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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import gradio as gr
import torch
from peft import PeftConfig, PeftModel
# Loading PEFT model
PEFT_MODEL = "TurtleLiu/mistral7b_psychology_bot"
config = PeftConfig.from_pretrained(PEFT_MODEL)
bnb_config = BitsAndBytesConfig(
load_in_4bit= True,
bnb_4bit_quant_type= "nf4",
bnb_4bit_compute_dtype= torch.bfloat16,
bnb_4bit_use_double_quant= False,
)
peft_base_model = AutoModelForCausalLM.from_pretrained(
config.base_model_name_or_path,
return_dict=True,
quantization_config=bnb_config,
device_map="auto",
trust_remote_code=True,
)
model = PeftModel.from_pretrained(peft_base_model, PEFT_MODEL)
model = model.merge_and_unload()
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
# Generate response
def format_prompt(message, history):
prompt = "<s>"
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s> "
prompt += f"[INST] {message} [/INST]"
return prompt
def generate_response(prompt, history, temperature=0.9, max_new_tokens=1024, top_p=0.95, repetition_penalty=1.0):
'''
from huggingface_hub import InferenceClient
import gradio as gr
client = InferenceClient(
"TurtleLiu/mistral7b_psychology_bot"
)
def format_prompt(message, history):
prompt = "<s>"
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s> "
prompt += f"[INST] {message} [/INST]"
return prompt
def format_prompt(message, history):
prompt = "<s>"
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s> "
prompt += f"[INST] As a psychology counselor assistant, provide an assessment and plan for the following counseling notes. Please present a summary, don't make it so long. Present in lines.: {message} [/INST]"
return prompt
def generate(
prompt, history, temperature=0.9, max_new_tokens=1024, top_p=0.95, repetition_penalty=1.0,
):
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=42,
)
formatted_prompt = format_prompt(f"{prompt}", history)
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
output += response.token.text
yield output
return output
examples=[
["Patient is feeling stressed due to work and has trouble sleeping.", None, None, None, None, None],
["Client is dealing with relationship issues and is seeking advice on communication strategies.", None, None, None, None, None],
["Individual has recently experienced a loss and is having difficulty coping with grief.", None, None, None, None, None],
]
gr.ChatInterface(
fn=generate,
chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
title="Psychological Assistant: Expert in Assessment and Strategic Planning",
description="Enter counseling notes to generate an assessment and plan.",
examples=examples,
concurrency_limit=20,
).launch(show_api=False, debug=True)
''' |