diff --git a/app/api/endpoints/tokens.py b/app/api/endpoints/tokens.py
index ca0ced7..299b926 100644
--- a/app/api/endpoints/tokens.py
+++ b/app/api/endpoints/tokens.py
@@ -21,6 +21,8 @@ class TokenRequest(BaseModel):
catalogs: list[CatalogConfig] | None = Field(default=None, description="Optional catalog configuration")
language: str = Field(default="en-US", description="Language for TMDB API")
rpdb_key: str | None = Field(default=None, description="Optional RPDB API Key")
+ excluded_movie_genres: list[str] = Field(default_factory=list, description="List of movie genre IDs to exclude")
+ excluded_series_genres: list[str] = Field(default_factory=list, description="List of series genre IDs to exclude")
class TokenResponse(BaseModel):
@@ -130,6 +132,8 @@ async def create_token(payload: TokenRequest, request: Request) -> TokenResponse
language=payload.language or default_settings.language,
catalogs=payload.catalogs if payload.catalogs else default_settings.catalogs,
rpdb_key=rpdb_key,
+ excluded_movie_genres=payload.excluded_movie_genres,
+ excluded_series_genres=payload.excluded_series_genres,
)
# encode_settings now includes the "settings:" prefix
diff --git a/app/core/settings.py b/app/core/settings.py
index 38b4c4e..924d599 100644
--- a/app/core/settings.py
+++ b/app/core/settings.py
@@ -14,6 +14,8 @@ class UserSettings(BaseModel):
catalogs: list[CatalogConfig]
language: str = "en-US"
rpdb_key: str | None = None
+ excluded_movie_genres: list[str] = []
+ excluded_series_genres: list[str] = []
def encode_settings(settings: UserSettings) -> str:
diff --git a/app/services/catalog.py b/app/services/catalog.py
index 58eae4e..c9f1334 100644
--- a/app/services/catalog.py
+++ b/app/services/catalog.py
@@ -45,7 +45,9 @@ def build_catalog_entry(self, item, label, config_id):
"extra": [],
}
- async def get_theme_based_catalogs(self, library_items: list[dict]) -> list[dict]:
+ async def get_theme_based_catalogs(
+ self, library_items: list[dict], user_settings: UserSettings | None = None
+ ) -> list[dict]:
catalogs = []
# 1. Build User Profile
# Combine loved and watched
@@ -65,16 +67,27 @@ async def get_theme_based_catalogs(self, library_items: list[dict]) -> list[dict
scored_obj = self.scoring_service.process_item(item_data)
scored_objects.append(scored_obj)
+ # Get excluded genres
+ excluded_movie_genres = []
+ excluded_series_genres = []
+ if user_settings:
+ excluded_movie_genres = [int(g) for g in user_settings.excluded_movie_genres]
+ excluded_series_genres = [int(g) for g in user_settings.excluded_series_genres]
+
# 2. Generate Thematic Rows with Type-Specific Profiles
# Generate for Movies
- movie_profile = await self.user_profile_service.build_user_profile(scored_objects, content_type="movie")
+ movie_profile = await self.user_profile_service.build_user_profile(
+ scored_objects, content_type="movie", excluded_genres=excluded_movie_genres
+ )
movie_rows = await self.row_generator.generate_rows(movie_profile, "movie")
for row in movie_rows:
catalogs.append({"type": "movie", "id": row.id, "name": row.title, "extra": []})
# Generate for Series
- series_profile = await self.user_profile_service.build_user_profile(scored_objects, content_type="series")
+ series_profile = await self.user_profile_service.build_user_profile(
+ scored_objects, content_type="series", excluded_genres=excluded_series_genres
+ )
series_rows = await self.row_generator.generate_rows(series_profile, "series")
for row in series_rows:
@@ -98,7 +111,7 @@ async def get_dynamic_catalogs(
catalogs = []
if include_theme_based_rows:
- catalogs.extend(await self.get_theme_based_catalogs(library_items))
+ catalogs.extend(await self.get_theme_based_catalogs(library_items, user_settings))
# 3. Add Item-Based Rows
if include_item_based_rows:
diff --git a/app/services/discovery.py b/app/services/discovery.py
index fd892aa..35d1c3d 100644
--- a/app/services/discovery.py
+++ b/app/services/discovery.py
@@ -14,7 +14,11 @@ def __init__(self):
self.tmdb_service = TMDBService()
async def discover_recommendations(
- self, profile: UserTasteProfile, content_type: str, limit: int = 20
+ self,
+ profile: UserTasteProfile,
+ content_type: str,
+ limit: int = 20,
+ excluded_genres: list[int] | None = None,
) -> list[dict]:
"""
Find content that matches the user's taste profile.
@@ -33,17 +37,26 @@ async def discover_recommendations(
top_crew = profile.get_top_crew(limit=1) # e.g. [(555, 1.0)] - Director
top_countries = profile.get_top_countries(limit=2)
+ top_year = profile.get_top_year(limit=1)
if not top_genres and not top_keywords and not top_cast:
# Fallback if profile is empty
return []
tasks = []
+ base_params = {}
+ if excluded_genres:
+ base_params["without_genres"] = "|".join([str(g) for g in excluded_genres])
# Query 1: Top Genres Mix
if top_genres:
genre_ids = "|".join([str(g[0]) for g in top_genres])
- params_popular = {"with_genres": genre_ids, "sort_by": "popularity.desc", "vote_count.gte": 100}
+ params_popular = {
+ "with_genres": genre_ids,
+ "sort_by": "popularity.desc",
+ "vote_count.gte": 500,
+ **base_params,
+ }
tasks.append(self._fetch_discovery(content_type, params_popular))
# fetch atleast two pages of results
@@ -51,15 +64,21 @@ async def discover_recommendations(
params_rating = {
"with_genres": genre_ids,
"sort_by": "ratings.desc",
- "vote_count.gte": 300,
+ "vote_count.gte": 500,
"page": i + 1,
+ **base_params,
}
tasks.append(self._fetch_discovery(content_type, params_rating))
# Query 2: Top Keywords
if top_keywords:
keyword_ids = "|".join([str(k[0]) for k in top_keywords])
- params_keywords = {"with_keywords": keyword_ids, "sort_by": "popularity.desc"}
+ params_keywords = {
+ "with_keywords": keyword_ids,
+ "sort_by": "popularity.desc",
+ "vote_count.gte": 500,
+ **base_params,
+ }
tasks.append(self._fetch_discovery(content_type, params_keywords))
# fetch atleast two pages of results
@@ -67,18 +86,29 @@ async def discover_recommendations(
params_rating = {
"with_keywords": keyword_ids,
"sort_by": "ratings.desc",
- "vote_count.gte": 300,
+ "vote_count.gte": 500,
"page": i + 1,
+ **base_params,
}
tasks.append(self._fetch_discovery(content_type, params_rating))
# Query 3: Top Actors
for actor in top_cast:
actor_id = actor[0]
- params_actor = {"with_cast": str(actor_id), "sort_by": "popularity.desc"}
+ params_actor = {
+ "with_cast": str(actor_id),
+ "sort_by": "popularity.desc",
+ "vote_count.gte": 500,
+ **base_params,
+ }
tasks.append(self._fetch_discovery(content_type, params_actor))
- params_rating = {"with_cast": str(actor_id), "sort_by": "ratings.desc", "vote_count.gte": 300}
+ params_rating = {
+ "with_cast": str(actor_id),
+ "sort_by": "ratings.desc",
+ "vote_count.gte": 500,
+ **base_params,
+ }
tasks.append(self._fetch_discovery(content_type, params_rating))
# Query 4: Top Director
@@ -87,19 +117,47 @@ async def discover_recommendations(
params_director = {
"with_crew": str(director_id),
"sort_by": "vote_average.desc", # Directors imply quality preference
+ "vote_count.gte": 500,
+ **base_params,
}
tasks.append(self._fetch_discovery(content_type, params_director))
- params_rating = {"with_crew": str(director_id), "sort_by": "ratings.desc", "vote_count.gte": 300}
+ params_rating = {
+ "with_crew": str(director_id),
+ "sort_by": "ratings.desc",
+ "vote_count.gte": 500,
+ **base_params,
+ }
tasks.append(self._fetch_discovery(content_type, params_rating))
# Query 5: Top Countries
if top_countries:
country_ids = "|".join([str(c[0]) for c in top_countries])
- params_country = {"with_origin_country": country_ids, "sort_by": "popularity.desc", "vote_count.gte": 100}
+ params_country = {
+ "with_origin_country": country_ids,
+ "sort_by": "popularity.desc",
+ "vote_count.gte": 100,
+ **base_params,
+ }
tasks.append(self._fetch_discovery(content_type, params_country))
- params_rating = {"with_origin_country": country_ids, "sort_by": "ratings.desc", "vote_count.gte": 300}
+ params_rating = {
+ "with_origin_country": country_ids,
+ "sort_by": "ratings.desc",
+ "vote_count.gte": 300,
+ **base_params,
+ }
+ tasks.append(self._fetch_discovery(content_type, params_rating))
+
+ # query 6: Top year
+ if top_year:
+ year = top_year[0][0]
+ params_rating = {
+ "year": year,
+ "sort_by": "ratings.desc",
+ "vote_count.gte": 500,
+ **base_params,
+ }
tasks.append(self._fetch_discovery(content_type, params_rating))
# 3. Execute Parallel Queries
diff --git a/app/services/recommendation_service.py b/app/services/recommendation_service.py
index 7f929ec..e54220b 100644
--- a/app/services/recommendation_service.py
+++ b/app/services/recommendation_service.py
@@ -254,6 +254,15 @@ async def get_recommendations_for_item(self, item_id: str) -> list[dict]:
# 1. Filter by TMDB ID
recommendations = await self._filter_candidates(recommendations, watched_imdb, watched_tmdb)
+ # 1.5 Filter by Excluded Genres
+ # We need to detect content_type from item_id or media_type to know which exclusion list to use.
+ # media_type is already resolved above.
+ excluded_ids = set(self._get_excluded_genre_ids(media_type))
+ if excluded_ids:
+ recommendations = [
+ item for item in recommendations if not excluded_ids.intersection(item.get("genre_ids") or [])
+ ]
+
# 2. Fetch Metadata (gets IMDB IDs)
meta_items = await self._fetch_metadata_for_items(recommendations, media_type)
@@ -278,6 +287,15 @@ async def get_recommendations_for_item(self, item_id: str) -> list[dict]:
logger.info(f"Found {len(final_items)} valid recommendations for {item_id}")
return final_items
+ def _get_excluded_genre_ids(self, content_type: str) -> list[int]:
+ if not self.user_settings:
+ return []
+ if content_type == "movie":
+ return [int(g) for g in self.user_settings.excluded_movie_genres]
+ elif content_type in ["series", "tv"]:
+ return [int(g) for g in self.user_settings.excluded_series_genres]
+ return []
+
async def get_recommendations_for_theme(self, theme_id: str, content_type: str, limit: int = 20) -> list[dict]:
"""
Parse a dynamic theme ID and fetch recommendations.
@@ -315,6 +333,16 @@ async def get_recommendations_for_theme(self, theme_id: str, content_type: str,
if "sort_by" not in params:
params["sort_by"] = "popularity.desc"
+ # Apply Excluded Genres
+ excluded_ids = self._get_excluded_genre_ids(content_type)
+ if excluded_ids:
+ # If with_genres is specified, we technically shouldn't exclude what is explicitly asked for?
+ # But the user asked to "exclude those genres".
+ # If I exclude them from "without_genres", TMDB might return 0 results if the theme IS that genre.
+ # But RowGenerator safeguards against generating themes for excluded genres.
+ # So this is safe for keyword/country rows.
+ params["without_genres"] = "|".join(str(g) for g in excluded_ids)
+
# Fetch
recommendations = await self.tmdb_service.get_discover(content_type, **params)
candidates = recommendations.get("results", [])
@@ -407,15 +435,25 @@ async def get_recommendations(
tasks_a.append(self._fetch_recommendations_from_tmdb(source.get("_id"), source.get("type"), limit=10))
similarity_candidates = []
similarity_recommendations = await asyncio.gather(*tasks_a, return_exceptions=True)
+
+ excluded_ids = set(self._get_excluded_genre_ids(content_type))
+
similarity_recommendations = [item for item in similarity_recommendations if not isinstance(item, Exception)]
- for item in similarity_recommendations:
- similarity_candidates.extend(item)
+ for batch in similarity_recommendations:
+ similarity_candidates.extend(
+ item for item in batch if not excluded_ids.intersection(item.get("genre_ids") or [])
+ )
# --- Candidate Set B: Profile-based Discovery ---
+ # Extract excluded genres
+ excluded_genres = list(excluded_ids) # Convert back to list for consistency
+
# Use typed profile based on content_type
- user_profile = await self.user_profile_service.build_user_profile(scored_objects, content_type=content_type)
+ user_profile = await self.user_profile_service.build_user_profile(
+ scored_objects, content_type=content_type, excluded_genres=excluded_genres
+ )
discovery_candidates = await self.discovery_engine.discover_recommendations(
- user_profile, content_type, limit=20
+ user_profile, content_type, limit=20, excluded_genres=excluded_genres
)
# --- Combine & Deduplicate ---
diff --git a/app/services/user_profile.py b/app/services/user_profile.py
index 6495136..3d21ff8 100644
--- a/app/services/user_profile.py
+++ b/app/services/user_profile.py
@@ -38,7 +38,10 @@ def __init__(self):
self.tmdb_service = TMDBService()
async def build_user_profile(
- self, scored_items: list[ScoredItem], content_type: str | None = None
+ self,
+ scored_items: list[ScoredItem],
+ content_type: str | None = None,
+ excluded_genres: list[int] | None = None,
) -> UserTasteProfile:
"""
Aggregates multiple item vectors into a single User Taste Profile.
@@ -76,7 +79,7 @@ async def build_user_profile(
# Scale by Interest Score (0.0 - 1.0)
interest_weight = item.score / 100.0
- self._merge_vector(profile_data, item_vector, interest_weight)
+ self._merge_vector(profile_data, item_vector, interest_weight, excluded_genres)
# Convert to Pydantic Model
profile = UserTasteProfile(
@@ -206,7 +209,13 @@ def _vectorize_item(self, meta: dict) -> dict[str, list[int] | int | list[str] |
return vector
- def _merge_vector(self, profile: dict, item_vector: dict, weight: float):
+ def _merge_vector(
+ self,
+ profile: dict,
+ item_vector: dict,
+ weight: float,
+ excluded_genres: list[int] | None = None,
+ ):
"""Merges an item's sparse vector into the main profile with a weight."""
# Weights for specific dimensions (Feature Importance)
@@ -228,6 +237,8 @@ def _merge_vector(self, profile: dict, item_vector: dict, weight: float):
profile["years"][ids] += final_weight
elif ids:
for feature_id in ids:
+ if dim == "genres" and excluded_genres and feature_id in excluded_genres:
+ continue
profile[dim][feature_id] += final_weight
async def _fetch_full_metadata(self, tmdb_id: int, type_: str) -> dict | None:
diff --git a/static/index.html b/static/index.html
index fe9ab9a..1c4785c 100644
--- a/static/index.html
+++ b/static/index.html
@@ -208,6 +208,31 @@
+
+
+
+
+