Sebastjan Artič
68247bb84c
ElevenLabs Scribe replaces local Whisper as default transcription: - 96.7% accuracy English, 2.4% WER Indonesian (vs Whisper 7.7%) - 18x faster (200s song = 11s vs 3-5 min on CPU) - No hallucinations on songs (Whisper invented 'Pony und Kleid' for 'Bonnie und Clyde') - 99 languages supported, including SLO/HR/BS/SR - $0.40/h pricing, ~$0.022 per 200s song Implementation: - transcribe_with_elevenlabs() function uses Scribe v1 - ISO 639-1 ↔ 639-3 mapping (Scribe needs 'deu' not 'de') - Word-level timestamps converted to pseudo-segments (close on 0.6s pause or 6s duration) - 24MB upload limit guard with auto-fallback to local Default whisper_provider='auto': - If ELEVENLABS_API_KEY set → use Scribe - Otherwise → fallback to local faster-whisper - 'elevenlabs' strict mode: no fallback - 'local' strict mode: skip Scribe entirely Tested on Ben Zucker - Ohne dich: Scribe correctly transcribed 'Wir sind Bonnie und Clyde, zu allem bereit' where local Whisper hallucinated.
747 lines
30 KiB
Python
747 lines
30 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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# 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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else:
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input_path = Path(job["input_path"])
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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 = original filename (Claude lahko prepozna pesem)
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if job.get("filename"):
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# Brez extension
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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={
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"language": analysis["language"],
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"language_probability": analysis["language_probability"],
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"instrumental": analysis["instrumental"],
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"clip_range": cr,
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"fade": fade,
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"chorus_preview": analysis["chorus"]["best"]["text_preview"]
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if analysis.get("chorus") and analysis["chorus"].get("best") else None,
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"video_duration": analysis.get("video_duration"),
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"candidates": analysis["chorus"].get("all_candidates", [])[:5]
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if analysis.get("chorus") else [],
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"claude_corrected_text": analysis.get("claude_corrected_text", False),
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},
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# Cel transkript shranimo za UI prikaz
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full_transcript=[
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{"start": s["start"], "end": s["end"], "text": s["text"]}
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for s in analysis.get("transcript", {}).get("segments", [])
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],
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start=cr["start"],
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duration=cr["duration"],
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fade_in=fade["fade_in"],
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fade_out=fade["fade_out"],
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detected_language=analysis["language"],
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is_instrumental=analysis["instrumental"],
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claude_srt_path=srt_from_claude,
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)
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# Auto-disable subs za instrumental
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if analysis["instrumental"] and not job.get("no_subs"):
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update_job(job_id, no_subs=True, auto_disabled_subs=True)
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# Reload local dict
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job = load_job(job_id)
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except (json.JSONDecodeError, KeyError) as e:
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update_job(job_id, chorus_error=f"Analysis parse: {e}")
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else:
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update_job(job_id, chorus_error=(proc.stderr or "")[-500:])
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# ── 3. Reframe + subtitles (clip.py orchestrator) ─────
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output_path = OUTPUT_DIR / f"{job_id}.mp4"
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update_job(job_id, current_step="Reframe + subtitles")
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cmd = [
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"python3", str(SCRIPTS_DIR / "clip.py"),
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str(input_path), str(output_path),
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"--mode", job.get("mode", "track"),
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"--quality", job.get("quality", "medium"),
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"--style", job.get("subtitle_style", "reels"),
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]
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if job.get("start") is not None:
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cmd += ["--start", str(job["start"])]
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if job.get("duration") is not None:
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cmd += ["--duration", str(job["duration"])]
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if job.get("fade_in", 0) > 0:
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cmd += ["--fade-in", str(job["fade_in"])]
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if job.get("fade_out", 0) > 0:
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cmd += ["--fade-out", str(job["fade_out"])]
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# SRT iz Claude (boljše besedilo) — preda direktno v subtitle.py
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if job.get("claude_srt_path") and Path(job["claude_srt_path"]).exists() and not job.get("no_subs"):
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cmd += ["--srt", job["claude_srt_path"]]
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# lang: prefer detected_language če auto
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chosen_lang = job.get("lang")
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if chosen_lang in (None, "auto", ""):
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chosen_lang = job.get("detected_language")
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if chosen_lang:
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cmd += ["--lang", chosen_lang]
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if job.get("no_subs"):
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cmd += ["--no-subs"]
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cmd += ["--model", job.get("whisper_model", "large-v3")]
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# DEBUG: zapiši natanko kakšen ukaz se izvede
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update_job(job_id, debug_clip_cmd=" ".join(cmd))
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if not run_subprocess_logged(cmd, job_id, "Reframe + subtitles"):
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return
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|
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# ── Done ──────────────────────────────────────────────
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if output_path.exists():
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update_job(
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job_id,
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status="done",
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current_step="Končano",
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output_path=str(output_path),
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output_size_mb=round(output_path.stat().st_size / 1024 / 1024, 2),
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)
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else:
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update_job(
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job_id,
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status="failed",
|
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error="Output datoteka ne obstaja po obdelavi",
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)
|
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except Exception as e:
|
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update_job(job_id, status="failed", error=str(e))
|
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|
|
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# ────────────────────────────────────────────────────────────────
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# FastAPI app
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# ────────────────────────────────────────────────────────────────
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app = FastAPI(title="Reels Clipper")
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app.mount("/static", StaticFiles(directory=Path(__file__).parent.parent / "static"), name="static")
|
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|
|
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@app.on_event("startup")
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async def resume_or_cleanup_jobs():
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"""Ob startu containerja: avto-resume processing jobs ali jih označi kot error.
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Ko Coolify deploya nov container, prejšnji se ubije sredi obdelave,
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JSON file pa ostane status='processing'.
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|
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Strategija:
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- Če je analyze.json že narejen (analiza je končana) → resume z reframe+subs
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- Če ni analyze.json → restart pipeline od začetka
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- Po 3 napakah (resume_attempts >= 3) → mark error
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"""
|
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import asyncio
|
|
print("🔄 Preverjam in obnavljam jobs po restart-u...")
|
|
resumed_count = 0
|
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error_count = 0
|
|
|
|
for f in JOBS_DIR.glob("*.json"):
|
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try:
|
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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(...),
|
|
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(),
|
|
}
|
|
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")
|
|
return FileResponse(
|
|
out,
|
|
media_type="video/mp4",
|
|
filename=f"reel_{job_id}.mp4",
|
|
)
|
|
|
|
|
|
@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}
|