from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

MODEL_NAME = "dmacres/bart-large-mimiciii-v2"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)

def generate_explanation(note: str, risk_score: float) -> str:
    prompt = f"""
Discharge summary:
{note}

Predicted readmission risk: {risk_score:.2f}

Explain the key clinical reasons for readmission risk.
"""

    inputs = tokenizer(
        prompt,
        return_tensors="pt",
        truncation=True,
        max_length=1024
    )

    outputs = model.generate(
        **inputs,
        max_length=200,
        num_beams=4,
        early_stopping=True
    )

    return tokenizer.decode(outputs[0], skip_special_tokens=True)