spacy nlp ner

backend/nlp-server/server.py

@@ -0,0 +1,110 @@
+import os
+import logging
+from typing import List, Optional
+
+import spacy
+from fastapi import FastAPI
+from pydantic import BaseModel
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("nlp-server")
+
+NLP_MODEL = os.getenv("NLP_MODEL", "fr_core_news_md")
+
+print(f"[INIT] Loading spaCy model '{NLP_MODEL}'...", flush=True)
+nlp = spacy.load(NLP_MODEL)
+print("[INIT] spaCy model ready.", flush=True)
+
+app = FastAPI()
+
+
+class NLPRequest(BaseModel):
+    text: str
+
+
+class Entity(BaseModel):
+    text: str
+    label: str
+    start: int
+    end: int
+
+
+class NLPEntityResult(BaseModel):
+    text: str
+    label: str
+    start: int
+    end: int
+    confidence: float
+
+
+class NLPToken(BaseModel):
+    text: str
+    lemma: str
+    pos: str
+    tag: str
+    dep: str
+    is_stop: bool
+
+
+class NLPResult(BaseModel):
+    entities: List[NLPEntityResult]
+    tokens: List[NLPToken]
+    noun_chunks: List[str]
+    sentences: List[str]
+
+
+class NLPResponse(BaseModel):
+    errorCode: int
+    result: Optional[NLPResult] = None
+    message: Optional[str] = None
+
+
+@app.get("/health")
+def health():
+    return {"status": "healthy", "service": "spaCy NLP Server", "model": NLP_MODEL}
+
+
+@app.post("/nlp")
+def analyze(req: NLPRequest):
+    print(f"[NLP] Request received, text length: {len(req.text)}", flush=True)
+    try:
+        doc = nlp(req.text)
+
+        entities = []
+        for ent in doc.ents:
+            entities.append(NLPEntityResult(
+                text=ent.text,
+                label=ent.label_,
+                start=ent.start_char,
+                end=ent.end_char,
+                confidence=0.0,
+            ))
+
+        tokens = []
+        for token in doc:
+            tokens.append(NLPToken(
+                text=token.text,
+                lemma=token.lemma_,
+                pos=token.pos_,
+                tag=token.tag_,
+                dep=token.dep_,
+                is_stop=token.is_stop,
+            ))
+
+        noun_chunks = [chunk.text for chunk in doc.noun_chunks]
+
+        sentences = [sent.text.strip() for sent in doc.sents]
+
+        result = NLPResult(
+            entities=entities,
+            tokens=tokens,
+            noun_chunks=noun_chunks,
+            sentences=sentences,
+        )
+
+        print(f"[NLP] Done: {len(entities)} entities, {len(tokens)} tokens, {len(sentences)} sentences", flush=True)
+        return {"errorCode": 0, "result": result}
+
+    except Exception as e:
+        logger.exception("NLP analysis failed")
+        return {"errorCode": 2, "message": str(e)}