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


# Retrieve the token from environment variables
huggingface_token = os.getenv('LLAMA_ACCES_TOKEN')

# Use the token with from_pretrained
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf", token=huggingface_token)
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", token=huggingface_token)


# 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

# List of topics for the dropdown
topics_list = ['Aviation', 'Science', 'Education', 'Air Force Pilot', 'Space Exploration', 'Technology']

def is_inappropriate_or_offtopic(message, selected_topics):
    if any(bad_word in message.lower() for bad_word in bad_words):
        return True
    if selected_topics and not any(topic.lower() in message.lower() for topic in selected_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(message, selected_topics):
    if is_inappropriate_or_offtopic(message, selected_topics):
        return "Sorry, let's try to keep our conversation focused on positive and relevant topics!"
    if check_content(message):
        return "I'm here to provide a safe and friendly conversation. Let's talk about something else."
    
    inputs = tokenizer.encode(message, return_tensors="pt")
    outputs = model.generate(inputs, max_length=50, do_sample=True)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    #response = f"Echo: {message}. Selected topics: {', '.join(selected_topics)}"
    return response


def main():
    with gr.Blocks() as demo:
        gr.Markdown("### Child-Safe Chatbot BETA")
        with gr.Row():
            message_input = gr.Textbox(label="Your Message")
            topics_dropdown = gr.Dropdown(choices=topics_list, label="Select Topics", multiselect=True)
            submit_btn = gr.Button("Send")
        response_output = gr.Textbox(label="Bot Response")

        # Corrected to directly pass selected_topics without wrapping it in another list
        submit_btn.click(
            fn=generate_response, 
            inputs=[message_input, topics_dropdown], 
            outputs=response_output
        )

    demo.launch()

# Run the app
if __name__ == "__main__":
    main()