import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import gradio as gr
import spaces

model_id = "meta-llama/Llama-Guard-3-8B-INT8"
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.bfloat16

quantization_config = BitsAndBytesConfig(load_in_8bit=True)

def load_model():
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(
        model_id, 
        torch_dtype=dtype, 
        device_map="auto",
        quantization_config=quantization_config,
        low_cpu_mem_usage=True
    )
    return tokenizer, model

tokenizer, model = load_model()

@spaces.GPU
def moderate(user_input, assistant_response):
    chat = [
        {"role": "user", "content": user_input},
        {"role": "assistant", "content": assistant_response},
    ]
    input_ids = tokenizer.apply_chat_template(chat, return_tensors="pt").to(device)
    
    with torch.no_grad():
        output = model.generate(
            input_ids=input_ids, 
            max_new_tokens=200,
            pad_token_id=tokenizer.eos_token_id,
            do_sample=False
        )
    
    result = tokenizer.decode(output[0], skip_special_tokens=True)
    
    result = result.split(assistant_response)[-1].strip()
    
    is_safe = "safe" in result.lower()
    categories = []
    if not is_safe and "categories:" in result:
        categories = [cat.strip() for cat in result.split("categories:")[1].split(",") if cat.strip()]
    
    return {
        "is_safe": "Safe" if is_safe else "Unsafe",
        "categories": ", ".join(categories) if categories else "None",
        "raw_output": result
    }

iface = gr.Interface(
    fn=moderate,
    inputs=[
        gr.Textbox(lines=3, label="User Input"),
        gr.Textbox(lines=3, label="Assistant Response")
    ],
    outputs=[
        gr.Textbox(label="Safety Status"),
        gr.Textbox(label="Violated Categories"),
        gr.Textbox(label="Raw Output")
    ],
    title="Llama Guard Moderation",
    description="Enter a user input and an assistant response to check for content moderation."
)

if __name__ == "__main__":
    iface.launch()