Quick Start
from transformers import AutoTokenizer, AutoModel
def get_dialogue_history(dialogue_history_list: list):
dialogue_history_tmp = []
for item in dialogue_history_list:
if item['role'] == 'counselor':
text = '咨询师:'+ item['content']
else:
text = '来访者:'+ item['content']
dialogue_history_tmp.append(text)
dialogue_history = '\n'.join(dialogue_history_tmp)
return dialogue_history + '\n' + '咨询师:'
def get_instruction(dialogue_history):
instruction = f'''现在你扮演一位专业的心理咨询师,你具备丰富的心理学和心理健康知识。你擅长运用多种心理咨询技巧,例如认知行为疗法原则、动机访谈技巧和解决问题导向的短期疗法。以温暖亲切的语气,展现出共情和对来访者感受的深刻理解。以自然的方式与来访者进行对话,避免过长或过短的回应,确保回应流畅且类似人类的对话。提供深层次的指导和洞察,使用具体的心理概念和例子帮助来访者更深入地探索思想和感受。避免教导式的回应,更注重共情和尊重来访者的感受。根据来访者的反馈调整回应,确保回应贴合来访者的情境和需求。请为以下的对话生成一个回复。
对话:
{dialogue_history}'''
return instruction
tokenizer = AutoTokenizer.from_pretrained('qiuhuachuan/PsyChat', trust_remote_code=True)
model = AutoModel.from_pretrained('qiuhuachuan/PsyChat', trust_remote_code=True).half().cuda()
model = model.eval()
dialogue_history_list = []
while True:
usr_msg = input('来访者:')
if usr_msg == '0':
exit()
else:
dialogue_history_list.append({
'role': 'client',
'content': usr_msg
})
dialogue_history = get_dialogue_history(dialogue_history_list=dialogue_history_list)
instruction = get_instruction(dialogue_history=dialogue_history)
response, history = model.chat(tokenizer, instruction, history=[], temperature=0.8, top_p=0.8)
print(f'咨询师:{response}')
dialogue_history_list.append({
'role': 'counselor',
'content': response
})
@INPROCEEDINGS{10580641,
author={Qiu, Huachuan and Li, Anqi and Ma, Lizhi and Lan, Zhenzhong},
booktitle={2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)},
title={PsyChat: A Client-Centric Dialogue System for Mental Health Support},
year={2024},
volume={},
number={},
pages={2979-2984},
keywords={Employee welfare;Accuracy;Federated learning;Focusing;Mental health;Generators;dialogue system;client-centric;mental health support;client behavior recognition;counselor strategy selection},
doi={10.1109/CSCWD61410.2024.10580641}}
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