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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 = 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)
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