import gradio as gr
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer

# Load your model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("allenai/llama")
model = AutoModelForCausalLM.from_pretrained("allenai/llama")

# Load a content moderation pipeline
moderation_pipeline = pipeline("text-classification", model="typeform/mobilebert-uncased-mnli")

# Function to load bad words from a file
def load_bad_words(filepath):
    with open(filepath, 'r', encoding='utf-8') as file:
        return [line.strip().lower() for line in file]

# Load bad words list
bad_words = load_bad_words('badwords.txt')  # Adjust the path to your bad words file

def is_inappropriate_or_offtopic(message, topics):
    # Check for inappropriate content using the loaded bad words list
    if any(bad_word in message.lower() for bad_word in bad_words):
        return True
    # Check if off-topic
    if topics and not any(topic.lower() in message.lower() for topic in topics if topic):
        return True
    return False

def check_content(message):
    predictions = moderation_pipeline(message)
    if predictions[0]['label'] == 'LABEL_1':  # Adjust based on the model's output
        return True
    return False

def generate_response(prompt, topic1, topic2, topic3):
    topics = [topic1, topic2, topic3]  # Collect user-defined topics
    if is_inappropriate_or_offtopic(prompt, topics):
        return "Sorry, let's try to keep our conversation focused on positive and relevant topics!"
    if check_content(prompt):
        return "I'm here to provide a safe and friendly conversation. Let's talk about something else."
    
    inputs = tokenizer.encode(prompt, return_tensors="pt")
    outputs = model.generate(inputs, max_length=50, do_sample=True)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Define Gradio interface with topic inputs
iface = gr.Interface(fn=generate_response, 
                     inputs=[gr.inputs.Textbox(lines=2, placeholder="Type your message here..."),
                             gr.inputs.Textbox(label="Topic 1", placeholder="Optional", default=""),
                             gr.inputs.Textbox(label="Topic 2", placeholder="Optional", default=""),
                             gr.inputs.Textbox(label="Topic 3", placeholder="Optional", default="")],
                     outputs="text",
                     title="Child-Safe Chatbot",
                     description="A chatbot that stays on topic and filters inappropriate content. Define up to three topics.")

# Run the app
if __name__ == "__main__":
    iface.launch()