classification.py

gistfile1.txt

  from transformers import pipeline

# Load a pre-trained NER model for biomedical entities
ner_model = "Helios9/BioMed_NER"  # Example model for biomedical NER
ner_pipeline = pipeline("ner", model=ner_model)

# Load a pre-trained zero-shot classification model
zero_shot_model = "MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33"  # Example zero-shot model
classifier_pipeline = pipeline("zero-shot-classification", model=zero_shot_model)

# Sample longer medical text (one continuous string)
text = """Patient John Doe, a 45-year-old male, was diagnosed with hypertension and prescribed 
50mg of Lisinopril daily. He also has a history of type 2 diabetes and reports experiencing 
occasional chest pain. During his last visit, his blood pressure was recorded at 160/100 mmHg. 
The doctor advised him to monitor his blood sugar levels regularly and scheduled a follow-up 
appointment in three months. Additionally, he was referred to a nutritionist for dietary counseling."""

# Run NER to extract entities
entities = ner_pipeline(text)

# Define candidate labels for classification
labels = ["Description", "Disease", "Medication", "Diagnosis", "Treatment"]
classification_result = classifier_pipeline(text, candidate_labels=labels)

# Print results
print("Extracted Entities:")
print(entities)

print("\nClassification Result:")
print(classification_result)