ner-service/app.py

#!/usr/bin/env python3
"""
GLiNER NER Service - Zero-shot entity extraction
Port: 9094, GPU 5
"""
import os
import re
import logging
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import List
import uvicorn

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("ner-service")

app = FastAPI(title="GLiNER NER Service", version="1.0.0")
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)

# Global model
model = None
MODEL_PATH = "/mnt/disk0/yumoqing/models/gliner-multitask-large-v0.5"


def load_model():
    global model
    try:
        from gliner import GLiNER
        logger.info(f"Loading GLiNER from {MODEL_PATH}...")
        model = GLiNER.from_pretrained(MODEL_PATH, local_files_only=True)
        logger.info("Model loaded successfully")
        return True
    except Exception as e:
        logger.error(f"Failed to load model: {e}")
        return False


class ExtractRequest(BaseModel):
    text: str
    entities: List[str] = ["person", "company", "product", "location", "concept", "event"]
    threshold: float = 0.5


class Entity(BaseModel):
    text: str
    label: str
    start: int
    end: int
    score: float


class Relation(BaseModel):
    source: str
    target: str
    relation: str
    confidence: float


class ExtractResponse(BaseModel):
    entities: List[Entity]
    relations: List[Relation]


@app.on_event("startup")
async def startup():
    success = load_model()
    if not success:
        logger.warning("Model not loaded, /api/extract will return 503")


@app.get("/api/status")
async def status():
    return {
        "service": "ner-service",
        "status": "ready" if model else "no_model",
        "model": "gliner-multitask-large-v0.5",
        "model_path": MODEL_PATH,
        "model_loaded": model is not None,
    }


@app.post("/api/extract", response_model=ExtractResponse)
async def extract(req: ExtractRequest):
    if not model:
        raise HTTPException(status_code=503, detail="Model not loaded")

    if not req.text or len(req.text.strip()) == 0:
        return ExtractResponse(entities=[], relations=[])

    try:
        entities_raw = model.predict_entities(
            req.text, req.entities, threshold=req.threshold
        )

        entities = [
            Entity(
                text=e["text"],
                label=e["label"],
                start=e["start"],
                end=e["end"],
                score=round(e["score"], 4),
            )
            for e in entities_raw
        ]

        relations = extract_relations(req.text, entities)
        return ExtractResponse(entities=entities, relations=relations)

    except Exception as e:
        logger.error(f"Extraction failed: {e}", exc_info=True)
        raise HTTPException(status_code=500, detail=str(e))


def extract_relations(text: str, entities: List[Entity]) -> List[Relation]:
    """Rule-based relation extraction using co-occurrence in sentences."""
    relations = []
    entity_texts = {e.text: e.label for e in entities}

    sentences = re.split(r'[。！？.!?\n]+', text)

    relation_patterns = [
        (r'(.+?)在(.+?)(?:担任|工作|任职)', "works_at"),
        (r'(.+?)是(.+?)的(?:CEO|总裁|董事长|创始人)', "leads"),
        (r'(.+?)收购(?:了)?(.+?)', "acquired"),
        (r'(.+?)发布(?:了)?(.+?)', "released"),
        (r'(.+?)位于(.+?)', "located_in"),
        (r'(.+?)投资(?:了)?(.+?)', "invested_in"),
        (r'(.+?)属于(.+?)', "belongs_to"),
        (r'(.+?)与(.+?)合作', "collaborates"),
    ]

    for sent in sentences:
        sent = sent.strip()
        if not sent:
            continue

        sent_entities = [e for e in entities if e.text in sent]
        if len(sent_entities) < 2:
            continue

        # Pattern-based relations
        for pattern, rel_type in relation_patterns:
            match = re.search(pattern, sent)
            if match:
                groups = match.groups()
                if len(groups) >= 2:
                    src, tgt = groups[0].strip(), groups[1].strip()
                    if src in entity_texts and tgt in entity_texts:
                        relations.append(Relation(
                            source=src, target=tgt,
                            relation=rel_type, confidence=0.8,
                        ))

        # Co-occurrence fallback
        seen_pairs = set()
        for i in range(len(sent_entities)):
            for j in range(i + 1, len(sent_entities)):
                e1, e2 = sent_entities[i], sent_entities[j]
                pair = tuple(sorted([e1.text, e2.text]))
                if pair not in seen_pairs:
                    seen_pairs.add(pair)
                    relations.append(Relation(
                        source=pair[0], target=pair[1],
                        relation="co_occur", confidence=0.5,
                    ))

    return relations


if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=9094)