fix(connectors): connector detectors returning name of ID alongside connector obj

This is easier and quicker compared to deriving a topics list based on the dataset that has been scraped.

While using LLMs to create a personalised topic list based on the query, category or dataset itself would yield better results for most, it is beyond the scope of this project.

server/connectors/registry.py

@@ -15,11 +15,11 @@ def _discover_connectors() -> list[type[BaseConnector]]:
         if cls.source_name  # guard against abstract intermediaries
     ]
 
-def get_available_connectors() -> list[type[BaseConnector]]:
-    return [c for c in _discover_connectors() if c.is_available()]
+def get_available_connectors() -> dict[str, type[BaseConnector]]:
+    return {c.source_name: c for c in _discover_connectors() if c.is_available()}
 
 def get_connector_metadata() -> list[dict]:
     return [
-        {"id": c.source_name, "label": c.display_name}
-        for c in get_available_connectors()
+        {"id": id, "label": obj.display_name}
+        for id, obj in get_available_connectors().items()
     ]

server/topics.json

@@ -0,0 +1,67 @@
+{
+  "Personal Life": "daily life, life updates, what happened today, personal stories, life events, reflections",
+
+  "Relationships": "dating, relationships, breakups, friendships, family relationships, marriage, relationship advice",
+
+  "Family & Parenting": "parents, parenting, children, raising kids, family dynamics, family stories",
+
+  "Work & Careers": "jobs, workplaces, office life, promotions, quitting jobs, career advice, workplace drama",
+
+  "Education": "school, studying, exams, university, homework, academic pressure, learning experiences",
+
+  "Money & Finance": "saving money, debt, budgeting, cost of living, financial advice, personal finance",
+
+  "Health & Fitness": "exercise, gym, workouts, running, diet, fitness routines, weight loss",
+
+  "Mental Health": "stress, anxiety, depression, burnout, therapy, emotional wellbeing",
+
+  "Food & Cooking": "meals, cooking, recipes, restaurants, snacks, food opinions",
+
+  "Travel": "holidays, trips, tourism, travel experiences, airports, flights, travel tips",
+
+  "Entertainment": "movies, TV shows, streaming services, celebrities, pop culture",
+
+  "Music": "songs, albums, artists, concerts, music opinions",
+
+  "Gaming": "video games, gaming culture, consoles, PC gaming, esports",
+
+  "Sports": "sports matches, teams, players, competitions, sports opinions",
+
+  "Technology": "phones, gadgets, apps, AI, software, tech trends",
+
+  "Internet Culture": "memes, viral trends, online jokes, internet drama, trending topics",
+
+  "Social Media": "platforms, influencers, content creators, algorithms, online communities",
+
+  "News & Current Events": "breaking news, world events, major incidents, public discussions",
+
+  "Politics": "political debates, elections, government policies, ideology",
+
+  "Culture & Society": "social issues, cultural trends, generational debates, societal changes",
+
+  "Identity & Lifestyle": "personal identity, lifestyle choices, values, self-expression",
+
+  "Hobbies & Interests": "art, photography, crafts, collecting, hobbies",
+
+  "Fashion & Beauty": "clothing, style, makeup, skincare, fashion trends",
+
+  "Animals & Pets": "pets, animal videos, pet care, wildlife",
+
+  "Humour": "jokes, funny stories, sarcasm, memes",
+
+  "Opinions & Debates": "hot takes, controversial opinions, arguments, discussions",
+
+  "Advice & Tips": "life advice, tutorials, how-to tips, recommendations",
+
+  "Product Reviews": "reviews, recommendations, experiences with products",
+
+  "Complaints & Rants": "frustrations, complaining, venting about things",
+
+  "Motivation & Inspiration": "motivational quotes, success stories, encouragement",
+
+  "Questions & Curiosity": "asking questions, seeking opinions, curiosity posts",
+
+  "Celebrations & Achievements": "birthdays, milestones, achievements, good news",
+
+  "Random Thoughts": "shower thoughts, observations, random ideas"
+}

server/utils.py

@@ -48,3 +48,7 @@ def get_request_filters() -> dict:
         filters["data_sources"] = data_sources
 
     return filters
+
+def split_limit(limit: int, n: int) -> list[int]:
+    base, remainder = divmod(limit, n)
+    return [base + (1 if i < remainder else 0) for i in range(n)]