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from __future__ import annotations
from fastapi import FastAPI, UploadFile, File, Form
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from pathlib import Path
import shutil
import uvicorn
import json
from video_processing import process_video_pipeline
from casting_loader import ensure_chroma, build_faces_index, build_voices_index
from narration_system import NarrationSystem
from llm_router import load_yaml, LLMRouter
app = FastAPI(title="Veureu Engine API", version="0.2.0")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
ROOT = Path("/tmp/veureu")
ROOT.mkdir(parents=True, exist_ok=True)
@app.get("/")
def root():
return {"ok": True, "service": "veureu-engine"}
@app.post("/process_video")
async def process_video(
video_file: UploadFile = File(...),
config_path: str = Form("config.yaml"),
out_root: str = Form("results"),
db_dir: str = Form("chroma_db"),
):
tmp_video = ROOT / video_file.filename
with tmp_video.open("wb") as f:
shutil.copyfileobj(video_file.file, f)
result = process_video_pipeline(str(tmp_video), config_path=config_path, out_root=out_root, db_dir=db_dir)
return JSONResponse(result)
@app.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}
@app.post("/refine_narration")
async def refine_narration(
dialogues_srt: str = Form(...),
frame_descriptions_json: str = Form("[]"),
config_path: str = Form("config.yaml"),
):
cfg = load_yaml(config_path)
frames = json.loads(frame_descriptions_json)
model_name = cfg.get("narration", {}).get("model", "salamandra-instruct")
use_remote = model_name in (cfg.get("models", {}).get("routing", {}).get("use_remote_for", []))
if use_remote:
router = LLMRouter(cfg)
system_msg = (
"Eres un sistema de audiodescripción que cumple UNE-153010. "
"Fusiona diálogos del SRT con descripciones concisas en los huecos, evitando redundancias. "
"Devuelve JSON con {narrative_text, srt_text}."
)
prompt = json.dumps({"dialogues_srt": dialogues_srt, "frames": frames, "rules": cfg.get("narration", {})}, ensure_ascii=False)
try:
txt = router.instruct(prompt=prompt, system=system_msg, model=model_name)
out = {}
try:
out = json.loads(txt)
except Exception:
out = {"narrative_text": txt, "srt_text": ""}
return {
"narrative_text": out.get("narrative_text", ""),
"srt_text": out.get("srt_text", ""),
"approved": True,
"critic_feedback": "",
}
except Exception:
ns = NarrationSystem(model_url=None, une_guidelines_path=cfg.get("narration", {}).get("narration_une_guidelines_path", "UNE_153010.txt"))
res = ns.run(dialogues_srt, frames)
return {"narrative_text": res.narrative_text, "srt_text": res.srt_text, "approved": res.approved, "critic_feedback": res.critic_feedback}
ns = NarrationSystem(model_url=None, une_guidelines_path=cfg.get("narration", {}).get("une_guidelines_path", "UNE_153010.txt"))
out = ns.run(dialogues_srt, frames)
return {"narrative_text": out.narrative_text, "srt_text": out.srt_text, "approved": out.approved, "critic_feedback": out.critic_feedback}
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860)
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