from clinical_ner import ClinicalNER

# Initialize the NER system with POS tagging
ner = ClinicalNER(use_pos=True)

# Sample clinical text
text = "Patient presents with hypertension and diabetes. Prescribed metformin 500mg."

print("="*70)
print("NAMED ENTITY RECOGNITION")
print("="*70)
results = ner.basic_ner(text)
for entity in results:
    print(f"  {entity['word']} -> {entity['entity_group']} (score: {entity['score']:.4f})")

print("\n" + "="*70)
print("PART-OF-SPEECH TAGGING")
print("="*70)
pos_results = ner.pos_tagging(text)
for token in pos_results:
    print(f"  {token['token']:15} | POS: {token['pos']:6} | Tag: {token['tag']:6} | Dep: {token['dep']}")

print("\n" + "="*70)
print("PROLOG FACTS - NER")
print("="*70)
print(ner.prolog_ner(text))

print("\n" + "="*70)
print("PROLOG FACTS - POS")
print("="*70)
print(ner.prolog_pos(text))

print("\n" + "="*70)
print("COMBINED PROLOG FACTS")
print("="*70)
print(ner.prolog_combined(text))