usage_example.py

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

def load_model(model_path="./"):
    """
    Load the Llama 3 Dementia Care model and tokenizer.
    
    Args:
        model_path (str): Path to the model directory
        
    Returns:
        tuple: (model, tokenizer)
    """
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    model = AutoModelForCausalLM.from_pretrained(
        model_path,
        torch_dtype=torch.float16,
        device_map="auto",
        trust_remote_code=True
    )
    return model, tokenizer

def generate_response(model, tokenizer, prompt, max_new_tokens=256, temperature=0.7, top_p=0.9, top_k=50):
    """
    Generate a response using the dementia care model.
    
    Args:
        model: The loaded model
        tokenizer: The loaded tokenizer
        prompt (str): The user's question or prompt
        max_new_tokens (int): Maximum number of new tokens to generate
        temperature (float): Sampling temperature
        top_p (float): Nucleus sampling parameter
        top_k (int): Top-k sampling parameter
        
    Returns:
        str: The model's response
    """
    # Prepare the conversation with system prompt
    messages = [
        {
            "role": "system", 
            "content": "You are a specialized assistant for dementia and memory care. Provide compassionate, accurate, and helpful information about dementia, Alzheimer's disease, caregiving strategies, and support resources. Always be empathetic and practical in your responses."
        },
        {
            "role": "user", 
            "content": prompt
        }
    ]
    
    # Apply chat template
    input_ids = tokenizer.apply_chat_template(
        messages, 
        return_tensors="pt",
        add_generation_prompt=True
    )
    
    # Generate response
    with torch.no_grad():
        outputs = model.generate(
            input_ids,
            max_new_tokens=max_new_tokens,
            temperature=temperature,
            top_p=top_p,
            top_k=top_k,
            repetition_penalty=1.1,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id
        )
    
    # Decode the response
    response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
    return response.strip()

def interactive_demo():
    """
    Run an interactive demo of the dementia care model.
    """
    print("Loading Llama 3 Dementia Care Assistant...")
    model, tokenizer = load_model()
    print("Model loaded successfully!\n")
    
    print("Llama 3 Dementia Care Assistant")
    print("=" * 40)
    print("This model provides specialized guidance for dementia and memory care.")
    print("Ask questions about caregiving, communication, safety, or support resources.")
    print("Type 'quit' to exit.\n")
    
    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in ['quit', 'exit', 'bye']:
            print("Thank you for using the Dementia Care Assistant. Take care!")
            break
        
        if not user_input:
            continue
            
        print("\nAssistant: ", end="")
        response = generate_response(model, tokenizer, user_input)
        print(response)
        print("\n" + "-" * 60 + "\n")

def example_usage():
    """
    Demonstrate example usage of the model.
    """
    print("Loading model for examples...")
    model, tokenizer = load_model()
    
    examples = [
        "What are some effective strategies for helping someone with dementia maintain their daily routine?",
        "How should I communicate with my mother who has Alzheimer's disease when she becomes confused?",
        "What safety modifications should I make to my home for someone with dementia?",
        "How can I handle agitation and restlessness in dementia patients?"
    ]
    
    print("Example responses from the Dementia Care Assistant:")
    print("=" * 60)
    
    for i, example in enumerate(examples, 1):
        print(f"\n{i}. Question: {example}")
        print(f"   Answer: {generate_response(model, tokenizer, example)}")
        print("-" * 60)

if __name__ == "__main__":
    import sys
    
    if len(sys.argv) > 1 and sys.argv[1] == "examples":
        example_usage()
    else:
        interactive_demo()