Qnet song match — fetcha Songs.txt iz 5 MB playerjev (FOLX DE/SLO, ZWEI, ONE, ADRIA), 20K+ songs, fuzzy match na upload-u → clean parsed_artist/parsed_title + auto tv_station. /api/qnet/{stats,match,sync}
This commit is contained in:
OpenClaw Agent
parent
6f79aaea8d
commit
b938d1e4d8
@ -30,7 +30,7 @@ COPY templates/ ./templates/
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RUN mkdir -p ./static
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# Data direktorij (Coolify bo prek Persistent Storage UI bind-al volume)
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RUN mkdir -p /data/uploads /data/outputs /data/jobs /data/cookies
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RUN mkdir -p /data/uploads /data/outputs /data/jobs /data/cookies /data/qnet
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ENV DATA_DIR=/data
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ENV PYTHONUNBUFFERED=1
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87
app/main.py
87
app/main.py
@ -43,11 +43,17 @@ DATA_DIR = Path(os.environ.get("DATA_DIR", "/data"))
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UPLOAD_DIR = DATA_DIR / "uploads"
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OUTPUT_DIR = DATA_DIR / "outputs"
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JOBS_DIR = DATA_DIR / "jobs"
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QNET_DIR = DATA_DIR / "qnet"
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SCRIPTS_DIR = Path(__file__).parent.parent / "scripts"
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UPLOAD_DIR.mkdir(parents=True, exist_ok=True)
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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JOBS_DIR.mkdir(parents=True, exist_ok=True)
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QNET_DIR.mkdir(parents=True, exist_ok=True)
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# Qnet song match — povezava z MB player bazami
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os.environ.setdefault("QNET_LOOKUP_PATH", str(QNET_DIR / "songs_lookup.json"))
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from app import qnet_match # noqa: E402
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# Dedup DB — sledi že obdelanim/naloženim komadom
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DEDUP_DB = DATA_DIR / "processed.db"
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@ -1280,20 +1286,38 @@ async def upload_video(
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if batch_id:
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job["batch_id"] = batch_id
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# Artist + title — najprej user-provided, potem parse iz filename
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# Artist + title — najprej user-provided, potem Qnet match, potem parse iz filename
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if artist and title:
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# User je vpisal ali potrdil
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job["parsed_artist"] = artist.strip()
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job["parsed_title"] = title.strip()
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job["has_clean_name"] = True
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else:
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# Auto parse iz filename
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a, t = parse_artist_title(file.filename)
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if a:
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job["parsed_artist"] = a
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if t:
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job["parsed_title"] = t
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job["has_clean_name"] = bool(a and t)
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# 1) Probaj match proti Qnet bazi (clean Artist+Title direct iz playlistov)
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qm = qnet_match.match_filename(file.filename)
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if qm["matched"] and qm["confidence"] >= 0.85:
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job["parsed_artist"] = qm["artist"]
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job["parsed_title"] = qm["title"]
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job["has_clean_name"] = True
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# Če station ni že nastavljen ročno, vzemi iz Qnet match-a
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if not job.get("tv_station"):
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job["tv_station"] = qm["station"]
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job["qnet_match"] = {
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"method": qm["method"],
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"confidence": qm["confidence"],
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"matched_file": qm["file"],
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"matched_station": qm["station"],
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}
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print(f"🎯 Qnet match [{qm['method']}, {qm['confidence']}]: "
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f"{qm['station']} — {qm['artist']} — {qm['title']}", flush=True)
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else:
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# 2) Fallback: filename parser (regex-based)
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a, t = parse_artist_title(file.filename)
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if a:
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job["parsed_artist"] = a
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if t:
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job["parsed_title"] = t
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job["has_clean_name"] = bool(a and t)
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save_job(job)
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return job
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@ -1733,6 +1757,53 @@ async def get_transcript(job_id: str, user: str = Depends(check_auth)):
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raise HTTPException(500, f"Napaka pri branju: {e}")
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# ─── Qnet song match (MB player baza) ────────────────────────────
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@app.get("/api/qnet/stats")
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async def qnet_stats(user: str = Depends(check_auth)):
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"""Statistika Qnet baze (koliko songov, koliko star, po postajah)."""
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return qnet_match.db_stats()
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@app.get("/api/qnet/match")
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async def qnet_match_filename(filename: str, user: str = Depends(check_auth)):
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"""Test endpoint — vrne match result za poljuben filename."""
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if not filename:
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raise HTTPException(400, "filename query param required")
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return qnet_match.match_filename(filename)
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@app.post("/api/qnet/sync")
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async def qnet_sync(background: BackgroundTasks, user: str = Depends(check_auth)):
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"""Sproži sync (fetch Songs.txt iz vseh playerjev). Async background task."""
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sync_script = SCRIPTS_DIR / "sync_qnet.py"
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if not sync_script.exists():
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raise HTTPException(500, f"sync_qnet.py ne obstaja v {SCRIPTS_DIR}")
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def run_sync():
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try:
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import subprocess
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env = os.environ.copy()
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env["QNET_DB_PATH"] = str(QNET_DIR / "songs.json")
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env["QNET_LOOKUP_PATH"] = str(QNET_DIR / "songs_lookup.json")
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proc = subprocess.run(
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["python3", str(sync_script)],
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env=env,
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capture_output=True,
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text=True,
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timeout=300,
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)
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print(f"[qnet sync] exit={proc.returncode}", flush=True)
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if proc.stdout:
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print(f"[qnet sync] stdout:\n{proc.stdout}", flush=True)
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if proc.stderr:
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print(f"[qnet sync] stderr:\n{proc.stderr}", flush=True)
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except Exception as e:
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print(f"[qnet sync] error: {e}", flush=True)
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background.add_task(run_sync)
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return {"started": True, "lookup_path": str(QNET_DIR / "songs_lookup.json")}
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class RecutRequest(BaseModel):
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start: float
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end: float
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247
app/qnet_match.py
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247
app/qnet_match.py
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@ -0,0 +1,247 @@
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"""
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Qnet match modul — iz uploaded filename najde matching song v Qnet bazah.
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Vrne (artist, title, station, confidence, matched_file).
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Strategija:
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1. **Exact filename match** (case-insensitive, with normalized punctuation)
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— stem only, brez extension. Confidence: 1.0.
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2. **Normalized basename match** — odstrani noise besede (Official, HD, 4K,
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letnice, številke v oklepajih), normaliziraj presledke, primerjaj.
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Confidence: 0.9.
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3. **Artist+Title fuzzy match** — najprej parsa filename z obstoječim
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parse_artist_title, potem išče v bazi po normalized (artist|title)
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bigram-keywords. Confidence: 0.6–0.85 odvisno od ratio.
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4. Če ni zadetka → None.
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Vrne lookup result dict:
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{
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"matched": bool,
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"method": "exact" | "normalized" | "fuzzy" | None,
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"confidence": float, # 0.0–1.0
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"artist": str,
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"title": str,
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"station": str,
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"file": str,
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}
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"""
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import json
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import re
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import os
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import time
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import unicodedata
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from pathlib import Path
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from typing import Optional
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from difflib import SequenceMatcher
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LOOKUP_PATH = Path(os.environ.get("QNET_LOOKUP_PATH", "/data/qnet/songs_lookup.json"))
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# In-memory cache — re-load if file is newer than cache
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_cache: dict = {"mtime": 0.0, "songs": [], "by_norm_file": {}, "by_norm_stem": {}}
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def _norm(s: str) -> str:
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"""Aggressive normalize: lowercase, strip diacritics, kill punctuation."""
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if not s:
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return ""
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s = unicodedata.normalize("NFKD", s)
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s = "".join(c for c in s if not unicodedata.combining(c))
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s = s.lower()
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# Replace en-dash / em-dash / various separators with hyphen
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s = s.replace("–", "-").replace("—", "-").replace("|", "-")
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# Drop everything except alnum and space
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s = re.sub(r"[^a-z0-9\s]+", " ", s)
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s = re.sub(r"\s+", " ", s).strip()
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return s
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# Noise words pogosto v "Official Video" / YouTube ipd.
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_NOISE_WORDS = {
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"official", "officiel", "video", "musikvideo", "musicvideo", "music",
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"lyric", "lyrics", "audio", "hd", "4k", "8k", "uhd", "live", "remix",
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"remaster", "remastered", "version", "ver", "extended", "edit",
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"videoclip", "clip", "premiere", "premiera", "tv", "final", "promo",
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"1080p", "720p", "2160p",
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}
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def _norm_minus_noise(s: str) -> str:
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"""Norm + remove noise words + drop standalone digits 2/3/4-digit (years/versions)."""
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n = _norm(s)
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toks = []
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for t in n.split():
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if t in _NOISE_WORDS:
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continue
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if t.isdigit() and len(t) <= 4:
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continue
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toks.append(t)
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return " ".join(toks)
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def _load_db():
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"""Re-load lookup file if changed. Uses mtime check."""
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global _cache
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try:
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mtime = LOOKUP_PATH.stat().st_mtime
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except FileNotFoundError:
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return False
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if mtime <= _cache["mtime"]:
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return True
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songs = json.loads(LOOKUP_PATH.read_text(encoding="utf-8"))
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by_norm_file = {} # normalized whole filename → song
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by_norm_stem = {} # normalized stem (no ext) → song
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by_norm_minus = {} # normalized minus noise → song
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for s in songs:
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f = s.get("file") or ""
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if not f:
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continue
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nf = _norm(f)
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ns = _norm(Path(f).stem)
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nm = _norm_minus_noise(Path(f).stem)
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# Prvi zadetek wins (po prvi station ki ima ta file — kasneje lahko
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# menjamo na "all stations match")
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by_norm_file.setdefault(nf, s)
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by_norm_stem.setdefault(ns, s)
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if nm:
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by_norm_minus.setdefault(nm, s)
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_cache = {
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"mtime": mtime,
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"songs": songs,
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"by_norm_file": by_norm_file,
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"by_norm_stem": by_norm_stem,
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"by_norm_minus": by_norm_minus,
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}
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print(f"[qnet_match] loaded {len(songs)} songs from {LOOKUP_PATH} (mtime={mtime})", flush=True)
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return True
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def _result(song: dict, method: str, confidence: float) -> dict:
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return {
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"matched": True,
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"method": method,
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"confidence": round(confidence, 3),
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"artist": song.get("artist", ""),
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"title": song.get("title", ""),
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"station": song.get("station", ""),
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"file": song.get("file", ""),
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}
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def _no_match() -> dict:
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return {
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"matched": False,
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"method": None,
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"confidence": 0.0,
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"artist": "",
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"title": "",
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"station": "",
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"file": "",
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}
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def match_filename(filename: str) -> dict:
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"""Najdi match za uploaded filename. Glavna API funkcija."""
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if not filename:
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return _no_match()
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if not _load_db():
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return _no_match() # baza ni naložena
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stem = Path(filename).stem
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nf_full = _norm(filename)
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nf_stem = _norm(stem)
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nm_stem = _norm_minus_noise(stem)
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# 1) Exact normalized match z extensionom
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if nf_full in _cache["by_norm_file"]:
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return _result(_cache["by_norm_file"][nf_full], "exact", 1.0)
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# 2) Stem match (brez extension)
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if nf_stem in _cache["by_norm_stem"]:
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return _result(_cache["by_norm_stem"][nf_stem], "exact_stem", 0.95)
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# 3) Stem match brez noise
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if nm_stem and nm_stem in _cache["by_norm_minus"]:
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return _result(_cache["by_norm_minus"][nm_stem], "normalized", 0.9)
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# 4) Fuzzy substring — če se input vsebuje v song.file ali obratno (samo če dovolj dolgo)
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# Najprej probaj parse "Artist - Title" iz filename
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parsed_a, parsed_t = _parse_artist_title_simple(stem)
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if parsed_a and parsed_t:
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cand = _fuzzy_artist_title(parsed_a, parsed_t)
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if cand:
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return cand
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# 5) Last-resort fuzzy — sequence ratio na stem proti vsem stem-om (slow but bounded)
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# Samo če stem >= 8 znakov, da ne dobimo random match-ev
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if len(nm_stem) >= 8:
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best = None
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best_ratio = 0.0
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for cand_stem, song in _cache["by_norm_minus"].items():
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ratio = SequenceMatcher(None, nm_stem, cand_stem).ratio()
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if ratio > best_ratio:
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best_ratio = ratio
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best = song
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if best and best_ratio >= 0.85:
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return _result(best, "fuzzy", best_ratio * 0.85)
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return _no_match()
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def _parse_artist_title_simple(name: str):
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"""Lightweight parser za 'Artist - Title' / 'Artist – Title'."""
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if not name:
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return (None, None)
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# Probaj najprej en/em-dash (najbolj distinctive), potem hyphen
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for sep in [" – ", " — ", " - ", "_-_"]:
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if sep in name:
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parts = name.split(sep, 1)
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if len(parts) == 2:
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a = parts[0].strip()
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t = parts[1].strip()
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if a and t and len(a) <= 80 and len(t) <= 100:
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return (a, t)
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return (None, None)
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def _fuzzy_artist_title(artist: str, title: str) -> Optional[dict]:
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"""Najdi pesem v bazi po normalized artist+title fuzzy match."""
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na = _norm_minus_noise(artist)
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nt = _norm_minus_noise(title)
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if not na or not nt:
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return None
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best = None
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best_score = 0.0
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for song in _cache["songs"]:
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sa = _norm_minus_noise(song.get("artist", ""))
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st = _norm_minus_noise(song.get("title", ""))
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if not sa or not st:
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continue
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ar = SequenceMatcher(None, na, sa).ratio()
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tr = SequenceMatcher(None, nt, st).ratio()
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score = (ar + tr) / 2
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if score > best_score:
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best_score = score
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best = song
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if best and best_score >= 0.82:
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return _result(best, "fuzzy_at", best_score * 0.9)
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return None
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def db_stats() -> dict:
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"""Vrne statistiko baze za health check / admin endpoint."""
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_load_db()
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if not _cache["songs"]:
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return {"loaded": False, "total": 0, "stations": {}}
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by_station = {}
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for s in _cache["songs"]:
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st = s.get("station", "?")
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by_station[st] = by_station.get(st, 0) + 1
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return {
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"loaded": True,
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"total": len(_cache["songs"]),
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"stations": by_station,
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"mtime": _cache["mtime"],
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"age_seconds": time.time() - _cache["mtime"],
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}
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186
scripts/sync_qnet.py
Normal file
186
scripts/sync_qnet.py
Normal file
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#!/usr/bin/env python3
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"""
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Qnet baz fetcher za reels-app.
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Fetcha Songs.txt iz 5 Qnet instalacij na MB playerjih (preko ssh-api proxy-ja
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na openclaw → SSH na Windows playerje), pretvori iz Windows-1252 v UTF-8,
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parsa TSV in shrani enotno JSON bazo v /data/qnet/songs.json.
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Cron-friendly: poženi enkrat na uro.
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Output struktura:
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{
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"synced_at": 1746198000.0,
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"stations": {
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"FOLX DE": {"count": 4038, "fetched_at": 1746198000.0},
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...
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},
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"songs": [
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{
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"station": "FOLX DE",
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"artist": "Sašo Avsenik und seine Oberkrainer",
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"title": "Na Golici",
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"file": "Sašo Avsenik und seine Oberkrainer - Na Golici.mp4",
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"type": "DGL",
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"length": "2:32.277",
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"comments": "",
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"last_played": "17/4/2026"
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},
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...
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]
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}
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"""
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import csv
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import io
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import json
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import os
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import sys
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import time
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import base64
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import requests
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from pathlib import Path
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SSH_API = os.environ.get("PTC_SSH_API", "https://mail.folx.tv/ssh-api/v2")
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SSH_TOKEN = os.environ.get("PTC_SSH_TOKEN") or "ptc-ssh-2026-a7b3c9d4e5f6012389abcdef01234567890abcdef01234567890abcdef012345"
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OUT_PATH = Path(os.environ.get("QNET_DB_PATH", "/data/qnet/songs.json"))
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OUT_PATH.parent.mkdir(parents=True, exist_ok=True)
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|
||||
# (station_label, player_ip, qnet_subdir_on_C)
|
||||
STATIONS = [
|
||||
("FOLX DE", "100.64.0.2", "qnet"),
|
||||
("ZWEI", "100.64.0.2", "qnetzwei"),
|
||||
("ONE", "100.64.0.3", "QnetONE"),
|
||||
("ADRIA", "100.64.0.4", "Qnet"),
|
||||
("FOLX SLO", "100.64.0.4", "QnetFOLXSLO"),
|
||||
]
|
||||
|
||||
SSH_KEY = "/root/.ssh/players/folx_players"
|
||||
|
||||
|
||||
def ssh_exec(cmd: str, timeout: int = 60) -> dict:
|
||||
"""Pošlji ukaz preko ssh-api na openclaw."""
|
||||
r = requests.post(
|
||||
SSH_API,
|
||||
headers={
|
||||
"Authorization": f"Bearer {SSH_TOKEN}",
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
json={"host": "openclaw", "cmd": cmd, "timeout": timeout},
|
||||
timeout=timeout + 30,
|
||||
)
|
||||
r.raise_for_status()
|
||||
return r.json()
|
||||
|
||||
|
||||
def fetch_one(station: str, ip: str, subdir: str) -> str:
|
||||
"""Fetcha Songs.txt z windows playerja, vrne UTF-8 string."""
|
||||
# 1) scp z playerja na openclaw, iconv v utf8, base64 nazaj
|
||||
cmd = (
|
||||
f"set -e; "
|
||||
f"TMP=$(mktemp); "
|
||||
f"scp -i {SSH_KEY} -o StrictHostKeyChecking=no "
|
||||
f'"folxadmin@{ip}:c:/{subdir}/Data/Songs.txt" "$TMP"; '
|
||||
f'iconv -f WINDOWS-1252 -t UTF-8 "$TMP" | base64 -w 0; '
|
||||
f'rm -f "$TMP"'
|
||||
)
|
||||
res = ssh_exec(cmd, timeout=90)
|
||||
if res.get("exit_code") != 0:
|
||||
raise RuntimeError(f"{station}: ssh-api error: {res}")
|
||||
b64 = res.get("output", "").strip()
|
||||
if not b64:
|
||||
raise RuntimeError(f"{station}: empty response")
|
||||
return base64.b64decode(b64).decode("utf-8", errors="replace")
|
||||
|
||||
|
||||
def parse_songs_tsv(text: str, station: str) -> list[dict]:
|
||||
"""Parse TSV → list of clean dicts. Drop incomplete rows."""
|
||||
out = []
|
||||
reader = csv.DictReader(io.StringIO(text), delimiter="\t")
|
||||
for row in reader:
|
||||
artist = (row.get("Artist") or "").strip()
|
||||
title = (row.get("Title") or "").strip()
|
||||
file_ = (row.get("File") or "").strip()
|
||||
# Skip popolnoma prazne vrstice
|
||||
if not (artist or title or file_):
|
||||
continue
|
||||
out.append({
|
||||
"station": station,
|
||||
"artist": artist,
|
||||
"title": title,
|
||||
"file": file_,
|
||||
"type": (row.get("Type") or "").strip(),
|
||||
"length": (row.get("Length") or "").strip(),
|
||||
"comments": (row.get("Comments") or "").strip(),
|
||||
"language": (row.get("Language") or "").strip(),
|
||||
"genre": (row.get("Genre") or "").strip(),
|
||||
"last_played": (row.get("Last date played") or "").strip(),
|
||||
"display_artist": (row.get("Display artist") or "").strip(),
|
||||
"display_title": (row.get("Display title") or "").strip(),
|
||||
})
|
||||
return out
|
||||
|
||||
|
||||
def main():
|
||||
t0 = time.time()
|
||||
all_songs = []
|
||||
stations_meta = {}
|
||||
errors = []
|
||||
|
||||
for station, ip, subdir in STATIONS:
|
||||
try:
|
||||
print(f"→ {station} ({ip}:c:/{subdir}/Data/Songs.txt)", flush=True)
|
||||
text = fetch_one(station, ip, subdir)
|
||||
songs = parse_songs_tsv(text, station)
|
||||
all_songs.extend(songs)
|
||||
stations_meta[station] = {
|
||||
"count": len(songs),
|
||||
"fetched_at": time.time(),
|
||||
"ok": True,
|
||||
}
|
||||
print(f" ✓ {len(songs)} songov", flush=True)
|
||||
except Exception as e:
|
||||
err = f"{station}: {type(e).__name__}: {e}"
|
||||
print(f" ✗ {err}", flush=True)
|
||||
errors.append(err)
|
||||
stations_meta[station] = {"count": 0, "ok": False, "error": str(e)}
|
||||
|
||||
# Zapiši na disk (atomic preko temp + rename)
|
||||
payload = {
|
||||
"synced_at": time.time(),
|
||||
"duration_seconds": round(time.time() - t0, 1),
|
||||
"total_songs": len(all_songs),
|
||||
"stations": stations_meta,
|
||||
"errors": errors,
|
||||
"songs": all_songs,
|
||||
}
|
||||
|
||||
tmp = OUT_PATH.with_suffix(".json.tmp")
|
||||
tmp.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
|
||||
tmp.replace(OUT_PATH)
|
||||
|
||||
# Tudi ločen "lookup index" — manjši fajl samo za matching
|
||||
lookup = []
|
||||
for s in all_songs:
|
||||
if s["artist"] and s["title"]:
|
||||
lookup.append({
|
||||
"station": s["station"],
|
||||
"artist": s["artist"],
|
||||
"title": s["title"],
|
||||
"file": s["file"],
|
||||
})
|
||||
lookup_path = OUT_PATH.parent / "songs_lookup.json"
|
||||
tmp2 = lookup_path.with_suffix(".json.tmp")
|
||||
tmp2.write_text(json.dumps(lookup, ensure_ascii=False), encoding="utf-8")
|
||||
tmp2.replace(lookup_path)
|
||||
|
||||
print(f"\n✓ Done: {len(all_songs)} songov v {OUT_PATH} ({round(time.time()-t0,1)}s)")
|
||||
if errors:
|
||||
print(f"⚠ {len(errors)} napak:")
|
||||
for e in errors:
|
||||
print(f" - {e}")
|
||||
sys.exit(1 if len(errors) == len(STATIONS) else 0)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in New Issue
Block a user