Spacy 2

app.py

@@ -55,7 +55,6 @@ class SearchRequest(BaseModel):
 classifier_pipeline = None
 qa_pipeline = None
 summarization_pipeline = None
-ner_pipeline = None
 nlp = spacy.load("en_core_web_sm")
 rake = Rake()
 
@@ -156,21 +155,6 @@ def load_summarization_model():
     except Exception as e:
         logger.error(f"Summarization model load error: {e}")
 
-def load_ner_model():
-    """Load NER model"""
-    global ner_pipeline
-    try:
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        ner_pipeline = pipeline(
-            "ner",
-            model="dbmdz/bert-large-cased-finetuned-conll03-english",
-            device=0 if device == "cuda" else -1,
-            grouped_entities=True
-        )
-        logger.info("NER model loaded")
-    except Exception as e:
-        logger.error(f"NER model load error: {e}")
-
 def classify_query(prompt: str) -> str:
     """Classify query using zero-shot learning"""
     candidate_labels = ['question answering', 'summarization']
@@ -209,7 +193,6 @@ def format_search_context(results: List[Dict[str, Any]]) -> str:
 async def startup_event():
     """Initialize core models on startup"""
     load_classifier()
-    # load_ner_model()
 
 @app.post("/chat", response_model=ChatResponse)
 async def chat_endpoint(request: ChatRequest):
@@ -220,11 +203,6 @@ async def chat_endpoint(request: ChatRequest):
         search_context = ""
         # Web search processing
         if request.use_search:
-            # entities = ner_pipeline(request.prompt)
-            # search_terms = [
-            #     ent["word"] for ent in entities
-            #     if ent["entity_group"] in ["PER", "ORG", "LOC", "MISC"]
-            # ]
             search_terms = extract_search_terms(request.prompt.lower())
             search_query = " ".join(search_terms) or request.prompt
             search_results = search_web(search_query)