feat: integrate training UI, BEATs model, and clean up legacy code

- Remove legacy distance-mode scanning (build_profile, _similarity, etc.)
  and hand-crafted intensity features — pipeline is now embedding-only
- Integrate Microsoft BEATs as embedding option alongside wav2vec2/HuBERT
- Add TrainDialog with positive class selector, model picker, video dir
  fallback, and live training stats
- Add TrainWorker QThread with cancel support and proper lifecycle cleanup
- Add source_path column to DB for robust source video tracking
- Add get_export_folders/get_training_data/get_training_stats to DB
- Wire source_path in all export DB writes (_on_clip_done, _on_auto_clip_done)
- Cancel scan/train workers in closeEvent to prevent use-after-free crashes
- Add setup_env.sh supporting both conda and python venv (CUDA 12.8)
- Update requirements.txt with all actual dependencies
- Update 8cut_train.py with --positive flag for new DB-driven training

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

8cut_train.py

@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+"""Train an audio scan classifier from DB ground truth.
+
+Usage:
+    python 8cut_train.py                                    # default model, auto-detect positive
+    python 8cut_train.py --model BEATS                      # specific embedding model
+    python 8cut_train.py --positive mp4_Intense                 # explicit positive folder
+    python 8cut_train.py --positive mp4_Intense --model BEATS   # both
+"""
+import sys, os, warnings
+sys.path.insert(0, os.path.dirname(__file__))
+warnings.filterwarnings("ignore")
+
+from core.audio_scan import train_classifier, default_model_path, _EMBED_MODELS
+from core.db import ProcessedDB
+
+PROFILE_NAME = "JAV_missionary"
+
+# Fallback for old DB rows without source_path
+PLEX_DIR = "/media/unraid/appdata/plex/download/porn_jav/"
+
+
+def main():
+    embed_model = None
+    if "--model" in sys.argv:
+        idx = sys.argv.index("--model")
+        if idx + 1 < len(sys.argv):
+            embed_model = sys.argv[idx + 1]
+            if embed_model not in _EMBED_MODELS:
+                print(f"Unknown model: {embed_model}")
+                print(f"Available: {', '.join(_EMBED_MODELS)}")
+                sys.exit(1)
+
+    positive_suffix = None
+    if "--positive" in sys.argv:
+        idx = sys.argv.index("--positive")
+        if idx + 1 < len(sys.argv):
+            positive_suffix = sys.argv[idx + 1]
+
+    db = ProcessedDB()
+
+    # If --positive given, use the new DB helper
+    if positive_suffix:
+        video_infos = db.get_training_data(
+            PROFILE_NAME, positive_suffix, fallback_video_dir=PLEX_DIR,
+        )
+        if not video_infos:
+            print(f"No training data found for positive='{positive_suffix}'")
+            sys.exit(1)
+    else:
+        # Legacy fallback: classify by folder path pattern
+        rows = db._con.execute(
+            "SELECT filename, start_time, output_path, source_path"
+            " FROM processed WHERE profile = ?",
+            (PROFILE_NAME,),
+        ).fetchall()
+
+        intense_by_video, soft_by_video = {}, {}
+        source_by_fn = {}
+        for fn, st, op, sp in rows:
+            if sp:
+                source_by_fn[fn] = sp
+            if "/mp4_Intense/" in op or "_Intense/" in op:
+                intense_by_video.setdefault(fn, set()).add(st)
+            elif "/mp4_Soft/" in op or "_Soft/" in op:
+                soft_by_video.setdefault(fn, set()).add(st)
+
+        video_infos = []
+        for fn in intense_by_video:
+            # Try source_path from DB first, fall back to PLEX_DIR
+            vpath = source_by_fn.get(fn) or os.path.join(PLEX_DIR, fn)
+            if not os.path.exists(vpath):
+                print(f"  skip (not found): {fn}")
+                continue
+            gt_intense = sorted(intense_by_video[fn])
+            gt_soft = sorted(soft_by_video.get(fn, set()))
+            video_infos.append((vpath, gt_intense, gt_soft))
+
+    label = embed_model or "WAV2VEC2_BASE"
+    print(f"Training {label} model on {len(video_infos)} videos...")
+    model_path = default_model_path(PROFILE_NAME)
+    result = train_classifier(
+        video_infos, model_path=model_path, embed_model=embed_model,
+    )
+    if result is None:
+        print("Training failed: no valid samples or missing class balance")
+        sys.exit(1)
+    print(f"Model saved to {model_path}")
+
+
+if __name__ == "__main__":
+    main()