144 lines
3.9 KiB
Python
144 lines
3.9 KiB
Python
import json
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import random
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import argparse
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from tqdm import tqdm
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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 = ""
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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康问题的病
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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 = [
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"钦佩",
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"崇拜",
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"欣赏",
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"娱乐",
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"焦虑",
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"敬畏",
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"尴尬",
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"厌倦",
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"冷静",
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"困惑",
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"渴望",
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"厌恶",
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"同情",
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"痛苦"
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"着迷",
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"嫉妒",
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"兴奋",
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"恐惧",
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"痛恨",
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"有趣",
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"快乐",
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"怀旧",
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"浪漫",
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"悲伤",
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"满意",
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"性欲",
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"同情",
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"满足"
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]
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areas_of_life = [
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"工作",
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"学业",
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"生活",
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"身体",
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"家人",
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"朋友",
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"社交",
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"恋爱",
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"就业",
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"责任",
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"爱好",
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"环境",
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"隐私",
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"安全",
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"梦想",
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"自由"
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]
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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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for itm in res.split('\n'):
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if itm.startswith("病人："):
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dia_tuple.append(itm.split("：")[1])
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elif itm.startswith("医生："):
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dia_tuple.append(itm.split("：")[1])
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if len(dia_tuple) == 2 and 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": dia_tuple[0],
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"output": dia_tuple[1]
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},
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)
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dia_tuple = []
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elif len(dia_tuple) == 2:
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one_conversation['conversation'].append(
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{
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"input": dia_tuple[0],
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"output": dia_tuple[1]
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},
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)
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dia_tuple = []
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conversation_lis.append(one_conversation)
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idx += 1
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# 每生成2条数据存储一次
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if (idx % 2 == 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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