---
license: mit
language:
- zh
tags:
- mental health
- psychology
- medical
---

## Quick Start
```Python
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
        })

```

```bibtex
@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}}

```