Sebastjan Artič
b543057cee
Changes: 1. UI: removed blocking prompt() that asked for artist+title on filename that didn't match 'Artist - Title' pattern. Upload always proceeds. Instead shows yellow warning saying 'server will try to recognize'. 2. Backend: added scripts/acr_recognize.py — extracts 20s audio sample from video (at 15s and 60s offsets for robustness), computes ACRCloud fingerprint via native binary (3KB payload), sends to identify API. 3. Pipeline: process_job() now runs ACR recognition step before analysis IF parsed_artist or parsed_title is missing. Result is saved to job metadata and used for download filename + Scribe/Claude filename hint. 4. Credentials: ACR_HOST + ACR_ACCESS_KEY + ACR_SECRET_KEY env vars added to Coolify (using existing keys from openclaw fb-agent metka). 5. requirements.txt: added pyacrcloud==1.0.11 for native fingerprinting. This unblocks future automation/cron upload pipelines — files don't need to be perfectly named, ACRCloud will identify them automatically. Fallback chain: 1. Filename parsing (Artist - Title.mp4) 2. ACRCloud audio fingerprint (works even for '12345.mp4', 'IMG_001.mp4') 3. If both fail: download filename uses 'reel_<id>.mp4' (still works)
915 lines
37 KiB
Python
915 lines
37 KiB
Python
"""
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reels.biba.live — FastAPI backend.
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Endpoints:
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GET / — frontend HTML
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POST /api/upload — naloži video file
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POST /api/youtube — submit YouTube URL
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POST /api/process/{id} — start processing job
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GET /api/jobs — list vseh jobov
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GET /api/jobs/{id} — status job-a
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GET /api/stream/{id} — SSE progress stream
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GET /api/download/{id} — download finalni reel
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GET /api/preview/{id} — preview video stream
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DELETE /api/jobs/{id} — pobriši job + datoteke
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"""
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import asyncio
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import json
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import os
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import secrets
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import shutil
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import subprocess
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import time
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import uuid
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from pathlib import Path
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from typing import Optional
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from fastapi import (
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FastAPI, UploadFile, File, Form, HTTPException, Depends,
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BackgroundTasks, Request, status
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)
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from fastapi.responses import (
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FileResponse, HTMLResponse, StreamingResponse, JSONResponse, Response
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)
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from fastapi.staticfiles import StaticFiles
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from fastapi.security import HTTPBasic, HTTPBasicCredentials
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from pydantic import BaseModel
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# ────────────────────────────────────────────────────────────────
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# Config
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# ────────────────────────────────────────────────────────────────
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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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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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AUTH_USER = os.environ.get("AUTH_USER", "sebastjan")
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AUTH_PASS = os.environ.get("AUTH_PASS", "change-me-in-coolify-env")
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MAX_UPLOAD_MB = int(os.environ.get("MAX_UPLOAD_MB", "2000"))
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# ────────────────────────────────────────────────────────────────
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# Auth
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# ────────────────────────────────────────────────────────────────
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security = HTTPBasic()
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def check_auth(creds: HTTPBasicCredentials = Depends(security)):
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correct_user = secrets.compare_digest(creds.username, AUTH_USER)
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correct_pass = secrets.compare_digest(creds.password, AUTH_PASS)
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if not (correct_user and correct_pass):
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raise HTTPException(
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status_code=status.HTTP_401_UNAUTHORIZED,
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detail="Napačno geslo",
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headers={"WWW-Authenticate": "Basic"},
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)
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return creds.username
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# ────────────────────────────────────────────────────────────────
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# Artist + title parsing iz filename / YouTube title
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# ────────────────────────────────────────────────────────────────
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import re
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_NOISE_PATTERNS = [
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# Pogosti "noise" ki ga je treba odstraniti
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r"\(Official\s+(?:Music\s+)?Video\)",
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r"\(Officia[lk]\s+Audio\)",
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r"\(Offizielles\s+(?:Musik)?[Vv]ideo\)",
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r"\(Lyric[s]?\s+Video\)",
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r"\(Audio\)",
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r"\(HD\)", r"\(HQ\)", r"\(4K\)",
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r"\(Live\)", r"\(Remix\)",
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r"\(Remastered\)", r"\(Remaster(?:ed)?\s*\d{0,4}\)",
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r"\[Official.*?\]", r"\[Music.*?\]", r"\[Audio.*?\]",
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r"\bofficial\s+video\b", r"\bofficial\s+audio\b",
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r"\boriginal\s+(?:video|audio)\b",
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r"\bMV\b", r"\b4K\b", r"\bHD\b", r"\bHQ\b",
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]
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def parse_artist_title(filename_or_title):
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"""Iz imena datoteke / YouTube naslova ekstrahira (artist, title).
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Podpira pogoste vzorce:
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- "Artist - Title.mp4"
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- "Artist - Title (Official Music Video).mp4"
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- "Artist – Title" (em-dash)
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- "Artist | Title"
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Vrne (artist, title) ali (None, None) če ni razvidno.
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"""
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if not filename_or_title:
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return (None, None)
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# Odstrani extension
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name = Path(filename_or_title).stem if "." in filename_or_title else filename_or_title
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# Odstrani noise patterns
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for pat in _NOISE_PATTERNS:
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name = re.sub(pat, "", name, flags=re.IGNORECASE)
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# Normaliziraj presledke
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name = re.sub(r"\s+", " ", name).strip()
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# Probaj različne separatorje
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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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artist = parts[0].strip()
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title = parts[1].strip()
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# Strip trailing/leading puncutation
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artist = re.sub(r'^[\s\-–—|.:_]+|[\s\-–—|.:_]+$', '', artist)
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title = re.sub(r'^[\s\-–—|.:_]+|[\s\-–—|.:_]+$', '', title)
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if artist and title and len(artist) <= 80 and len(title) <= 100:
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return (artist, title)
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return (None, None)
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def safe_filename(s, max_len=80):
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"""Naredi varno ime datoteke (brez znakov ki bi razbili FS)."""
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if not s:
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return ""
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# Replace problematic chars with safe alternative
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s = re.sub(r'[<>:"/\\|?*\x00-\x1f]', '', s)
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s = re.sub(r'\s+', ' ', s).strip()
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return s[:max_len]
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def build_download_filename(job):
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"""Sestavi pravilno ime download datoteke iz job metadata."""
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# Najprej probaj job-shranjene parsed values
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artist = job.get("parsed_artist")
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title = job.get("parsed_title")
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# Fallback: parse from filename
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if not artist or not title:
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source = job.get("filename") or job.get("youtube_title") or ""
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parsed_artist, parsed_title = parse_artist_title(source)
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artist = artist or parsed_artist
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title = title or parsed_title
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if artist and title:
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return f"{safe_filename(artist)} - {safe_filename(title)} - REEL.mp4"
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if title:
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return f"{safe_filename(title)} - REEL.mp4"
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# Last resort: job ID (vendar to bi se moralo preprečiti že ob upload-u)
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return f"reel_{job['id']}.mp4"
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# ────────────────────────────────────────────────────────────────
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# Job state (filesystem-based, persistent prek restartov)
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# ────────────────────────────────────────────────────────────────
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def job_path(job_id):
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return JOBS_DIR / f"{job_id}.json"
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def load_job(job_id):
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p = job_path(job_id)
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if not p.exists():
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return None
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return json.loads(p.read_text())
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def save_job(job):
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job_path(job["id"]).write_text(json.dumps(job, ensure_ascii=False, indent=2))
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def update_job(job_id, **kwargs):
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job = load_job(job_id)
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if not job:
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return None
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job.update(kwargs)
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job["updated_at"] = time.time()
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save_job(job)
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return job
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def list_jobs():
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out = []
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for f in sorted(JOBS_DIR.glob("*.json"), reverse=True):
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try:
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out.append(json.loads(f.read_text()))
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except Exception:
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pass
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return out
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def generate_srt_from_segments(segments, clip_start, clip_end, output_path):
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"""Generira SRT samo za dele, ki spadajo v [clip_start, clip_end].
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Timestamp-i so re-mapirani na 0-based (kot je v trim-anem videu).
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Razdeli dolge segmente (>2.5s) na enake kose za hiter pacing v reels stilu.
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Vse besedilo VELIKE TISKANE ČRKE.
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"""
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MAX_CHUNK_DURATION = 2.5
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def fmt_ts(s):
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h = int(s // 3600)
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m = int((s % 3600) // 60)
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sec = s % 60
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return f"{h:02d}:{m:02d}:{sec:06.3f}".replace(".", ",")
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lines = []
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idx = 1
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for seg in segments:
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s_start = float(seg["start"])
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s_end = float(seg["end"])
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text = str(seg["text"]).strip()
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# Filter v range
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if s_end <= clip_start or s_start >= clip_end:
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continue
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# Klipni
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s_start = max(s_start, clip_start)
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s_end = min(s_end, clip_end)
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if s_end - s_start < 0.2:
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continue
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# Re-mapraj na 0-based
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rel_start = s_start - clip_start
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rel_end = s_end - clip_start
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if not text:
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continue
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text_upper = text.upper()
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# Razdeli na chunk-e če je predolg
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duration = rel_end - rel_start
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if duration <= MAX_CHUNK_DURATION:
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lines.append(f"{idx}\n{fmt_ts(rel_start)} --> {fmt_ts(rel_end)}\n{text_upper}\n")
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idx += 1
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else:
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# Razdeli na N enakih kosov; če ima Whisper word-timing, jih lahko razdelimo bolje,
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# ampak za zdaj enako razdelimo
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n_parts = int(duration / MAX_CHUNK_DURATION) + 1
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words = text_upper.split()
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words_per_part = max(1, len(words) // n_parts)
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chunk_dur = duration / n_parts
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for i in range(n_parts):
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cs = rel_start + i * chunk_dur
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ce = rel_start + (i + 1) * chunk_dur
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# Vzemi pripadajoče besede
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wstart = i * words_per_part
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wend = (i + 1) * words_per_part if i < n_parts - 1 else len(words)
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chunk_text = " ".join(words[wstart:wend]) if wstart < len(words) else text_upper
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if not chunk_text.strip():
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chunk_text = text_upper
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lines.append(f"{idx}\n{fmt_ts(cs)} --> {fmt_ts(ce)}\n{chunk_text.strip()}\n")
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idx += 1
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with open(output_path, "w", encoding="utf-8") as f:
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f.write("\n".join(lines))
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return output_path
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# ────────────────────────────────────────────────────────────────
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# Pipeline runner (background task)
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# ────────────────────────────────────────────────────────────────
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def run_subprocess_logged(cmd, job_id, step_name):
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"""Pokliče subprocess, logi gredo v job."""
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update_job(job_id, current_step=step_name, status="processing")
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proc = subprocess.run(cmd, capture_output=True, text=True)
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if proc.returncode != 0:
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# Combine stdout + stderr za diagnostiko
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err_msg = (proc.stderr or "") + "\n" + (proc.stdout or "")
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update_job(
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job_id,
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status="failed",
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error=f"{step_name}: {err_msg[-800:].strip()}",
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)
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return False
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# Tudi pri success-u beleži stderr za diagnostiko (samo zadnji del)
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if proc.stderr and proc.stderr.strip():
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update_job(job_id, last_step_log=proc.stderr[-500:].strip())
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return True
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def process_job(job_id):
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"""Glavni pipeline: download (če YT) → find_chorus (če auto) → reframe → subs."""
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job = load_job(job_id)
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if not job:
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return
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try:
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# ── 1. Source preparation ─────────────────────────────
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if job["source_type"] == "youtube":
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update_job(job_id, status="downloading", current_step="YouTube download")
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input_path = UPLOAD_DIR / f"{job_id}_yt.mp4"
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cmd = [
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"python3", str(SCRIPTS_DIR / "yt_download.py"),
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job["youtube_url"], str(input_path),
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]
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if not run_subprocess_logged(cmd, job_id, "YouTube download"):
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return
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update_job(job_id, input_path=str(input_path))
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# Probaj dobiti YT naslov za artist+title parsing
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try:
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info_cmd = [
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"python3", str(SCRIPTS_DIR / "yt_download.py"),
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job["youtube_url"], "/dev/null", "--info-only",
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]
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proc = subprocess.run(info_cmd, capture_output=True, text=True, timeout=30)
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if proc.returncode == 0 and proc.stdout:
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info = json.loads(proc.stdout)
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yt_title = info.get("title", "")
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if yt_title:
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a, t = parse_artist_title(yt_title)
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updates = {"youtube_title": yt_title}
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if a:
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updates["parsed_artist"] = a
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if t:
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updates["parsed_title"] = t
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updates["has_clean_name"] = bool(a and t)
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update_job(job_id, **updates)
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# Reload job for downstream use
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job = load_job(job_id)
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except Exception as e:
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print(f"⚠️ Cannot fetch YT title: {e}", flush=True)
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else:
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input_path = Path(job["input_path"])
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# ── 1b. Music recognition (ACRCloud) — če nimamo artist+title ─────
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# Tudi za YouTube jobs lahko naslov ni razviden (npr. iz playliste, "Track 5")
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if not (job.get("parsed_artist") and job.get("parsed_title")):
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update_job(job_id, current_step="Avto-prepoznavam pesem (ACRCloud)")
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try:
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acr_cmd = [
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"python3", str(SCRIPTS_DIR / "acr_recognize.py"),
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str(input_path),
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]
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proc = subprocess.run(acr_cmd, capture_output=True, text=True, timeout=120)
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if proc.returncode == 0 and proc.stdout:
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data = json.loads(proc.stdout)
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a, t = data.get("artist"), data.get("title")
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if a and t:
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update_job(
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job_id,
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parsed_artist=a, parsed_title=t,
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has_clean_name=True,
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recognized_via="acrcloud",
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)
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job = load_job(job_id)
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print(f"✅ ACR prepoznal: {a} - {t}", flush=True)
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else:
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print(f"⚠️ ACR ni prepoznal pesmi", flush=True)
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else:
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print(f"⚠️ ACR exit {proc.returncode}: {proc.stderr[:200]}", flush=True)
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except Exception as e:
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print(f"⚠️ ACR error: {e}", flush=True)
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# ── 2. Smart analysis (če auto_chorus) ──────────────────────────
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if job.get("auto_chorus"):
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update_job(job_id, current_step="Analiza pesmi (transkript + energija)")
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analysis_path = OUTPUT_DIR / f"{job_id}.analysis.json"
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cmd = [
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"python3", str(SCRIPTS_DIR / "analyze.py"),
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str(input_path),
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"--target-duration", str(job.get("duration", 30)),
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"--max-duration", str(job.get("max_duration", 45)),
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"--min-duration", str(job.get("min_duration", 20)),
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"--output", str(analysis_path),
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]
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if job.get("include_prebuild"):
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cmd += ["--include-prebuild"]
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# LLM provider (claude/gemini/auto)
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if job.get("llm_provider"):
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cmd += ["--llm-provider", job["llm_provider"]]
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if job.get("llm_model"):
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cmd += ["--llm-model", job["llm_model"]]
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# Filename hint za Claude/Scribe — preferiraj parsed artist+title (čistejše)
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if job.get("parsed_artist") and job.get("parsed_title"):
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fn_hint = f"{job['parsed_artist']} - {job['parsed_title']}"
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cmd += ["--filename-hint", fn_hint]
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elif job.get("filename"):
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fn_hint = Path(job["filename"]).stem
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cmd += ["--filename-hint", fn_hint]
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# STT provider (elevenlabs = Scribe, local = faster-whisper, auto = preferiraj Scribe)
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if job.get("whisper_provider"):
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cmd += ["--whisper-provider", job["whisper_provider"]]
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# lang: če None ali 'auto', pusti analyze.py auto-detect
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if job.get("lang") and job["lang"] not in ("auto", ""):
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cmd += ["--lang", job["lang"]]
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cmd += ["--model", job.get("whisper_model", "large-v3")]
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proc = subprocess.run(cmd, capture_output=True, text=True)
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srt_from_claude = None # Pot do SRT iz Claude-popravljenega transcript-a
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if proc.returncode == 0 and analysis_path.exists():
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try:
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with open(analysis_path, "r", encoding="utf-8") as f:
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analysis = json.load(f)
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cr = analysis["clip_range"]
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fade = analysis["fade"]
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# Generiraj SRT iz transcript-a TRIM-ANEGA na clip_range
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# (Claude je morda popravil besedilo — uporabi popravljeno)
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if analysis.get("transcript", {}).get("segments"):
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srt_path_out = OUTPUT_DIR / f"{job_id}.subtitles.srt"
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try:
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generate_srt_from_segments(
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analysis["transcript"]["segments"],
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cr["start"], cr["end"],
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srt_path_out,
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)
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srt_from_claude = str(srt_path_out)
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llm_src = cr.get("source", "LLM")
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print(f"📝 Generated SRT from {llm_src} transcript: {srt_path_out}")
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except Exception as e:
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print(f"⚠️ SRT generation failed: {e}")
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update_job(
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job_id,
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analysis_summary={
|
||
"language": analysis["language"],
|
||
"language_probability": analysis["language_probability"],
|
||
"instrumental": analysis["instrumental"],
|
||
"clip_range": cr,
|
||
"fade": fade,
|
||
"chorus_preview": analysis["chorus"]["best"]["text_preview"]
|
||
if analysis.get("chorus") and analysis["chorus"].get("best") else None,
|
||
"video_duration": analysis.get("video_duration"),
|
||
"candidates": analysis["chorus"].get("all_candidates", [])[:5]
|
||
if analysis.get("chorus") else [],
|
||
"claude_corrected_text": analysis.get("claude_corrected_text", False),
|
||
},
|
||
# Cel transkript shranimo za UI prikaz
|
||
full_transcript=[
|
||
{"start": s["start"], "end": s["end"], "text": s["text"]}
|
||
for s in analysis.get("transcript", {}).get("segments", [])
|
||
],
|
||
start=cr["start"],
|
||
duration=cr["duration"],
|
||
fade_in=fade["fade_in"],
|
||
fade_out=fade["fade_out"],
|
||
detected_language=analysis["language"],
|
||
is_instrumental=analysis["instrumental"],
|
||
claude_srt_path=srt_from_claude,
|
||
)
|
||
# Auto-disable subs za instrumental
|
||
if analysis["instrumental"] and not job.get("no_subs"):
|
||
update_job(job_id, no_subs=True, auto_disabled_subs=True)
|
||
# Reload local dict
|
||
job = load_job(job_id)
|
||
except (json.JSONDecodeError, KeyError) as e:
|
||
update_job(job_id, chorus_error=f"Analysis parse: {e}")
|
||
else:
|
||
update_job(job_id, chorus_error=(proc.stderr or "")[-500:])
|
||
|
||
# ── 3. Reframe + subtitles (clip.py orchestrator) ─────
|
||
output_path = OUTPUT_DIR / f"{job_id}.mp4"
|
||
update_job(job_id, current_step="Reframe + subtitles")
|
||
|
||
cmd = [
|
||
"python3", str(SCRIPTS_DIR / "clip.py"),
|
||
str(input_path), str(output_path),
|
||
"--mode", job.get("mode", "track"),
|
||
"--quality", job.get("quality", "medium"),
|
||
"--style", job.get("subtitle_style", "reels"),
|
||
]
|
||
if job.get("start") is not None:
|
||
cmd += ["--start", str(job["start"])]
|
||
if job.get("duration") is not None:
|
||
cmd += ["--duration", str(job["duration"])]
|
||
if job.get("fade_in", 0) > 0:
|
||
cmd += ["--fade-in", str(job["fade_in"])]
|
||
if job.get("fade_out", 0) > 0:
|
||
cmd += ["--fade-out", str(job["fade_out"])]
|
||
# SRT iz Claude (boljše besedilo) — preda direktno v subtitle.py
|
||
if job.get("claude_srt_path") and Path(job["claude_srt_path"]).exists() and not job.get("no_subs"):
|
||
cmd += ["--srt", job["claude_srt_path"]]
|
||
# lang: prefer detected_language če auto
|
||
chosen_lang = job.get("lang")
|
||
if chosen_lang in (None, "auto", ""):
|
||
chosen_lang = job.get("detected_language")
|
||
if chosen_lang:
|
||
cmd += ["--lang", chosen_lang]
|
||
if job.get("no_subs"):
|
||
cmd += ["--no-subs"]
|
||
cmd += ["--model", job.get("whisper_model", "large-v3")]
|
||
|
||
# DEBUG: zapiši natanko kakšen ukaz se izvede
|
||
update_job(job_id, debug_clip_cmd=" ".join(cmd))
|
||
|
||
if not run_subprocess_logged(cmd, job_id, "Reframe + subtitles"):
|
||
return
|
||
|
||
# ── Done ──────────────────────────────────────────────
|
||
if output_path.exists():
|
||
update_job(
|
||
job_id,
|
||
status="done",
|
||
current_step="Končano",
|
||
output_path=str(output_path),
|
||
output_size_mb=round(output_path.stat().st_size / 1024 / 1024, 2),
|
||
)
|
||
else:
|
||
update_job(
|
||
job_id,
|
||
status="failed",
|
||
error="Output datoteka ne obstaja po obdelavi",
|
||
)
|
||
except Exception as e:
|
||
update_job(job_id, status="failed", error=str(e))
|
||
|
||
|
||
# ────────────────────────────────────────────────────────────────
|
||
# FastAPI app
|
||
# ────────────────────────────────────────────────────────────────
|
||
app = FastAPI(title="Reels Clipper")
|
||
app.mount("/static", StaticFiles(directory=Path(__file__).parent.parent / "static"), name="static")
|
||
|
||
|
||
@app.on_event("startup")
|
||
async def resume_or_cleanup_jobs():
|
||
"""Ob startu containerja: avto-resume processing jobs ali jih označi kot error.
|
||
|
||
Ko Coolify deploya nov container, prejšnji se ubije sredi obdelave,
|
||
JSON file pa ostane status='processing'.
|
||
|
||
Strategija:
|
||
- Če je analyze.json že narejen (analiza je končana) → resume z reframe+subs
|
||
- Če ni analyze.json → restart pipeline od začetka
|
||
- Po 3 napakah (resume_attempts >= 3) → mark error
|
||
"""
|
||
import asyncio
|
||
print("🔄 Preverjam in obnavljam jobs po restart-u...")
|
||
resumed_count = 0
|
||
error_count = 0
|
||
|
||
for f in JOBS_DIR.glob("*.json"):
|
||
try:
|
||
j = json.loads(f.read_text())
|
||
if j.get("status") != "processing":
|
||
continue
|
||
|
||
job_id = j.get("job_id") or f.stem
|
||
attempts = j.get("resume_attempts", 0)
|
||
|
||
# Po 3 neuspehih nehamo
|
||
if attempts >= 3:
|
||
j["status"] = "error"
|
||
j["current_step"] = "Preveč napak pri ponovnem zagonu"
|
||
j["chorus_error"] = f"Job restartal {attempts} krat — napaka v pipeline-u"
|
||
j["updated_at"] = time.time()
|
||
f.write_text(json.dumps(j, ensure_ascii=False, indent=2))
|
||
error_count += 1
|
||
continue
|
||
|
||
# Preveri ali input file še obstaja
|
||
input_path = UPLOAD_DIR / f"{job_id}.mp4"
|
||
if not input_path.exists():
|
||
j["status"] = "error"
|
||
j["current_step"] = "Vhodna datoteka ne obstaja"
|
||
j["chorus_error"] = f"Upload {job_id}.mp4 ne obstaja po restart-u"
|
||
j["updated_at"] = time.time()
|
||
f.write_text(json.dumps(j, ensure_ascii=False, indent=2))
|
||
error_count += 1
|
||
continue
|
||
|
||
# Resume: status=queued, +1 attempt, in pošlji v background
|
||
j["status"] = "queued"
|
||
j["resume_attempts"] = attempts + 1
|
||
j["current_step"] = f"Avto-resume po restart-u (poskus {attempts + 1}/3)"
|
||
j["last_resume_at"] = time.time()
|
||
j["updated_at"] = time.time()
|
||
f.write_text(json.dumps(j, ensure_ascii=False, indent=2))
|
||
resumed_count += 1
|
||
# Pošlji v background po startup-u (ne smemo blokirati startup)
|
||
asyncio.create_task(_resume_job_async(job_id))
|
||
print(f" 🔁 Resume {job_id} (attempt {attempts + 1}/3)")
|
||
except Exception as e:
|
||
print(f" ⚠️ Napaka pri {f.name}: {e}")
|
||
|
||
print(f" ✅ Resumed: {resumed_count}, Error: {error_count}")
|
||
|
||
|
||
async def _resume_job_async(job_id):
|
||
"""Pomožna funkcija ki zažene process_job v background-u."""
|
||
import asyncio
|
||
# Počakaj kratek čas da je startup končan
|
||
await asyncio.sleep(2)
|
||
try:
|
||
# process_job je sync funkcija, izvedi v thread executor
|
||
loop = asyncio.get_event_loop()
|
||
await loop.run_in_executor(None, process_job, job_id)
|
||
except Exception as e:
|
||
print(f" ❌ Resume failed for {job_id}: {e}")
|
||
|
||
|
||
@app.get("/", response_class=HTMLResponse)
|
||
async def index(user: str = Depends(check_auth)):
|
||
html = (Path(__file__).parent.parent / "templates" / "index.html").read_text()
|
||
return html
|
||
|
||
|
||
@app.get("/healthz")
|
||
async def healthz():
|
||
return {"ok": True}
|
||
|
||
|
||
# ────────────────────────────────────────────────────────────────
|
||
# Job models
|
||
# ────────────────────────────────────────────────────────────────
|
||
class YouTubeJobIn(BaseModel):
|
||
url: str
|
||
mode: str = "track"
|
||
lang: Optional[str] = None
|
||
auto_chorus: bool = True
|
||
start: Optional[float] = None
|
||
duration: Optional[float] = 30
|
||
no_subs: bool = False
|
||
subtitle_style: str = "reels"
|
||
whisper_model: str = "large-v3"
|
||
quality: str = "medium"
|
||
|
||
|
||
class StartJobIn(BaseModel):
|
||
job_id: str
|
||
mode: str = "track"
|
||
lang: Optional[str] = None # None/auto = Whisper auto-detect
|
||
auto_chorus: bool = True
|
||
include_prebuild: bool = False # vključi pre-chorus build-up
|
||
start: Optional[float] = None
|
||
duration: Optional[float] = 30
|
||
max_duration: Optional[float] = 45
|
||
min_duration: Optional[float] = 20
|
||
no_subs: bool = False
|
||
subtitle_style: str = "reels"
|
||
whisper_model: str = "large-v3"
|
||
quality: str = "medium"
|
||
# LLM za semantično analizo + popravke
|
||
llm_provider: str = "claude" # claude / gemini / auto
|
||
llm_model: Optional[str] = None # specifičen model (privzeto najboljši za provider)
|
||
# STT provider (Scribe je 18x hitreje + boljša multilingual accuracy)
|
||
whisper_provider: str = "auto" # auto / elevenlabs / local
|
||
|
||
|
||
# ────────────────────────────────────────────────────────────────
|
||
# Upload (file)
|
||
# ────────────────────────────────────────────────────────────────
|
||
@app.post("/api/upload")
|
||
async def upload_video(
|
||
file: UploadFile = File(...),
|
||
artist: Optional[str] = Form(None),
|
||
title: Optional[str] = Form(None),
|
||
user: str = Depends(check_auth),
|
||
):
|
||
if not file.filename:
|
||
raise HTTPException(400, "Brez imena")
|
||
|
||
job_id = uuid.uuid4().hex[:12]
|
||
ext = Path(file.filename).suffix or ".mp4"
|
||
input_path = UPLOAD_DIR / f"{job_id}{ext}"
|
||
|
||
size = 0
|
||
with input_path.open("wb") as f:
|
||
while chunk := await file.read(1024 * 1024):
|
||
size += len(chunk)
|
||
if size > MAX_UPLOAD_MB * 1024 * 1024:
|
||
f.close()
|
||
input_path.unlink(missing_ok=True)
|
||
raise HTTPException(413, f"Prevelika datoteka (limit {MAX_UPLOAD_MB} MB)")
|
||
f.write(chunk)
|
||
|
||
job = {
|
||
"id": job_id,
|
||
"source_type": "upload",
|
||
"filename": file.filename,
|
||
"input_path": str(input_path),
|
||
"size_mb": round(size / 1024 / 1024, 2),
|
||
"status": "uploaded",
|
||
"current_step": "Naloženo, čaka na obdelavo",
|
||
"created_at": time.time(),
|
||
"updated_at": time.time(),
|
||
}
|
||
|
||
# Artist + title — najprej user-provided, potem parse iz filename
|
||
if artist and title:
|
||
# User je vpisal ali potrdil
|
||
job["parsed_artist"] = artist.strip()
|
||
job["parsed_title"] = title.strip()
|
||
job["has_clean_name"] = True
|
||
else:
|
||
# Auto parse iz filename
|
||
a, t = parse_artist_title(file.filename)
|
||
if a:
|
||
job["parsed_artist"] = a
|
||
if t:
|
||
job["parsed_title"] = t
|
||
job["has_clean_name"] = bool(a and t)
|
||
|
||
save_job(job)
|
||
return job
|
||
|
||
|
||
# ────────────────────────────────────────────────────────────────
|
||
# YouTube submit
|
||
# ────────────────────────────────────────────────────────────────
|
||
@app.post("/api/youtube")
|
||
async def submit_youtube(
|
||
payload: YouTubeJobIn,
|
||
background: BackgroundTasks,
|
||
user: str = Depends(check_auth),
|
||
):
|
||
job_id = uuid.uuid4().hex[:12]
|
||
job = {
|
||
"id": job_id,
|
||
"source_type": "youtube",
|
||
"youtube_url": payload.url,
|
||
"status": "queued",
|
||
"current_step": "V vrsti za YouTube prenos",
|
||
"created_at": time.time(),
|
||
"updated_at": time.time(),
|
||
"mode": payload.mode,
|
||
"lang": payload.lang,
|
||
"auto_chorus": payload.auto_chorus,
|
||
"start": payload.start,
|
||
"duration": payload.duration,
|
||
"no_subs": payload.no_subs,
|
||
"subtitle_style": payload.subtitle_style,
|
||
"whisper_model": payload.whisper_model,
|
||
"quality": payload.quality,
|
||
}
|
||
save_job(job)
|
||
background.add_task(process_job, job_id)
|
||
return job
|
||
|
||
|
||
# ────────────────────────────────────────────────────────────────
|
||
# Start processing for uploaded job
|
||
# ────────────────────────────────────────────────────────────────
|
||
@app.post("/api/process")
|
||
async def start_processing(
|
||
payload: StartJobIn,
|
||
background: BackgroundTasks,
|
||
user: str = Depends(check_auth),
|
||
):
|
||
job = load_job(payload.job_id)
|
||
if not job:
|
||
raise HTTPException(404, "Job ne obstaja")
|
||
|
||
update_job(
|
||
payload.job_id,
|
||
status="queued",
|
||
mode=payload.mode,
|
||
lang=payload.lang,
|
||
auto_chorus=payload.auto_chorus,
|
||
include_prebuild=payload.include_prebuild,
|
||
start=payload.start,
|
||
duration=payload.duration,
|
||
max_duration=payload.max_duration,
|
||
min_duration=payload.min_duration,
|
||
no_subs=payload.no_subs,
|
||
subtitle_style=payload.subtitle_style,
|
||
whisper_model=payload.whisper_model,
|
||
quality=payload.quality,
|
||
llm_provider=payload.llm_provider,
|
||
llm_model=payload.llm_model,
|
||
whisper_provider=payload.whisper_provider,
|
||
current_step="V vrsti za obdelavo",
|
||
# Počisti pretekle napake (retry-friendly)
|
||
chorus_error=None,
|
||
interrupted_at=None,
|
||
)
|
||
background.add_task(process_job, payload.job_id)
|
||
return load_job(payload.job_id)
|
||
|
||
|
||
# ────────────────────────────────────────────────────────────────
|
||
# Job queries
|
||
# ────────────────────────────────────────────────────────────────
|
||
@app.get("/api/jobs")
|
||
async def get_jobs(user: str = Depends(check_auth)):
|
||
return {"jobs": list_jobs()}
|
||
|
||
|
||
@app.get("/api/jobs/{job_id}")
|
||
async def get_job(job_id: str, user: str = Depends(check_auth)):
|
||
job = load_job(job_id)
|
||
if not job:
|
||
raise HTTPException(404, "Ne obstaja")
|
||
return job
|
||
|
||
|
||
@app.get("/api/stream/{job_id}")
|
||
async def stream_job(job_id: str, user: str = Depends(check_auth)):
|
||
"""Server-Sent Events za real-time status."""
|
||
|
||
async def gen():
|
||
last_status = None
|
||
last_step = None
|
||
for _ in range(600): # max 10 min stream
|
||
job = load_job(job_id)
|
||
if not job:
|
||
yield f"data: {json.dumps({'error': 'not found'})}\n\n"
|
||
return
|
||
if job["status"] != last_status or job.get("current_step") != last_step:
|
||
yield f"data: {json.dumps(job, ensure_ascii=False)}\n\n"
|
||
last_status = job["status"]
|
||
last_step = job.get("current_step")
|
||
if job["status"] in ("done", "failed"):
|
||
return
|
||
await asyncio.sleep(1)
|
||
|
||
return StreamingResponse(gen(), media_type="text/event-stream")
|
||
|
||
|
||
# ────────────────────────────────────────────────────────────────
|
||
# Download / preview
|
||
# ────────────────────────────────────────────────────────────────
|
||
@app.get("/api/download/{job_id}")
|
||
async def download(job_id: str, user: str = Depends(check_auth)):
|
||
job = load_job(job_id)
|
||
if not job or job.get("status") != "done":
|
||
raise HTTPException(404, "Ne pripravljen")
|
||
out = Path(job["output_path"])
|
||
if not out.exists():
|
||
raise HTTPException(404, "Output ne obstaja")
|
||
|
||
# Pametno ime: "Izvajalec - Naslov - REEL.mp4"
|
||
download_name = build_download_filename(job)
|
||
|
||
return FileResponse(
|
||
out,
|
||
media_type="video/mp4",
|
||
filename=download_name,
|
||
)
|
||
|
||
|
||
@app.get("/api/preview/{job_id}")
|
||
async def preview(job_id: str, request: Request, user: str = Depends(check_auth)):
|
||
"""Video preview z Range request podporo (potrebno za HTML5 video player)."""
|
||
job = load_job(job_id)
|
||
if not job or job.get("status") != "done":
|
||
raise HTTPException(404, "Ne pripravljen")
|
||
out = Path(job["output_path"])
|
||
if not out.exists():
|
||
raise HTTPException(404, "Output ne obstaja")
|
||
|
||
file_size = out.stat().st_size
|
||
range_header = request.headers.get("range") or request.headers.get("Range")
|
||
|
||
if range_header:
|
||
# Parse "bytes=START-END"
|
||
try:
|
||
range_str = range_header.replace("bytes=", "").strip()
|
||
start_s, end_s = range_str.split("-")
|
||
start = int(start_s) if start_s else 0
|
||
end = int(end_s) if end_s else file_size - 1
|
||
end = min(end, file_size - 1)
|
||
if start > end or start >= file_size:
|
||
return Response(status_code=416) # Range Not Satisfiable
|
||
chunk_size = end - start + 1
|
||
|
||
def iter_file():
|
||
with open(out, "rb") as f:
|
||
f.seek(start)
|
||
remaining = chunk_size
|
||
while remaining > 0:
|
||
read_size = min(64 * 1024, remaining)
|
||
data = f.read(read_size)
|
||
if not data:
|
||
break
|
||
remaining -= len(data)
|
||
yield data
|
||
|
||
headers = {
|
||
"Content-Range": f"bytes {start}-{end}/{file_size}",
|
||
"Accept-Ranges": "bytes",
|
||
"Content-Length": str(chunk_size),
|
||
"Content-Type": "video/mp4",
|
||
}
|
||
return StreamingResponse(iter_file(), status_code=206, headers=headers,
|
||
media_type="video/mp4")
|
||
except (ValueError, IndexError):
|
||
pass
|
||
|
||
# Brez Range — vrni cel file
|
||
return FileResponse(
|
||
out,
|
||
media_type="video/mp4",
|
||
headers={"Accept-Ranges": "bytes", "Content-Length": str(file_size)},
|
||
)
|
||
|
||
|
||
@app.delete("/api/jobs/{job_id}")
|
||
async def delete_job(job_id: str, user: str = Depends(check_auth)):
|
||
job = load_job(job_id)
|
||
if not job:
|
||
raise HTTPException(404, "Ne obstaja")
|
||
for key in ("input_path", "output_path"):
|
||
p = job.get(key)
|
||
if p and Path(p).exists():
|
||
Path(p).unlink(missing_ok=True)
|
||
job_path(job_id).unlink(missing_ok=True)
|
||
return {"deleted": job_id}
|