79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
import json
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from tqdm import tqdm
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def qwen_api(prompt):
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import dashscope
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from http import HTTPStatus
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dashscope.api_key = "your key"
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prompt = "你是一位非常擅长将英文翻译成中文的专家。请你将下面的英文翻译成正确地道的中文，要求只返回翻译的中文句子:\n" + prompt
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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 get_conversation_list():
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with open('./ESConv.json', 'rt', encoding='utf-8') as file:
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data = json.load(file)
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idx = 0
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conversation_list = []
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for itm in tqdm(data):
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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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for dia in tqdm(itm['dialog']):
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# print(dia['speaker'], dia['content'])
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if dia['speaker'] == 'seeker':
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dia_tuple.append(qwen_api(dia['content']))
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elif dia['speaker'] == 'supporter':
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dia_tuple.append(qwen_api(dia['content']))
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else:
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exit("不存在角色!")
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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_list.append(one_conversation)
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idx += 1
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# if (idx == 1):
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# print(conversation_list)
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# break
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print(idx)
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return conversation_list
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if __name__ == '__main__':
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conversation_list = get_conversation_list()
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# 将conversation_list保存为一个json文件
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with open('conversation_list.json', 'wt', encoding='utf-8') as f:
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json.dump(conversation_list, f, ensure_ascii=False)
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