# Imports
import gradio as gr
import transformers
import torch
import os
from transformers import pipeline, AutoTokenizer

from huggingface_hub import login


HF_TOKEN = os.getenv('mentalhealth_llama_chat')
login(HF_TOKEN)

# Model name in Hugging Face docs
model ='klyang/MentaLLaMA-chat-13B'


tokenizer = AutoTokenizer.from_pretrained(model, use_auth_token=True)

llama_pipeline = pipeline(
    "text-generation",  # LLM task
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)


SYSTEM_PROMPT = """<s>[INST] <<SYS>>
You are Mentra, a friendly, empathetic mental health chatbot who listens and tries to understand the speaker’s perspective. 
Do not talk about anything else, focus only on mental health. If the user asks you about football or engineering or geography, DO NOT ANSWER! 
You do not use harmful, hurtful, rude, and crude language. 
<</SYS>>

"""

# Formatting function for message and history
def format_message(message: str, history: list, memory_limit: int = 20) -> str:
    """
    Formats the message and history for the Llama model.

    Parameters:
        message (str): Current message to send.
        history (list): Past conversation history.
        memory_limit (int): Limit on how many past interactions to consider.

    Returns:
        str: Formatted message string
    """
    # always keep len(history) <= memory_limit
    if len(history) > memory_limit:
        history = history[-memory_limit:]

    if len(history) == 0:
        return SYSTEM_PROMPT + f"{message} [/INST]"

    formatted_message = SYSTEM_PROMPT + f"{history[0][0]} [/INST] {history[0][1]} </s>"

    # Handle conversation history
    for user_msg, model_answer in history[1:]:
        formatted_message += f"<s>[INST] {user_msg} [/INST] {model_answer} </s>"

    # Handle the current message
    formatted_message += f"<s>[INST] {message} [/INST]"

    return formatted_message

    

# Generate a response from the Llama model
def get_llama_response(message: str, history: list) -> str:
    """
    Generates a conversational response from the Llama model.

    Parameters:
        message (str): User's input message.
        history (list): Past conversation history.

    Returns:
        str: Generated response from the Llama model.
    """
    query = format_message(message, history)
    response = ""

    sequences = llama_pipeline(
        query,
        do_sample=True,
        top_k=10,
        num_return_sequences=1,
        eos_token_id=tokenizer.eos_token_id,
        max_length=1024,
    )

    generated_text = sequences[0]['generated_text']
    response = generated_text[len(query):]  # Remove the prompt from the output

    print("Chatbot:", response.strip())
    return response.strip()


gr.ChatInterface(get_llama_response).launch(debug=True)