app/model_utils.py

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

def load_model_and_tokenizer(model_name: str):
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
    return model, tokenizer

def generate_summary(prompt: str, model, tokenizer) -> str:
    context_prompt = f"Provide a brief, informative article addressing the following mental health concern: {prompt}"
    
    try:
        inputs = tokenizer(context_prompt, return_tensors="pt", truncation=True, padding=True)
        summary_ids = model.generate(inputs["input_ids"], max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
        return summary
    except Exception as e:
        return str(e)