diff --git a/app_chat.py b/app_chat.py
index f98d3bb1..8c87d256 100644
--- a/app_chat.py
+++ b/app_chat.py
@@ -497,8 +497,6 @@ def _run_chat_pipeline(data, log_messages):
all_songs = plan_result['songs']
song_sources = plan_result['song_sources']
tools_used_history = plan_result['tools_used_history']
- tool_execution_summary = plan_result['tool_execution_summary']
- detected_min_rating = plan_result['detected_min_rating']
plan_notes = plan_result.get('plan_notes', [])
executed_query_str = plan_result['executed_query_str']
filter_applied = plan_result.get('filter_applied', False)
diff --git a/app_provider_migration.py b/app_provider_migration.py
index 7f400034..1eb24897 100644
--- a/app_provider_migration.py
+++ b/app_provider_migration.py
@@ -1481,7 +1481,6 @@ def matched_albums(session_id):
dry = state.get('dry_run') or {}
auto_matches = dry.get('matches') or {}
- match_tiers = dry.get('match_tiers') or {}
manual_matches = state.get('manual_matches') or {}
manual_unmatches = set(state.get('manual_unmatches') or [])
new_meta = state.get('new_meta') or {}
diff --git a/config.py b/config.py
index c3ee924b..8f4ff9a6 100644
--- a/config.py
+++ b/config.py
@@ -127,6 +127,11 @@ def _compute_headers():
# --- GPU Acceleration for Clustering (Optional, requires NVIDIA GPU and RAPIDS cuML) ---
USE_GPU_CLUSTERING = os.environ.get("USE_GPU_CLUSTERING", "False").lower() == "true"
+# --- Clustering Cleanup Behavior ---
+# When True (default), existing '_automatic' playlists are deleted before new clusters are created.
+# Set to False to preserve old automatic playlists when running clustering.
+CLUSTERING_CLEANING = os.environ.get("CLUSTERING_CLEANING", "True").lower() == "true"
+
# --- DBSCAN Only Constants (Ranges for Evolutionary Approach) ---
# Default ranges for DBSCAN parameters
DBSCAN_EPS_MIN = float(os.getenv("DBSCAN_EPS_MIN", "0.1"))
diff --git a/query/brainstorm_real_gmm_080.py b/query/brainstorm_real_gmm_080.py
index eac26981..08363f5e 100644
--- a/query/brainstorm_real_gmm_080.py
+++ b/query/brainstorm_real_gmm_080.py
@@ -170,7 +170,6 @@ def main():
item_ids, all_X = load_all_embeddings()
clap_scores = load_clap_scores() if USE_CLAP else {}
- id_to_idx = {iid: i for i, iid in enumerate(item_ids)}
pred_w = load_prediction_weights()
print("Loaded MSD prediction weights")
diff --git a/static/menu.css b/static/menu.css
index b12f9baa..cee3f133 100644
--- a/static/menu.css
+++ b/static/menu.css
@@ -85,7 +85,6 @@ html.sidebar-open .sidebar {
color: #d1d5db;
text-decoration: none;
padding: 0.75rem 0;
- font-size: 1rem;
white-space: nowrap;
transition: background-color 0.2s, color 0.2s;
border-radius: 0.375rem;
diff --git a/static/setup.js b/static/setup.js
index 606d26ba..03c259a3 100644
--- a/static/setup.js
+++ b/static/setup.js
@@ -315,7 +315,7 @@ function renderAdvancedFields(fields) {
label: field.name,
placeholder: field.default ? field.default : '',
type: field.type === 'bool' ? 'boolean' : field.type,
- inputType: field.type === 'boolean' ? 'text' : 'text',
+ inputType: 'text',
secret: secret,
has_value: field.has_value,
options: Array.isArray(field.options) ? field.options : null,
diff --git a/static/sunburst.js b/static/sunburst.js
index 8708eb5b..01b84cd2 100644
--- a/static/sunburst.js
+++ b/static/sunburst.js
@@ -23,7 +23,7 @@ const SunburstChart = (() => {
function buildTree(data) {
const TAG_DEPTH = 3;
const root = {name: 'root', children: [], depth: 0};
- for (const mood of Object.keys(data).sort()) {
+ for (const mood of Object.keys(data).sort((a, b) => a.localeCompare(b))) {
const moodNode = {name: mood, children: [], depth: 1, mood: mood};
data[mood].forEach(c => {
let parent = moodNode;
diff --git a/tasks/artist_gmm_manager.py b/tasks/artist_gmm_manager.py
index e879af56..be4f661a 100644
--- a/tasks/artist_gmm_manager.py
+++ b/tasks/artist_gmm_manager.py
@@ -149,9 +149,6 @@ def fit_artist_gmm(artist_name: str, track_embeddings: List[np.ndarray]) -> Opti
if n_samples < 5:
logger.info(f"Artist '{artist_name}' has {n_samples} tracks - using each song as a GMM component with equal weights")
- # Use a small fixed covariance for numerical stability
- # This acts like narrow Gaussians centered on each actual song
- fixed_variance = 0.01
# Each song becomes one component with equal weight
n_components = n_samples
diff --git a/tasks/clap_analyzer.py b/tasks/clap_analyzer.py
index 0b8b3b0a..fd1b19fe 100644
--- a/tasks/clap_analyzer.py
+++ b/tasks/clap_analyzer.py
@@ -265,7 +265,6 @@ def _load_text_model():
provider_options=[p[1] for p in provider_options]
)
- active_provider = session.get_providers()[0]
logger.info(f"✓ CLAP text model loaded successfully (~478MB)")
except Exception as e:
@@ -694,9 +693,7 @@ def get_text_embeddings_batch(query_texts: list) -> Optional[np.ndarray]:
# Get text-only model for text search
session = get_clap_text_model()
tokenizer = get_tokenizer()
-
- batch_size = len(query_texts)
-
+
# Tokenize all texts at once (max_length=77 for CLAP)
encoded = tokenizer(
query_texts,
diff --git a/tasks/clustering.py b/tasks/clustering.py
index 7ac90c12..69736405 100644
--- a/tasks/clustering.py
+++ b/tasks/clustering.py
@@ -19,21 +19,23 @@
from psycopg2.extras import DictCursor
# Import configuration
-from config import MAX_SONGS_PER_CLUSTER, MOOD_LABELS, STRATIFIED_GENRES, MUTATION_KMEANS_COORD_FRACTION, MUTATION_INT_ABS_DELTA, MUTATION_FLOAT_ABS_DELTA, TOP_N_ELITES, EXPLOITATION_START_FRACTION, EXPLOITATION_PROBABILITY_CONFIG, SAMPLING_PERCENTAGE_CHANGE_PER_RUN, ITERATIONS_PER_BATCH_JOB, MAX_CONCURRENT_BATCH_JOBS, MIN_PLAYLIST_SIZE_FOR_TOP_N, CLUSTERING_BATCH_TIMEOUT_MINUTES, CLUSTERING_MAX_FAILED_BATCHES
+from config import MAX_SONGS_PER_CLUSTER, MOOD_LABELS, STRATIFIED_GENRES, MUTATION_KMEANS_COORD_FRACTION, MUTATION_INT_ABS_DELTA, MUTATION_FLOAT_ABS_DELTA, TOP_N_ELITES, EXPLOITATION_START_FRACTION, EXPLOITATION_PROBABILITY_CONFIG, SAMPLING_PERCENTAGE_CHANGE_PER_RUN, ITERATIONS_PER_BATCH_JOB, MAX_CONCURRENT_BATCH_JOBS, MIN_PLAYLIST_SIZE_FOR_TOP_N, CLUSTERING_BATCH_TIMEOUT_MINUTES, CLUSTERING_MAX_FAILED_BATCHES, CLUSTERING_CLEANING
from error import error_manager
from error.error_dictionary import ERR_CLUSTERING_FAILED
# Import AI naming function and prompt template
-from tasks.ai.api import get_ai_playlist_name
-from tasks.ai.prompts import creative_prompt_template
+# (used by clustering_helper._try_ai_name_playlist, imported there)
# Import media server functions
from .mediaserver import create_playlist, delete_automatic_playlists
# Import refactored clustering helpers
from .clustering_helper import (
_get_stratified_song_subset,
get_job_result_safely,
- _perform_single_clustering_iteration
+ _perform_single_clustering_iteration,
+ _shuffle_playlist_songs,
+ _assign_playlist_chunks,
+ _try_ai_name_playlist,
)
# Import post-processing functions from dedicated module
from .clustering_postprocessing import (
@@ -399,15 +401,12 @@ def _log_and_update(message, progress, details_to_add_or_update=None, task_state
# --- 2. Batch Job Orchestration ---
num_total_batches = (num_clustering_runs + ITERATIONS_PER_BATCH_JOB - 1) // ITERATIONS_PER_BATCH_JOB if ITERATIONS_PER_BATCH_JOB > 0 else 0
next_batch_to_launch = 0
- batches_completed_count = 0
# STATE RECOVERY
child_tasks_from_db = get_child_tasks_from_db(current_task_id)
if child_tasks_from_db:
logger.info(f"Found {len(child_tasks_from_db)} existing child tasks. Attempting state recovery.")
_monitor_and_process_batches(_main_task_accumulated_details, current_task_id, initial_check=True)
- # Count batches processed during recovery (these are now in processed_job_ids)
- batches_completed_count = len(_main_task_accumulated_details.get('processed_job_ids', set()))
# Determine next batch to launch based on total runs accounted for
runs_accounted_for = _main_task_accumulated_details["runs_completed"]
@@ -540,12 +539,14 @@ def _log_and_update(message, progress, details_to_add_or_update=None, task_state
ollama_model_name_param,
openai_server_url_param, openai_model_name_param, openai_api_key_param,
gemini_api_key_param, gemini_model_name_param,
- mistral_api_key_param, mistral_model_name_param,
- enable_clustering_embeddings_param
+ mistral_api_key_param, mistral_model_name_param
)
- _log_and_update("Deleting existing automatic playlists...", 97)
- delete_automatic_playlists()
+ if CLUSTERING_CLEANING:
+ _log_and_update("Deleting existing automatic playlists...", 97)
+ delete_automatic_playlists()
+ else:
+ _log_and_update("CLUSTERING_CLEANING is disabled — skipping deletion of existing automatic playlists.", 97)
# *** ABSOLUTE FINAL SHUFFLE: Guarantee random order right before database storage ***
logger.info("=== ABSOLUTE FINAL SHUFFLE: Randomizing all playlists before database storage ===")
@@ -905,13 +906,12 @@ def _launch_batch_job(state_dict, parent_task_id, batch_idx, total_runs, genre_m
logger.info(f"Enqueued batch job {new_job.id} for runs {start_run}-{start_run + num_iterations - 1}.")
-def _name_and_prepare_playlists(best_result, ai_provider, ollama_url, ollama_model, openai_url, openai_model, openai_key, gemini_key, gemini_model, mistral_key, mistral_model, embeddings_used):
+def _name_and_prepare_playlists(best_result, ai_provider, ollama_url, ollama_model, openai_url, openai_model, openai_key, gemini_key, gemini_model, mistral_key, mistral_model):
"""
Uses AI to name playlists and formats them for creation.
Returns a dictionary mapping final playlist names to lists of song tuples (id, title, author).
"""
final_playlists = {}
- centroids = best_result.get("playlist_centroids", {})
named_playlists = best_result.get("named_playlists", {})
max_songs = best_result.get("parameters", {}).get("max_songs_per_cluster", MAX_SONGS_PER_CLUSTER)
@@ -919,36 +919,22 @@ def _name_and_prepare_playlists(best_result, ai_provider, ollama_url, ollama_mod
if not songs:
continue
- final_name = original_name
- if ai_provider in ["OLLAMA", "OPENAI", "GEMINI", "MISTRAL"]:
+ if ai_provider in ("OLLAMA", "OPENAI", "GEMINI", "MISTRAL"):
try:
- # Simplified feature extraction for AI prompt
- name_parts = original_name.split('_')
- feature1 = name_parts[0] if len(name_parts) > 0 else "Music"
- feature2 = name_parts[1] if len(name_parts) > 1 else "Vibes"
- feature3 = name_parts[2] if len(name_parts) > 2 else "Collection"
- if embeddings_used:
- feature1, feature2, feature3 = "Vibe", "Focused", "Collection"
-
- ai_config = {
- 'provider': ai_provider,
- 'ollama_url': ollama_url, 'ollama_model': ollama_model,
- 'openai_url': openai_url, 'openai_model': openai_model, 'openai_key': openai_key,
- 'gemini_key': gemini_key, 'gemini_model': gemini_model,
- 'mistral_key': mistral_key, 'mistral_model': mistral_model,
- }
- ai_name = get_ai_playlist_name(
- creative_prompt_template,
- [{'title': s_title, 'author': s_author} for _, s_title, s_author in songs],
- centroids.get(original_name, {}),
- ai_config,
+ final_name = _try_ai_name_playlist(
+ original_name, songs,
+ best_result.get("playlist_centroids", {}),
+ ai_provider,
+ ollama_url, ollama_model,
+ openai_url, openai_model, openai_key,
+ gemini_key, gemini_model,
+ mistral_key, mistral_model,
)
- if ai_name and "Error" not in ai_name:
- final_name = ai_name.strip().replace("\n", " ")
- else:
- logger.warning(f"AI naming failed for '{original_name}': {ai_name}. Using original name.")
except Exception as e:
logger.warning(f"AI naming failed for '{original_name}': {e}. Using original name.")
+ final_name = original_name
+ else:
+ final_name = original_name
# Ensure unique names
temp_name = final_name
@@ -958,34 +944,9 @@ def _name_and_prepare_playlists(best_result, ai_provider, ollama_url, ollama_mod
temp_name = f"{final_name} ({suffix})"
final_name = temp_name
- # Add suffix and handle chunking
- base_name_with_suffix = f"{final_name}_automatic"
-
- # The 'songs' variable is already the list of tuples: [(item_id, title, author), ...]
- # *** FINAL SAFETY SHUFFLE: Ensure songs are randomized in final playlists ***
- final_songs = songs.copy()
- n = len(final_songs)
-
- if n > 1:
- # FISHER-YATES MANUAL SHUFFLE - GUARANTEED TO RANDOMIZE
- current_time_seed = int(time.time() * 1000000) % 1000000
-
- for i in range(n - 1, 0, -1):
- j = (random.randint(0, i) + current_time_seed + i * 7) % (i + 1)
- final_songs[i], final_songs[j] = final_songs[j], final_songs[i]
- current_time_seed = (current_time_seed * 1103515245 + 12345) % (2**31)
-
- logger.info(f"FINAL FISHER-YATES SHUFFLE applied to '{base_name_with_suffix}': {len(final_songs)} songs")
- logger.info(f"FINAL ORDER: First song = '{final_songs[0][1]}', Last song = '{final_songs[-1][1]}'")
- else:
- logger.info(f"FINAL: '{base_name_with_suffix}' has only {n} songs - no shuffling needed")
-
- if max_songs > 0 and len(final_songs) > max_songs:
- chunks = [final_songs[i:i+max_songs] for i in range(0, len(final_songs), max_songs)]
- for idx, chunk in enumerate(chunks, 1):
- final_playlists[f"{base_name_with_suffix} ({idx})"] = chunk # Store the chunk of tuples
- else:
- final_playlists[base_name_with_suffix] = final_songs # Store the list of tuples
+ base_name = f"{final_name}_automatic"
+ shuffled = _shuffle_playlist_songs(songs, base_name)
+ _assign_playlist_chunks(shuffled, max_songs, base_name, final_playlists)
return final_playlists
diff --git a/tasks/clustering_helper.py b/tasks/clustering_helper.py
index bfdfd4fb..3ce5e236 100644
--- a/tasks/clustering_helper.py
+++ b/tasks/clustering_helper.py
@@ -3,6 +3,7 @@
import json
import random
import logging
+import time
import numpy as np
from collections import defaultdict
# time, re, and cdist imports moved to clustering_postprocessing.py
@@ -38,6 +39,65 @@
USE_GPU_CLUSTERING)
from .commons import score_vector
+# Import AI naming for playlist helpers
+from tasks.ai.api import get_ai_playlist_name
+from tasks.ai.prompts import creative_prompt_template
+
+
+# --- Playlist Naming & Shuffling Helpers ---
+
+def _shuffle_playlist_songs(songs, playlist_name):
+ """Fisher-Yates shuffle a list of song tuples; log the result."""
+ final_songs = songs.copy()
+ n = len(final_songs)
+ if n <= 1:
+ logger.info("FINAL: '%s' has only %d songs - no shuffling needed", playlist_name, n)
+ return final_songs
+
+ current_time_seed = int(time.time() * 1000000) % 1000000
+ for i in range(n - 1, 0, -1):
+ j = (random.randint(0, i) + current_time_seed + i * 7) % (i + 1)
+ final_songs[i], final_songs[j] = final_songs[j], final_songs[i]
+ current_time_seed = (current_time_seed * 1103515245 + 12345) % (2 ** 31)
+
+ logger.info("FINAL FISHER-YATES SHUFFLE applied to '%s': %d songs", playlist_name, len(final_songs))
+ logger.info("FINAL ORDER: First song = '%s', Last song = '%s'", final_songs[0][1], final_songs[-1][1])
+ return final_songs
+
+
+def _assign_playlist_chunks(final_songs, max_songs, base_name, final_playlists):
+ """Chunk oversized playlists or store the list as-is."""
+ if max_songs > 0 and len(final_songs) > max_songs:
+ chunks = [final_songs[i:i + max_songs] for i in range(0, len(final_songs), max_songs)]
+ for idx, chunk in enumerate(chunks, 1):
+ final_playlists[f"{base_name} ({idx})"] = chunk
+ else:
+ final_playlists[base_name] = final_songs
+
+
+def _try_ai_name_playlist(original_name, songs, centroids, ai_provider,
+ ollama_url, ollama_model, openai_url, openai_model, openai_key,
+ gemini_key, gemini_model, mistral_key, mistral_model):
+ """Attempt AI naming; return the original name on failure."""
+ ai_config = {
+ 'provider': ai_provider,
+ 'ollama_url': ollama_url, 'ollama_model': ollama_model,
+ 'openai_url': openai_url, 'openai_model': openai_model, 'openai_key': openai_key,
+ 'gemini_key': gemini_key, 'gemini_model': gemini_model,
+ 'mistral_key': mistral_key, 'mistral_model': mistral_model,
+ }
+ ai_name = get_ai_playlist_name(
+ creative_prompt_template,
+ [{'title': s_title, 'author': s_author} for _, s_title, s_author in songs],
+ centroids.get(original_name, {}),
+ ai_config,
+ )
+ if ai_name and "Error" not in ai_name:
+ return ai_name.strip().replace("\n", " ")
+ logger.warning("AI naming failed for '%s': %s. Using original name.", original_name, ai_name)
+ return original_name
+
+
# --- Main Orchestrator for a Single Iteration ---
def _perform_single_clustering_iteration(
diff --git a/tasks/index_build_helpers.py b/tasks/index_build_helpers.py
index 871a6055..a4ab18ab 100644
--- a/tasks/index_build_helpers.py
+++ b/tasks/index_build_helpers.py
@@ -880,7 +880,7 @@ def load_segmented_blob(
row = cur.fetchone()
if row and row[0]:
data = row[0]
- return bytes(data) if not isinstance(data, (bytes, bytearray)) else bytes(data)
+ return bytes(data)
cur.execute(select_segments_sql, (like_pattern,))
rows = cur.fetchall()
diff --git a/tasks/mediaserver_emby.py b/tasks/mediaserver_emby.py
index d962950d..39bd57f1 100644
--- a/tasks/mediaserver_emby.py
+++ b/tasks/mediaserver_emby.py
@@ -541,19 +541,15 @@ def _select_best_artist(item, title="Unknown"):
if item.get('ArtistItems') and len(item['ArtistItems']) > 0:
track_artist = item['ArtistItems'][0].get('Name', 'Unknown Artist')
artist_id = item['ArtistItems'][0].get('Id')
- used_field = 'ArtistItems[0]'
elif item.get('Artists') and len(item['Artists']) > 0:
track_artist = item['Artists'][0] # Take first artist if multiple
artist_id = None
- used_field = 'Artists[0]'
elif item.get('AlbumArtist'):
track_artist = item['AlbumArtist']
artist_id = None
- used_field = 'AlbumArtist'
else:
track_artist = 'Unknown Artist'
artist_id = None
- used_field = 'fallback'
return track_artist, artist_id
diff --git a/tasks/mediaserver_jellyfin.py b/tasks/mediaserver_jellyfin.py
index 205fec52..42663b23 100644
--- a/tasks/mediaserver_jellyfin.py
+++ b/tasks/mediaserver_jellyfin.py
@@ -298,19 +298,15 @@ def _select_best_artist(item, title="Unknown"):
if item.get('ArtistItems') and len(item['ArtistItems']) > 0:
track_artist = item['ArtistItems'][0].get('Name', 'Unknown Artist')
artist_id = item['ArtistItems'][0].get('Id')
- used_field = 'ArtistItems[0]'
elif item.get('Artists') and len(item['Artists']) > 0:
track_artist = item['Artists'][0] # Take first artist if multiple
artist_id = None
- used_field = 'Artists[0]'
elif item.get('AlbumArtist'):
track_artist = item['AlbumArtist']
artist_id = None
- used_field = 'AlbumArtist'
else:
track_artist = 'Unknown Artist'
artist_id = None
- used_field = 'fallback'
return track_artist, artist_id
diff --git a/tasks/mediaserver_lyrion.py b/tasks/mediaserver_lyrion.py
index 5e0288a3..e3f8e6f1 100644
--- a/tasks/mediaserver_lyrion.py
+++ b/tasks/mediaserver_lyrion.py
@@ -711,22 +711,16 @@ def get_all_songs(user_creds=None):
# Prioritize track artist over album artist to avoid "Various Artists"
if song.get('trackartist'):
track_artist = song.get('trackartist')
- used_field = 'trackartist'
elif song.get('contributor'):
track_artist = song.get('contributor')
- used_field = 'contributor'
elif song.get('artist'):
track_artist = song.get('artist')
- used_field = 'artist'
elif song.get('albumartist'):
track_artist = song.get('albumartist')
- used_field = 'albumartist'
elif song.get('band'):
track_artist = song.get('band')
- used_field = 'band'
else:
track_artist = 'Unknown Artist'
- used_field = 'fallback'
mapped_song = {
'Id': song.get('id'),
@@ -1058,22 +1052,16 @@ def get_tracks_from_album(album_id, user_creds=None):
# Prioritize track artist over album artist to avoid "Various Artists"
if s.get('trackartist'):
artist = s.get('trackartist')
- used_field = 'trackartist'
elif s.get('contributor'):
artist = s.get('contributor')
- used_field = 'contributor'
elif s.get('artist'):
artist = s.get('artist')
- used_field = 'artist'
elif s.get('albumartist'):
artist = s.get('albumartist')
- used_field = 'albumartist'
elif s.get('band'):
artist = s.get('band')
- used_field = 'band'
else:
artist = 'Unknown Artist'
- used_field = 'fallback'
path = s.get('url') or s.get('Path') or s.get('path') or ''
mapped.append({
@@ -1109,22 +1097,16 @@ def get_top_played_songs(limit):
# Prioritize track artist over album artist to avoid "Various Artists"
if s.get('trackartist'):
track_artist = s.get('trackartist')
- used_field = 'trackartist'
elif s.get('contributor'):
track_artist = s.get('contributor')
- used_field = 'contributor'
elif s.get('artist'):
track_artist = s.get('artist')
- used_field = 'artist'
elif s.get('albumartist'):
track_artist = s.get('albumartist')
- used_field = 'albumartist'
elif s.get('band'):
track_artist = s.get('band')
- used_field = 'band'
else:
track_artist = 'Unknown Artist'
- used_field = 'fallback'
mapped_songs.append({
'Id': s.get('id'),
diff --git a/tasks/mediaserver_navidrome.py b/tasks/mediaserver_navidrome.py
index 2a865041..86eeadde 100644
--- a/tasks/mediaserver_navidrome.py
+++ b/tasks/mediaserver_navidrome.py
@@ -278,15 +278,12 @@ def _select_best_artist(song_item, title="Unknown"):
if song_item.get('artist'):
track_artist = song_item['artist']
artist_id = song_item.get('artistId')
- used_field = 'artist'
elif song_item.get('albumArtist'):
track_artist = song_item['albumArtist']
artist_id = song_item.get('albumArtistId')
- used_field = 'albumArtist'
else:
track_artist = 'Unknown Artist'
artist_id = None
- used_field = 'fallback'
return track_artist, artist_id
diff --git a/tasks/memory_utils.py b/tasks/memory_utils.py
index e159209b..06cb3aad 100644
--- a/tasks/memory_utils.py
+++ b/tasks/memory_utils.py
@@ -304,8 +304,6 @@ def comprehensive_memory_cleanup(force_cuda: bool = True, reset_onnx_pool: bool
# Final garbage collection + return the freed heap to the OS so RSS drops
results['malloc_trim'] = release_memory_to_os()
- successful_cleanups = sum(results.values())
- total_methods = len([k for k, v in {'cuda': force_cuda, 'onnx_pool': reset_onnx_pool, 'gc': True, 'malloc_trim': True}.items() if v])
return results
diff --git a/tasks/voyager_manager.py b/tasks/voyager_manager.py
index de05cfb8..82f87bd0 100644
--- a/tasks/voyager_manager.py
+++ b/tasks/voyager_manager.py
@@ -1077,7 +1077,6 @@ def _accept_at_index(i):
best_i = None
best_score = float('inf')
- best_d = None
for i in avail_idxs:
meta = items[i]
@@ -1123,7 +1122,6 @@ def _accept_at_index(i):
if score < best_score:
best_score = score
best_i = i
- best_d = dist_prev
if best_i is None:
break
@@ -1168,7 +1166,7 @@ def _accept_at_index(i):
start_id = None
if bi == 0:
start_id = playlist_ids[0] if playlist_ids else None
- sub = _walk_single_bucket(bi, start_item_id=start_id)
+ _walk_single_bucket(bi, start_item_id=start_id)
processed_buckets += 1
if len(playlist_ids) >= n:
break
diff --git a/templates/alchemy.html b/templates/alchemy.html
index 20fe7d00..c64ba49d 100644
--- a/templates/alchemy.html
+++ b/templates/alchemy.html
@@ -729,8 +729,7 @@ Saved Anchors
if (isNaN(temperature) && cfg && cfg.alchemy_temperature) temperature = cfg.alchemy_temperature;
const payload = { items: valid, n, temperature };
- if (metric === 'angular') payload.subtract_distance = parseFloat(subtractSlider.value);
- else payload.subtract_distance = parseFloat(subtractSlider.value);
+ payload.subtract_distance = parseFloat(subtractSlider.value);
const resp = await fetch("{{ url_for('alchemy_bp.alchemy_api') }}", { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(payload) });
const data = await resp.json();
diff --git a/templates/waveform.html b/templates/waveform.html
index ae17f511..f38a6c0f 100644
--- a/templates/waveform.html
+++ b/templates/waveform.html
@@ -116,7 +116,7 @@ Track Title
const waveformCanvas = document.getElementById('waveform-canvas');
const trackTitle = document.getElementById('track-title');
const trackArtist = document.getElementById('track-artist');
- const trackAlbum = document.getElementById('track-album');
+ let trackAlbum = document.getElementById('track-album');
const statusDiv = document.getElementById('status');
// --- Autocomplete Logic ---
diff --git a/test/test_gpu_status.py b/test/test_gpu_status.py
index 824eb102..584d5650 100644
--- a/test/test_gpu_status.py
+++ b/test/test_gpu_status.py
@@ -85,7 +85,7 @@ def test_onnx_runtime():
if has_cuda:
try:
# Create a minimal test to verify CUDA actually works
- sess_options = ort.SessionOptions()
+ ort.SessionOptions()
# Just check that we can request CUDA without error
print_result("CUDA EP available", True, "CUDAExecutionProvider ready")
except Exception as e:
diff --git a/tests/unit/test_app_analysis.py b/tests/unit/test_app_analysis.py
index ff283ce6..0caa966f 100644
--- a/tests/unit/test_app_analysis.py
+++ b/tests/unit/test_app_analysis.py
@@ -339,7 +339,7 @@ def test_analysis_handles_enqueue_failure(
# Should raise exception (Flask will handle with 500)
with pytest.raises(Exception):
- response = client.post('/api/analysis/start', json={})
+ client.post('/api/analysis/start', json={})
@patch('app_helper.get_active_main_task', return_value={'task_id': 'existing-cleaning-123', 'status': 'STARTED', 'task_type': 'cleaning'})
@patch('app_helper.rq_queue_high')
@@ -369,7 +369,7 @@ def test_cleaning_handles_enqueue_failure(
# Should raise exception (Flask will handle with 500)
with pytest.raises(Exception):
- response = client.post('/api/cleaning/start')
+ client.post('/api/cleaning/start')
class TestBlueprintIntegration:
diff --git a/tests/unit/test_app_chat.py b/tests/unit/test_app_chat.py
index 33e82f37..fa6577e5 100644
--- a/tests/unit/test_app_chat.py
+++ b/tests/unit/test_app_chat.py
@@ -16,7 +16,6 @@ def test_song_similarity_empty_title_rejected(self):
# This test validates the logic without calling the full endpoint
# It tests the rejection criteria: title must be non-empty
title = ""
- artist = "Artist"
# Check if title passes validation
is_valid = bool(title.strip())
@@ -24,7 +23,6 @@ def test_song_similarity_empty_title_rejected(self):
def test_song_similarity_empty_artist_rejected(self):
"""song_similarity with empty artist should be skipped."""
- title = "Song"
artist = ""
# Check if artist passes validation
diff --git a/tests/unit/test_mcp_server.py b/tests/unit/test_mcp_server.py
index 22ba9c83..17fa8d1a 100644
--- a/tests/unit/test_mcp_server.py
+++ b/tests/unit/test_mcp_server.py
@@ -614,11 +614,6 @@ def test_search_database_energy_conversion(self):
"energy_max": 0.8
}, {})
- # Check the raw energy values passed to the query function
- if mock_query.called:
- kwargs = mock_query.call_args[1] if mock_query.call_args[1] else {}
- args = mock_query.call_args[0] if mock_query.call_args[0] else ()
- # energy should have been converted from 0-1 to raw
finally:
cfg.ENERGY_MIN = orig_min
cfg.ENERGY_MAX = orig_max
@@ -657,7 +652,7 @@ def test_exact_match_case_insensitive(self):
mock_voyager.find_nearest_neighbors_by_id = mock_nn
with patch.object(mod, 'get_db_connection', return_value=conn), \
patch.dict(sys.modules, {'tasks.voyager_manager': mock_voyager}):
- result = mod._song_similarity_api_sync("bohemian rhapsody", "queen", 10)
+ mod._song_similarity_api_sync("bohemian rhapsody", "queen", 10)
# Should have tried a DB lookup
assert cur.execute.called
@@ -893,7 +888,7 @@ def test_get_songs_limits_results(self):
with patch.object(mod, 'get_db_connection', return_value=conn), \
patch.dict(sys.modules, {'tasks.artist_gmm_manager': gmm_mod}):
- result = mod._artist_similarity_api_sync("Coldplay", count=5, get_songs=5)
+ mod._artist_similarity_api_sync("Coldplay", count=5, get_songs=5)
execute_calls = cur.execute.call_args_list
for c in execute_calls:
diff --git a/tests/unit/test_mediaserver.py b/tests/unit/test_mediaserver.py
index 9305d436..2d9da000 100644
--- a/tests/unit/test_mediaserver.py
+++ b/tests/unit/test_mediaserver.py
@@ -1597,8 +1597,8 @@ def test_calls_playlists_delete_command(self, mock_request):
mock_request.return_value = {'count': 1}
- result = delete_playlist('playlist-123')
-
+ delete_playlist('playlist-123')
+
call_args = mock_request.call_args
# First arg is command
assert call_args[0][0] == 'playlists', \
@@ -1998,7 +1998,7 @@ def test_handles_standalone_track_pseudo_albums(self, mock_config, mock_get):
mock_get.return_value = mock_response
# Call with pseudo-album ID
- tracks = get_tracks_from_album('standalone_real-track-id')
+ get_tracks_from_album('standalone_real-track-id')
# Should fetch the track directly, not list children
call_url = mock_get.call_args[0][0]
diff --git a/tests/unit/test_memory_cleanup.py b/tests/unit/test_memory_cleanup.py
index e3e1d912..e0613824 100644
--- a/tests/unit/test_memory_cleanup.py
+++ b/tests/unit/test_memory_cleanup.py
@@ -115,7 +115,7 @@ def test_no_cleanup_with_album_sessions(
# Call with pre-loaded sessions
with patch('tasks.analysis.cleanup_onnx_session') as mock_cleanup:
- result = analyze_track(
+ analyze_track(
"/tmp/test.mp3",
["happy", "sad"],
{
@@ -209,7 +209,7 @@ def test_cleanup_all_models_in_finally(
mock_clap_loaded.return_value = True
# Call function (should complete successfully)
- result = analyze_album_task("album_123", "Empty Album", 5, None)
+ analyze_album_task("album_123", "Empty Album", 5, None)
# Verify all cleanup functions were called
assert mock_memory_cleanup.called
@@ -277,7 +277,7 @@ def test_cleanup_onnx_sessions_on_success(
# Call function
with patch('tasks.clap_analyzer.is_clap_available', return_value=False):
- result = analyze_album_task("album_123", "Test Album", 5, None)
+ analyze_album_task("album_123", "Test Album", 5, None)
# Verify session cleanup was called for all loaded sessions
# Should be called 2 times (embedding + prediction; secondary models removed in v4.0.0)
diff --git a/tests/unit/test_playlist_ordering.py b/tests/unit/test_playlist_ordering.py
index 0d97e85d..c041321f 100644
--- a/tests/unit/test_playlist_ordering.py
+++ b/tests/unit/test_playlist_ordering.py
@@ -112,7 +112,7 @@ def test_empty_input(self):
def test_minimum_songs_no_ordering(self):
"""3+ songs with len <= 2 orderable → return input unchanged."""
- mod = _load_playlist_ordering()
+ _load_playlist_ordering()
# This simulates the case where we have 3 songs but fewer than 3 with DB data
# Since the function checks if len(orderable_ids) <= 2 and returns early,
# we verify this behavior by checking the algorithm logic itself.
diff --git a/tests/unit/test_provider_migration_execute.py b/tests/unit/test_provider_migration_execute.py
index 678ba93c..a08286c9 100644
--- a/tests/unit/test_provider_migration_execute.py
+++ b/tests/unit/test_provider_migration_execute.py
@@ -314,10 +314,6 @@ def test_voyager_id_map_rewrite_happens(self, mig):
# The migration should have issued an UPDATE voyager_index_data after rewriting
# (we can't easily verify the exact JSON written without capturing params, but
# we can verify the statement was executed)
- executed_upper = '\n'.join(
- s.upper() for s in mig._get_dedicated_conn.return_value.cursor.return_value.execute.call_args_list
- if isinstance(s, str)
- )
# Fallback: walk the call_args_list and check sql strings
calls = mig._get_dedicated_conn.return_value.cursor.return_value.execute.call_args_list
sqls = [c[0][0] for c in calls]
diff --git a/tests/unit/test_sem_grove_manager.py b/tests/unit/test_sem_grove_manager.py
index db14f959..8c6dbf92 100644
--- a/tests/unit/test_sem_grove_manager.py
+++ b/tests/unit/test_sem_grove_manager.py
@@ -510,7 +510,6 @@ def fake_stream(table, column, dim, where_clause=None, **kwargs):
whitening_json = whitening_row[1] # id_map_json column holds whitening stats
index_binary = index_row[0]
index_idmap = index_row[1]
- index_dim = index_row[2]
# Patch _load_sem_grove_index_from_db to use stored bytes directly
def fake_load():
@@ -525,7 +524,6 @@ def fake_load():
w_a = float(whitening["w_audio"])
ld_ = int(whitening["lyrics_dim"])
ad_ = int(whitening["audio_dim"])
- merged = ld_ + ad_
stream = io.BytesIO(index_binary)
loaded = _voyager.Index.load(stream)
diff --git a/windows/embedded_pg.py b/windows/embedded_pg.py
index 68ea22a0..b2bcd17e 100644
--- a/windows/embedded_pg.py
+++ b/windows/embedded_pg.py
@@ -146,7 +146,6 @@ def stop():
if _running_proc is None:
return
data_dir = paths.pgdata_dir()
- port = str(paths.pg_port())
logger.info("Stopping PostgreSQL")
try:
subprocess.run(