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