.gitignore

@@ -1,10 +0,0 @@
-__pycache__/
-*.pyc
-*.pyo
-logs/
-*.log
-nohup.out
-py3/
-*.egg-info/
-*.pid
-venv/

app.py

@@ -1,177 +0,0 @@
-#!/usr/bin/env python3
-"""
-GLiNER NER Service - Zero-shot entity extraction
-Port: 9093, 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 = "/data/ymq/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=9093)