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# predict.py
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import pickle
model_path = 'shirleylqs/mistral-snomed-classification'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
with open(f'{model_path}/label_encoder.pkl', 'rb') as f:
label_encoder = pickle.load(f)
def predict_class(text):
inputs = tokenizer(text, return_tensors='pt', truncation=True, max_length=128)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predict_class_id = logits.argmax(-1).item()
predict_label = label_encoder.inverse_transform([predict_class_id])[0]
return predict_label
if __name__ == "__main__":
text = "purulent discharge"
predicted_label = predict_class(text)
print(predicted_label)
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