# STACKS Usage Example
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

def load_stacks():
    """Load STACKS model and tokenizer"""
    model = AutoModelForCausalLM.from_pretrained(
        "gouthamsai78/STACKS",
        torch_dtype=torch.bfloat16,
        device_map="auto",
        attn_implementation="eager"
    )
    tokenizer = AutoTokenizer.from_pretrained("gouthamsai78/STACKS")
    return model, tokenizer

def generate_prompt(role, model, tokenizer, temperature=0.8):
    """Generate creative prompt for given role"""
    input_text = f"### Task: Generate a creative prompt for someone acting as {role}\n### Generated Prompt:"
    inputs = tokenizer(input_text, return_tensors="pt")

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=200,
            temperature=temperature,
            do_sample=True,
            top_p=0.9,
            repetition_penalty=1.1,
            pad_token_id=tokenizer.eos_token_id
        )

    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response[len(input_text):].strip()

# Example usage
if __name__ == "__main__":
    model, tokenizer = load_stacks()

    # Generate prompts for different roles
    roles = ["chef", "detective", "astronaut", "teacher", "artist"]

    for role in roles:
        prompt = generate_prompt(role, model, tokenizer)
        print(f"**{role.title()}**: {prompt}\n")