import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
import os

# Retrieve the Hugging Face token from environment variables
HF_TOKEN = os.getenv("HUGGINGFACE_TOKEN")

if HF_TOKEN is None:
    raise ValueError("HUGGINGFACE_TOKEN is not set. Add it in your Space's secrets.")

# Load model and tokenizer with authentication
MODEL_NAME = "meta-llama/Llama-Guard-3-1B"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_auth_token=HF_TOKEN)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, use_auth_token=HF_TOKEN)

# Streamlit UI
st.title("AI Safe Content Checker Tool")
st.write("Enter text below, and the model will check if it's safe.")

# User input
user_input = st.text_area("Enter your text here:")

def check_content(text):
    prompt = f"<|user|>\n{text}\n<|assistant|>\n"  # Ensure correct formatting for input
    inputs = tokenizer(prompt, return_tensors="pt")
    
    outputs = model.generate(**inputs, max_new_tokens=50)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)

    return response  # Check the response format to extract category

if st.button("Check Content"):
    if user_input:
        result = check_content(user_input)
        st.subheader("Moderation Result:")
        st.write(result)  # Print raw result first to analyze format
    else:
        st.warning("Please enter some text.")