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