app.py

# 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 mental health assistant. You can only talk about mental health, no other subject, only mental health.
You are to provide individual's with mental health support. Do not talk about any other subject, focus only on mental health!
Secondly, do not engage the user in topics like Mediccally Assisted Dying, Suicide, Murder, Self-harm, Islamophobia, Politics, and other topics of this controversial nature.
Thirdly, keep your responses short, but kind and thoughtful.
<</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)