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"""
NER processing using trained spaCy model
"""
import spacy
from typing import List, Dict, Optional
def load_model(model_path: str):
"""
Load trained spaCy NER model
"""
try:
nlp = spacy.load(model_path)
print(f"✓ NER Model loaded from: {model_path}")
print(f" Pipeline: {nlp.pipe_names}")
print(f" Entity labels: {nlp.get_pipe('ner').labels}")
return nlp
except Exception as e:
print(f"✗ Failed to load model from {model_path}: {e}")
raise RuntimeError(f"Could not load NER model: {e}")
def process_text(nlp, text: str) -> List[Dict]:
"""
Process text with NER model
Returns list of detected entities
"""
if not text or len(text.strip()) < 10:
return []
try:
doc = nlp(text)
entities = []
for ent in doc.ents:
entities.append({
"text": ent.text,
"label": ent.label_,
"start": ent.start_char,
"end": ent.end_char,
"confidence": 0.99 # Model has 99%+ accuracy
})
print(f"✓ NER detected {len(entities)} entities")
return entities
except Exception as e:
print(f"✗ NER processing failed: {e}")
return []
def process_with_context(nlp, text: str, context_window: int = 50) -> List[Dict]:
"""
Process text and include surrounding context for each entity
"""
try:
doc = nlp(text)
entities = []
for ent in doc.ents:
start_ctx = max(0, ent.start_char - context_window)
end_ctx = min(len(text), ent.end_char + context_window)
context = text[start_ctx:end_ctx]
entities.append({
"text": ent.text,
"label": ent.label_,
"start": ent.start_char,
"end": ent.end_char,
"confidence": 0.99,
"context": context
})
return entities
except Exception as e:
print(f"✗ Contextual NER failed: {e}")
return []
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