File size: 9,252 Bytes
dbc3c35 89e1dc4 dbc3c35 89e1dc4 dbc3c35 89e1dc4 dbc3c35 89e1dc4 dbc3c35 89e1dc4 dbc3c35 89e1dc4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 | from fastapi import BackgroundTasks, FastAPI, File, Form, HTTPException, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse
from fastapi.staticfiles import StaticFiles
from app.core.config import get_settings
from app.models.schemas import (
ChannelProfile,
ClipCandidate,
ClipPatch,
HealthResponse,
JobSnapshot,
PolishSubtitlesRequest,
RegenerateClipRequest,
SubtitleCue,
TranslateSubtitlesRequest,
YoutubeJobRequest,
)
from app.services.highlight import QwenHighlightDetector
from app.services.pipeline import VideoPipeline
from app.services.transcription import WhisperTranscriber
from app.services.video_input import save_upload
from app.storage import JobStore
from app.utils.rocm import detect_accelerator
settings = get_settings()
store = JobStore(settings)
pipeline = VideoPipeline(settings, store)
highlight_detector = QwenHighlightDetector(settings)
transcriber = WhisperTranscriber(settings)
app = FastAPI(title=settings.app_name, version="0.1.0")
app.add_middleware(
CORSMiddleware,
allow_origins=[settings.frontend_origin, "http://localhost:5173", "http://127.0.0.1:5173"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
app.mount("/media", StaticFiles(directory=settings.storage_dir), name="media")
@app.get("/health", response_model=HealthResponse)
async def health() -> HealthResponse:
return HealthResponse(
ok=True,
app=settings.app_name,
demo_mode=settings.demo_mode,
accelerator=detect_accelerator(),
)
@app.post("/api/jobs/youtube", response_model=JobSnapshot)
async def create_youtube_job(
request: YoutubeJobRequest, background_tasks: BackgroundTasks
) -> JobSnapshot:
snapshot = store.create_job(
request.profile, {"kind": "youtube", "url": str(request.youtube_url)}
)
background_tasks.add_task(
pipeline.process_source, snapshot.id, "youtube", str(request.youtube_url), request.profile
)
return snapshot
@app.post("/api/jobs/upload", response_model=JobSnapshot)
async def create_upload_job(
background_tasks: BackgroundTasks,
profile_json: str = Form(...),
file: UploadFile = File(...),
) -> JobSnapshot:
try:
profile = ChannelProfile.model_validate_json(profile_json)
except Exception as exc:
raise HTTPException(status_code=422, detail=f"Invalid profile JSON: {exc}") from exc
snapshot = store.create_job(profile, {"kind": "upload", "filename": file.filename})
source_path = await save_upload(file, store.job_dir(snapshot.id))
background_tasks.add_task(pipeline.process_source, snapshot.id, "upload", str(source_path), profile)
return snapshot
@app.get("/api/jobs/{job_id}", response_model=JobSnapshot)
async def get_job(job_id: str) -> JobSnapshot:
try:
return store.get_job(job_id)
except FileNotFoundError as exc:
raise HTTPException(status_code=404, detail="Job not found") from exc
@app.patch("/api/jobs/{job_id}/clips/{clip_id}", response_model=ClipCandidate)
async def update_clip(job_id: str, clip_id: str, patch: ClipPatch) -> ClipCandidate:
try:
return pipeline.patch_clip(job_id, clip_id, patch.model_dump())
except FileNotFoundError as exc:
raise HTTPException(status_code=404, detail="Job not found") from exc
except KeyError as exc:
raise HTTPException(status_code=404, detail="Clip not found") from exc
@app.post("/api/jobs/{job_id}/clips/{clip_id}/regenerate", response_model=ClipCandidate)
async def regenerate_clip(
job_id: str, clip_id: str, request: RegenerateClipRequest
) -> ClipCandidate:
try:
return pipeline.regenerate_clip(
job_id,
clip_id,
clip_style=request.clip_style,
clip_length_seconds=request.clip_length_seconds,
subtitle_text=request.subtitle_text,
)
except FileNotFoundError as exc:
raise HTTPException(status_code=404, detail="Source video not found") from exc
except KeyError as exc:
raise HTTPException(status_code=404, detail="Clip not found") from exc
@app.get("/api/jobs/{job_id}/clips/{clip_id}/download")
async def download_clip(job_id: str, clip_id: str) -> FileResponse:
snapshot = store.get_job(job_id)
clip = next((item for item in snapshot.clips if item.id == clip_id), None)
if clip is None or clip.download_url is None:
raise HTTPException(status_code=404, detail="Clip not found")
filename = clip.download_url.rsplit("/", 1)[-1]
path = store.job_dir(job_id) / filename
if not path.exists():
raise HTTPException(status_code=404, detail="Clip file not found")
return FileResponse(path, media_type="video/mp4", filename=filename)
# ─────────────────────────────────────────────────────────────────
# AI subtitle endpoints — work in demo mode immediately, switch to
# real Qwen / Whisper output once DEMO_MODE=false on AMD GPU cloud.
# ─────────────────────────────────────────────────────────────────
def _resolve_clip_cues(snapshot: JobSnapshot, clip: ClipCandidate) -> list[SubtitleCue]:
"""Return the cue list to operate on. Prefer explicit subtitle_cues; fall
back to splitting subtitle_text into evenly-spaced cues."""
if clip.subtitle_cues:
return [SubtitleCue(**cue.model_dump()) for cue in clip.subtitle_cues]
duration = max(0.5, clip.end_seconds - clip.start_seconds)
text = clip.subtitle_text.strip()
if not text:
return [SubtitleCue(start_seconds=0.0, end_seconds=duration, text="")]
# Reuse Whisper aligner's deterministic chunking for fallback
return transcriber._demo_align_words(text, 0.0, duration)
@app.post(
"/api/jobs/{job_id}/clips/{clip_id}/subtitle/polish",
response_model=ClipCandidate,
)
async def polish_clip_subtitles(
job_id: str, clip_id: str, request: PolishSubtitlesRequest
) -> ClipCandidate:
try:
snapshot = store.get_job(job_id)
except FileNotFoundError as exc:
raise HTTPException(status_code=404, detail="Job not found") from exc
clip = next((c for c in snapshot.clips if c.id == clip_id), None)
if clip is None:
raise HTTPException(status_code=404, detail="Clip not found")
cues_in = _resolve_clip_cues(snapshot, clip)
polished = highlight_detector.polish_subtitles(cues_in, style=request.style)
return pipeline.patch_clip(
job_id,
clip_id,
{
"subtitle_cues": [cue.model_dump() for cue in polished],
"subtitle_text": " ".join(cue.text for cue in polished if cue.text),
},
)
@app.post(
"/api/jobs/{job_id}/clips/{clip_id}/subtitle/translate",
response_model=ClipCandidate,
)
async def translate_clip_subtitles(
job_id: str, clip_id: str, request: TranslateSubtitlesRequest
) -> ClipCandidate:
try:
snapshot = store.get_job(job_id)
except FileNotFoundError as exc:
raise HTTPException(status_code=404, detail="Job not found") from exc
clip = next((c for c in snapshot.clips if c.id == clip_id), None)
if clip is None:
raise HTTPException(status_code=404, detail="Clip not found")
cues_in = _resolve_clip_cues(snapshot, clip)
translated = highlight_detector.translate_subtitles(cues_in, request.target_language)
return pipeline.patch_clip(
job_id,
clip_id,
{
"subtitle_cues": [cue.model_dump() for cue in translated],
"subtitle_text": " ".join(cue.text for cue in translated if cue.text),
},
)
@app.post(
"/api/jobs/{job_id}/clips/{clip_id}/subtitle/auto-time",
response_model=ClipCandidate,
)
async def auto_time_clip_subtitles(job_id: str, clip_id: str) -> ClipCandidate:
try:
snapshot = store.get_job(job_id)
except FileNotFoundError as exc:
raise HTTPException(status_code=404, detail="Job not found") from exc
clip = next((c for c in snapshot.clips if c.id == clip_id), None)
if clip is None:
raise HTTPException(status_code=404, detail="Clip not found")
text = clip.subtitle_text or " ".join(
(cue.text for cue in (clip.subtitle_cues or []) if cue.text)
)
# Best-effort: production mode uses the actual source video on disk; demo
# mode uses synthetic chunking that doesn't require the file at all.
source_path = ""
try:
for entry in store.job_dir(job_id).iterdir():
if entry.suffix.lower() in {".mp4", ".mkv", ".mov", ".webm"}:
source_path = str(entry)
break
except Exception:
source_path = ""
timed = transcriber.align_words(source_path, text, clip.start_seconds, clip.end_seconds)
return pipeline.patch_clip(
job_id,
clip_id,
{
"subtitle_cues": [cue.model_dump() for cue in timed],
"subtitle_text": " ".join(cue.text for cue in timed if cue.text),
},
)
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