manager.get_dataset_and_validate(dataset_id, int(user_id))
+        user_id = int(get_jwt_identity())
+        
+        if not dataset_manager.authorize_user_dataset(dataset_id, user_id):
+            raise NotAuthorisedException("This user is not authorised to access this dataset")
+
+        dataset_content = dataset_manager.get_dataset_content(dataset_id)
         filters = get_request_filters()
         filtered_dataset = stat_gen.filter_dataset(dataset_content, filters)
         return jsonify(filtered_dataset), 200
     except NotAuthorisedException:
         return jsonify({"error": "User is not authorised to access this content"}), 403
-    except NotExistentDatasetException:
+    except NonExistentDatasetException:
         return jsonify({"error": "Dataset does not exist"}), 404
     except Exception:
         print(traceback.format_exc())
         return jsonify({"error": "An unexpected error occured"}), 500
 
+@app.route("/dataset/<int:dataset_id>/status", methods=["GET"])
+@jwt_required()
+def get_dataset_status(dataset_id):
+    try:
+        user_id = int(get_jwt_identity())
+        
+        if not dataset_manager.authorize_user_dataset(dataset_id, user_id):
+            raise NotAuthorisedException("This user is not authorised to access this dataset")
+
+        dataset_status = dataset_manager.get_dataset_status(dataset_id)
+        return jsonify(dataset_status), 200
+    except NotAuthorisedException:
+        return jsonify({"error": "User is not authorised to access this content"}), 403
+    except NonExistentDatasetException:
+        return jsonify({"error": "Dataset does not exist"}), 404
+    except Exception:
+        print(traceback.format_exc())
+        return jsonify({"error": "An unexpected error occured"}), 500
 
 @app.route("/dataset/<int:dataset_id>/content", methods=["GET"])
 @jwt_required()
 def content_endpoint(dataset_id):
     try:
-        user_id = get_jwt_identity()
-        dataset_content = dataset_manager.get_dataset_and_validate(dataset_id, int(user_id))
+        user_id = int(get_jwt_identity())
+        if not dataset_manager.authorize_user_dataset(dataset_id, user_id):
+            raise NotAuthorisedException("This user is not authorised to access this dataset")
+
+        dataset_content = dataset_manager.get_dataset_content(dataset_id)
         filters = get_request_filters()
         return jsonify(stat_gen.get_content_analysis(dataset_content, filters)), 200
     except NotAuthorisedException:
@@ -187,8 +214,11 @@ def content_endpoint(dataset_id):
 @jwt_required()
 def get_summary(dataset_id):
     try:
-        user_id = get_jwt_identity()
-        dataset_content = dataset_manager.get_dataset_and_validate(dataset_id, int(user_id))
+        user_id = int(get_jwt_identity())
+        if not dataset_manager.authorize_user_dataset(dataset_id, user_id):
+            raise NotAuthorisedException("This user is not authorised to access this dataset")
+
+        dataset_content = dataset_manager.get_dataset_content(dataset_id)
         filters = get_request_filters()
         return jsonify(stat_gen.summary(dataset_content, filters)), 200
     except NotAuthorisedException:
@@ -204,8 +234,11 @@ def get_summary(dataset_id):
 @jwt_required()
 def get_time_analysis(dataset_id):
     try:
-        user_id = get_jwt_identity()
-        dataset_content = dataset_manager.get_dataset_and_validate(dataset_id, int(user_id))
+        user_id = int(get_jwt_identity())
+        if not dataset_manager.authorize_user_dataset(dataset_id, user_id):
+            raise NotAuthorisedException("This user is not authorised to access this dataset")
+
+        dataset_content = dataset_manager.get_dataset_content(dataset_id)
         filters = get_request_filters()
         return jsonify(stat_gen.get_time_analysis(dataset_content, filters)), 200
     except NotAuthorisedException:
@@ -221,8 +254,11 @@ def get_time_analysis(dataset_id):
 @jwt_required()
 def get_user_analysis(dataset_id):
     try:
-        user_id = get_jwt_identity()
-        dataset_content = dataset_manager.get_dataset_and_validate(dataset_id, int(user_id))
+        user_id = int(get_jwt_identity())
+        if not dataset_manager.authorize_user_dataset(dataset_id, user_id):
+            raise NotAuthorisedException("This user is not authorised to access this dataset")
+
+        dataset_content = dataset_manager.get_dataset_content(dataset_id)
         filters = get_request_filters()
         return jsonify(stat_gen.get_user_analysis(dataset_content, filters)), 200
     except NotAuthorisedException:
@@ -238,8 +274,11 @@ def get_user_analysis(dataset_id):
 @jwt_required()
 def get_cultural_analysis(dataset_id):
     try:
-        user_id = get_jwt_identity()
-        dataset_content = dataset_manager.get_dataset_and_validate(dataset_id, int(user_id))
+        user_id = int(get_jwt_identity())
+        if not dataset_manager.authorize_user_dataset(dataset_id, user_id):
+            raise NotAuthorisedException("This user is not authorised to access this dataset")
+
+        dataset_content = dataset_manager.get_dataset_content(dataset_id)
         filters = get_request_filters()
         return jsonify(stat_gen.get_cultural_analysis(dataset_content, filters)), 200
     except NotAuthorisedException:
@@ -255,8 +294,11 @@ def get_cultural_analysis(dataset_id):
 @jwt_required()
 def get_interaction_analysis(dataset_id):
     try:
-        user_id = get_jwt_identity()
-        dataset_content = dataset_manager.get_dataset_and_validate(dataset_id, int(user_id))
+        user_id = int(get_jwt_identity())
+        if not dataset_manager.authorize_user_dataset(dataset_id, user_id):
+            raise NotAuthorisedException("This user is not authorised to access this dataset")
+
+        dataset_content = dataset_manager.get_dataset_content(dataset_id)
         filters = get_request_filters()
         return jsonify(stat_gen.get_interactional_analysis(dataset_content, filters)), 200
     except NotAuthorisedException:

server/core/datasets.py

@@ -1,19 +1,22 @@
 import pandas as pd
 from server.db.database import PostgresConnector
 from psycopg2.extras import Json
-from server.exceptions import NotAuthorisedException
+from server.exceptions import NotAuthorisedException, NonExistentDatasetException
 
 class DatasetManager:
     def __init__(self, db: PostgresConnector):
         self.db = db
 
-    def get_dataset_and_validate(self, dataset_id: int, user_id: int) -> pd.DataFrame:
+    def authorize_user_dataset(self, dataset_id: int, user_id: int) -> bool:
         dataset_info = self.get_dataset_info(dataset_id)
 
-        if dataset_info.get("user_id") != user_id:
-            raise NotAuthorisedException("This user is not authorised to access this dataset")
+        if dataset_info.get("user_id", None) == None:
+            return False
 
-        return self.get_dataset_content(dataset_id)
+        if dataset_info.get("user_id") != user_id:
+            return False
+        
+        return True
 
     def get_dataset_content(self, dataset_id: int) -> pd.DataFrame:
         query = "SELECT * FROM events WHERE dataset_id = %s"
@@ -23,7 +26,11 @@ class DatasetManager:
     def get_dataset_info(self, dataset_id: int) -> dict:
         query = "SELECT * FROM datasets WHERE id = %s"
         result = self.db.execute(query, (dataset_id,), fetch=True)
-        return result[0] if result else None
+
+        if not result:
+            raise NonExistentDatasetException(f"Dataset {dataset_id} does not exist")
+
+        return result[0]
     
     def save_dataset_info(self, user_id: int, dataset_name: str, topics: dict) -> int:
         query = """
@@ -34,11 +41,6 @@ class DatasetManager:
         result = self.db.execute(query, (user_id, dataset_name, Json(topics)), fetch=True)
         return result[0]["id"] if result else None
 
-    def get_dataset_content(self, dataset_id: int) -> pd.DataFrame:
-        query = "SELECT * FROM events WHERE dataset_id = %s"
-        result = self.db.execute(query, (dataset_id,), fetch=True)
-        return pd.DataFrame(result)
-
     def save_dataset_content(self, dataset_id: int, event_data: pd.DataFrame):
         if event_data.empty:
             return
@@ -49,6 +51,7 @@ class DatasetManager:
                 type,
                 parent_id,
                 author,
+                title,
                 content,
                 timestamp,
                 date,
@@ -70,7 +73,8 @@ class DatasetManager:
                 %s, %s, %s, %s, %s,
                 %s, %s, %s, %s, %s,
                 %s, %s, %s, %s, %s,
-                %s, %s, %s, %s, %s
+                %s, %s, %s, %s, %s,
+                %s
             )
         """
 
@@ -80,6 +84,7 @@ class DatasetManager:
                 row["type"],
                 row["parent_id"],
                 row["author"],
+                row.get("title"),
                 row["content"],
                 row["timestamp"],
                 row["date"],
@@ -101,3 +106,35 @@ class DatasetManager:
         ]
 
         self.db.execute_batch(query, values)
+
+    def set_dataset_status(self, dataset_id: int, status: str, status_message: str | None = None):
+        if status not in ["processing", "complete", "error"]:
+            raise ValueError("Invalid status")
+
+        query = """
+            UPDATE datasets
+            SET status = %s,
+                status_message = %s,
+                completed_at = CASE
+                    WHEN %s = 'complete' THEN NOW()
+                    ELSE NULL
+                END
+            WHERE id = %s
+        """
+
+        self.db.execute(query, (status, status_message, status, dataset_id))
+
+    def get_dataset_status(self, dataset_id: int):
+        query = """
+            SELECT status, status_message, completed_at
+            FROM datasets
+            WHERE id = %s
+        """
+
+        result = self.db.execute(query, (dataset_id, ), fetch=True)
+        
+        if not result:
+            print(result)
+            raise NonExistentDatasetException(f"Dataset {dataset_id} does not exist")
+        
+        return result[0]

server/db/database.py

@@ -27,19 +27,21 @@ class PostgresConnector:
         self.connection.autocommit = False
 
     def execute(self, query, params=None, fetch=False) -> list:
+        try:
             with self.connection.cursor(cursor_factory=RealDictCursor) as cursor:
                 cursor.execute(query, params)
-            if fetch:
-                return cursor.fetchall()
+                result = cursor.fetchall() if fetch else None
             self.connection.commit()
+            return result
+        except Exception:
+            self.connection.rollback()
+            raise
 
     def execute_batch(self, query, values):
         with self.connection.cursor(cursor_factory=RealDictCursor) as cursor:
             execute_batch(cursor, query, values)
             self.connection.commit()
 
-
-    ## User Management Methods
     def close(self):
         if self.connection:
             self.connection.close()

server/db/schema.sql

@@ -11,9 +11,19 @@ CREATE TABLE datasets (
     user_id INTEGER NOT NULL,
     name VARCHAR(255) NOT NULL,
     description TEXT,
+
+    -- Job state machine
+    status TEXT NOT NULL DEFAULT 'processing',
+    status_message TEXT,
+    completed_at TIMESTAMP,
+
     created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
     topics JSONB,
-    FOREIGN KEY (user_id) REFERENCES users(id) ON DELETE CASCADE
+    FOREIGN KEY (user_id) REFERENCES users(id) ON DELETE CASCADE,
+
+    -- Enforce valid states
+    CONSTRAINT datasets_status_check
+    CHECK (status IN ('processing', 'complete', 'error'))
 );
 
 CREATE TABLE events (
@@ -30,7 +40,10 @@ CREATE TABLE events (
     hour INTEGER NOT NULL,
     weekday VARCHAR(255) NOT NULL,
 
-    /* Comments and Replies */
+    /* Posts Only */
+    title VARCHAR(255),
+
+    /* Comments Only*/
     parent_id VARCHAR(255),
     reply_to VARCHAR(255),
     source VARCHAR(255) NOT NULL,

server/exceptions.py

@@ -1,7 +1,7 @@
 class NotAuthorisedException(Exception):
     pass
 
-class NotExistentDatasetException(Exception):
+class NonExistentDatasetException(Exception):
     pass
 
 class DatabaseNotConfiguredException(Exception):

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 +1,24 @@
+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):
+
+    try:
+        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)
+        dataset_manager.set_dataset_status(dataset_id, "complete", "NLP Processing Completed Successfully")
+    except Exception as e:
+        dataset_manager.set_dataset_status(dataset_id, "error", f"An error occurred: {e}")