105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
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import json
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import random
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import argparse
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import yaml
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import re
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from tqdm import tqdm
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with open('config.yml', 'r', encoding='utf-8') as f:
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configs = yaml.load(f.read(), Loader=yaml.FullLoader)
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def qwen_api(data, emo):
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import dashscope
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from http import HTTPStatus
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dashscope.api_key = configs['dashscope_api_key']
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prompt = f'''你是一个研究过无数具有心理健康问题的病人与心理健康医生对话的专家，请你构造一些符合实际情况的具有心理健
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康问题的病人和心理健康医生的连续的多轮对话记录。要求病人的问题属于{data}场景，具有{emo}情感，医生的回复尽可能包含心理辅导知识，并且能够一步步诱导病人说出自己的问题进而提供解决问题的可行方案。注意，构造的数据必须以医生的陈述为结束语，请只返回完整的对话内容。请以如下格式返回生成的数据：
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病人：病人的咨询或陈述
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医生：医生的安抚和建议
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'''
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response = dashscope.Generation.call(
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model='qwen-max',
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prompt=prompt,
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history=[],
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)
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if response.status_code == HTTPStatus.OK:
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result = response.output.text
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print(result)
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else:
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result = 'ERROR'
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return result
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def save_jsonl(data_lis, file_path):
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import json
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# 将字典列表写入文件，每一行一个字典
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with open(file_path, 'at', encoding='utf-8') as file:
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for item in data_lis:
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json_string = json.dumps(item, ensure_ascii=False) + '\n'
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file.write(json_string)
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if __name__ == '__main__':
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idx = 0
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parser = argparse.ArgumentParser(description='数据生成参数')
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parser.add_argument('--data', type=str, help='生活场景')
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# 解析命令行参数
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args = parser.parse_args()
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emotions_lis = configs['emotions_list']
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areas_of_life = configs['areas_of_life']
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conversation_lis = []
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for i in tqdm(range(100)):
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one_conversation = {
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"conversation": []
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}
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dia_tuple = []
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emo = random.choice(emotions_lis)
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res = qwen_api(data=args.data, emo=emo)
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print(res)
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# 一次会话
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doctor_pattern = r'医生：(.*?)(病人：|$)'
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doctor_matches = re.findall(doctor_pattern, res, re.DOTALL)
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doctor_conversations = [match[0] for match in doctor_matches]
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patient_pattern = r'病人：(.*?)医生：'
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patient_matches = re.findall(patient_pattern, res, re.DOTALL)
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patient_conversations = [match for match in patient_matches]
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for doc, pat in zip(doctor_conversations, patient_conversations):
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if len(one_conversation['conversation']) == 0:
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one_conversation['conversation'].append(
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{
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"system": "现在你是一个心理专家，我有一些心理问题，请你用专业的知识帮我解决。",
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"input": pat,
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"output": doc
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},
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)
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else:
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one_conversation['conversation'].append(
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{
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"input": pat,
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"output": doc
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},
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)
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conversation_lis.append(one_conversation)
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idx += 1
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# 每生成10条数据存储一次
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if (idx % 10 == 0):
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path = f'./{args.data}.jsonl'
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save_jsonl(data_lis=conversation_lis, file_path=path)
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conversation_lis = [] # 清空
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