app.py

import gradio as gr
from huggingface_hub import InferenceClient, login  # Added login
from transformers import pipeline
import random

# Authenticate with Hugging Face (get token: https://huggingface.co/settings/tokens)
login(token="YOUR_HF_TOKEN")  # 👈 Replace with your token!

# Safety tools 🛡️
BLOCKED_WORDS = ["violence", "hate", "gun", "personal"]
SAFE_IDEAS = [
    "Design a robot to clean parks 🌳",
    "Code a game about recycling ♻️",
    "Plan an AI tool for school safety 🚸"
]

# Changed to PUBLIC safety model ✅
safety_checker = pipeline("text-classification", model="unitary/toxic-bert")

client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

def is_safe(text):
    text = text.lower()
    if any(bad_word in text for bad_word in BLOCKED_WORDS):
        return False
    result = safety_checker(text)[0]
    return not (result["label"] == "toxic" and result["score"] > 0.7)  # Changed label check

def respond(message, history, system_message, max_tokens, temperature, top_p):
    if not is_safe(message):
        return f"🚫 Let's focus on positive projects! Try: {random.choice(SAFE_IDEAS)}"
    
    messages = [{
        "role": "system",
        "content": f"{system_message}\nYou are a friendly STEM mentor for kids. Never discuss unsafe topics!"
    }]
    
    # Rest of your chat code...
    # (Keep your existing chat implementation here)
    
# Rest of your Gradio setup...