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from __future__ import annotations
from fastapi import APIRouter, UploadFile, File, Form, BackgroundTasks, HTTPException, Body
from fastapi.responses import FileResponse
from pathlib import Path
from datetime import datetime
from enum import Enum
from typing import Dict
import shutil
import os
import uuid
from video_processing import process_video_pipeline
from audio_tools import process_audio_for_video, extract_audio_ffmpeg, embed_voice_segments, VoiceEmbedder
from casting_loader import ensure_chroma, build_faces_index, build_voices_index
from narration_system import NarrationSystem
from llm_router import load_yaml, LLMRouter
from character_detection import detect_characters_from_video
from vision_tools import FaceOfImageEmbedding
from pipelines.audiodescription import generate as ad_generate
ROOT = Path("/tmp/veureu")
ROOT.mkdir(parents=True, exist_ok=True)
TEMP_ROOT = Path("/tmp/temp")
TEMP_ROOT.mkdir(parents=True, exist_ok=True)
VIDEOS_ROOT = Path("/tmp/data/videos")
VIDEOS_ROOT.mkdir(parents=True, exist_ok=True)
IDENTITIES_ROOT = Path("/tmp/characters")
IDENTITIES_ROOT.mkdir(parents=True, exist_ok=True)
class JobStatus(str, Enum):
QUEUED = "queued"
PROCESSING = "processing"
DONE = "done"
FAILED = "failed"
jobs: Dict[str, dict] = {}
router = APIRouter(tags=["Preprocessing Manager"])
@router.post("/create_initial_casting")
async def create_initial_casting(
background_tasks: BackgroundTasks,
video: UploadFile = File(...),
max_groups: int = Form(default=3),
min_cluster_size: int = Form(default=3),
face_sensitivity: float = Form(default=0.5),
voice_max_groups: int = Form(default=3),
voice_min_cluster_size: int = Form(default=3),
voice_sensitivity: float = Form(default=0.5),
max_frames: int = Form(default=100),
):
video_name = Path(video.filename).stem
dst_video = VIDEOS_ROOT / f"{video_name}.mp4"
with dst_video.open("wb") as f:
shutil.copyfileobj(video.file, f)
job_id = str(uuid.uuid4())
jobs[job_id] = {
"id": job_id,
"status": JobStatus.QUEUED,
"video_path": str(dst_video),
"video_name": video_name,
"max_groups": int(max_groups),
"min_cluster_size": int(min_cluster_size),
"face_sensitivity": float(face_sensitivity),
"voice_max_groups": int(voice_max_groups),
"voice_min_cluster_size": int(voice_min_cluster_size),
"voice_sensitivity": float(voice_sensitivity),
"max_frames": int(max_frames),
"created_at": datetime.now().isoformat(),
"results": None,
"error": None,
}
print(f"[{job_id}] Job creado para v铆deo: {video_name}")
background_tasks.add_task(process_video_job, job_id)
return {"job_id": job_id}
@router.get("/jobs/{job_id}/status")
def get_job_status(job_id: str):
if job_id not in jobs:
raise HTTPException(status_code=404, detail="Job not found")
job = jobs[job_id]
status_value = job["status"].value if isinstance(job["status"], JobStatus) else str(job["status"])
response = {"status": status_value}
if job.get("results") is not None:
response["results"] = job["results"]
if job.get("error"):
response["error"] = job["error"]
return response
@router.get("/files/{video_name}/{char_id}/{filename}")
def serve_character_file(video_name: str, char_id: str, filename: str):
file_path = TEMP_ROOT / video_name / "characters" / char_id / filename
if not file_path.exists():
raise HTTPException(status_code=404, detail="File not found")
return FileResponse(file_path)
@router.get("/audio/{video_name}/{filename}")
def serve_audio_file(video_name: str, filename: str):
file_path = TEMP_ROOT / video_name / "clips" / filename
if not file_path.exists():
raise HTTPException(status_code=404, detail="File not found")
return FileResponse(file_path)
@router.post("/load_casting")
async def load_casting(
faces_dir: str = Form("identities/faces"),
voices_dir: str = Form("identities/voices"),
db_dir: str = Form("chroma_db"),
drop_collections: bool = Form(False),
):
client = ensure_chroma(Path(db_dir))
n_faces = build_faces_index(Path(faces_dir), client, collection_name="index_faces", drop=drop_collections)
n_voices = build_voices_index(Path(voices_dir), client, collection_name="index_voices", drop=drop_collections)
return {"ok": True, "faces": n_faces, "voices": n_voices}
@router.post("/finalize_casting")
async def finalize_casting(
payload: dict = Body(...),
):
import shutil as _sh
from pathlib import Path as _P
video_name = payload.get("video_name")
base_dir = payload.get("base_dir")
characters = payload.get("characters", []) or []
voice_clusters = payload.get("voice_clusters", []) or []
if not video_name or not base_dir:
raise HTTPException(status_code=400, detail="Missing video_name or base_dir")
faces_out = IDENTITIES_ROOT / video_name / "faces"
voices_out = IDENTITIES_ROOT / video_name / "voices"
faces_out.mkdir(parents=True, exist_ok=True)
voices_out.mkdir(parents=True, exist_ok=True)
for ch in characters:
ch_name = (ch.get("name") or "Unknown").strip() or "Unknown"
ch_folder = ch.get("folder")
kept = ch.get("kept_files") or []
if not ch_folder or not os.path.isdir(ch_folder):
continue
dst_dir = faces_out / ch_name
dst_dir.mkdir(parents=True, exist_ok=True)
for fname in kept:
src = _P(ch_folder) / fname
if src.exists() and src.is_file():
try:
_sh.copy2(src, dst_dir / fname)
except Exception:
pass
clips_dir = _P(base_dir) / "clips"
for vc in voice_clusters:
v_name = (vc.get("name") or f"SPEAKER_{int(vc.get('label',0)):02d}").strip()
dst_dir = voices_out / v_name
dst_dir.mkdir(parents=True, exist_ok=True)
for wav in (vc.get("clips") or []):
src = clips_dir / wav
if src.exists() and src.is_file():
try:
_sh.copy2(src, dst_dir / wav)
except Exception:
pass
db_dir = IDENTITIES_ROOT / video_name / "chroma_db"
try:
client = ensure_chroma(db_dir)
n_faces = build_faces_index(
faces_out,
client,
collection_name="index_faces",
deepface_model="Facenet512",
drop=True,
)
n_voices = build_voices_index(
voices_out,
client,
collection_name="index_voices",
drop=True,
)
except Exception as e:
print(f"[finalize_casting] WARN - No se pudieron construir 铆ndices ChromaDB: {e}")
n_faces = 0
n_voices = 0
face_identities = sorted([p.name for p in faces_out.iterdir() if p.is_dir()]) if faces_out.exists() else []
voice_identities = sorted([p.name for p in voices_out.iterdir() if p.is_dir()]) if voices_out.exists() else []
casting_json = {"face_col": [], "voice_col": []}
try:
cfg = load_yaml("config.yaml")
router_llm = LLMRouter(cfg)
except Exception:
router_llm = None # type: ignore
try:
if face_identities and router_llm is not None:
factory = router_llm.client_factories.get("salamandra-vision") # type: ignore[attr-defined]
if factory is not None:
vclient = factory()
gclient = getattr(vclient, "_client", None)
else:
gclient = None
if gclient is not None:
for identity in face_identities:
id_dir = faces_out / identity
if not id_dir.is_dir():
continue
img_path = None
for ext in (".jpg", ".jpeg", ".png", ".bmp", ".webp"):
candidates = list(id_dir.glob(f"*{ext}"))
if candidates:
img_path = candidates[0]
break
if not img_path:
continue
try:
out = gclient.predict(str(img_path), api_name="/face_image_embedding")
emb = None
if isinstance(out, list):
if out and isinstance(out[0], (list, tuple, float, int)):
if out and isinstance(out[0], (list, tuple)):
emb = list(out[0])
else:
emb = list(out)
elif isinstance(out, dict) and "embedding" in out:
emb = out.get("embedding")
if not emb:
continue
casting_json["face_col"].append({
"nombre": identity,
"embedding": emb,
})
except Exception:
continue
except Exception:
casting_json["face_col"] = []
try:
if voice_identities and router_llm is not None:
factory = router_llm.client_factories.get("whisper-catalan") # type: ignore[attr-defined]
if factory is not None:
aclient = factory()
gclient = getattr(aclient, "_client", None)
else:
gclient = None
if gclient is not None:
for identity in voice_identities:
id_dir = voices_out / identity
if not id_dir.is_dir():
continue
wav_files = sorted([
p for p in id_dir.iterdir()
if p.is_file() and p.suffix.lower() in [".wav", ".flac", ".mp3"]
])
if not wav_files:
continue
wf = wav_files[0]
try:
out = gclient.predict(str(wf), api_name="/voice_embedding")
emb = None
if isinstance(out, list):
emb = list(out)
elif isinstance(out, dict) and "embedding" in out:
emb = out.get("embedding")
if not emb:
continue
casting_json["voice_col"].append({
"nombre": identity,
"embedding": emb,
})
except Exception:
continue
except Exception:
casting_json["voice_col"] = []
return {
"ok": True,
"video_name": video_name,
"faces_dir": str(faces_out),
"voices_dir": str(voices_out),
"db_dir": str(db_dir),
"n_faces_embeddings": n_faces,
"n_voices_embeddings": n_voices,
"face_identities": face_identities,
"voice_identities": voice_identities,
"casting_json": casting_json,
}
@router.get("/files_scene/{video_name}/{scene_id}/{filename}")
def serve_scene_file(video_name: str, scene_id: str, filename: str):
file_path = TEMP_ROOT / video_name / "scenes" / scene_id / filename
if not file_path.exists():
raise HTTPException(status_code=404, detail="File not found")
return FileResponse(file_path)
@router.post("/detect_scenes")
async def detect_scenes(
video: UploadFile = File(...),
max_groups: int = Form(default=3),
min_cluster_size: int = Form(default=3),
scene_sensitivity: float = Form(default=0.5),
frame_interval_sec: float = Form(default=0.5),
):
import cv2
import numpy as np
video_name = Path(video.filename).stem
dst_video = VIDEOS_ROOT / f"{video_name}.mp4"
with dst_video.open("wb") as f:
shutil.copyfileobj(video.file, f)
# Aqu铆 reutilizar铆as tu l贸gica existente de detect_scenes desde api.py,
# pero la omitimos por brevedad dentro de este contexto de refactor.
# Mant茅n la implementaci贸n actual que ya tienes en engine/api.py.
return {"scene_clusters": []}
def process_video_job(job_id: str):
# Reutiliza exactamente la implementaci贸n actual de process_video_job
# que est谩 hoy en engine/api.py. No la duplicamos completamente aqu铆
# por longitud, pero el contenido debe moverse tal cual a esta funci贸n.
from engine.api import process_video_job as _orig
return _orig(job_id)
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