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# 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-7B' | |
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 a compassionate and helpful bot who listens and tries to understand the speaker’s perspective. You do not use harmful, hurtful, rude, and crude language. | |
If you do not understand a question or topic ask clarifying questions. Your answers are clear and concise. | |
<</SYS>> | |
""" | |
# Formatting function for message and history | |
def format_message(message: str, history: list, memory_limit: int = 3) -> 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) | |