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Refactor imports and update configuration for LLM response processing; add post-processing functionality and enhance README documentation.
25fca04
import json | |
RESPONSE = """ | |
{ | |
"english": "I went for a walk in the park yesterday and saw many people exercising there.", | |
"important_words": [ | |
{ | |
"word_en": "exercise", | |
"meaning_ch": "to exercise", | |
"usage": "e.g. I go to the gym to exercise every day." | |
}, | |
{ | |
"word_en": "park", | |
"meaning_ch": "a public place for recreation", | |
"usage": "e.g. I took a walk in the park on Sunday." | |
}, | |
{ | |
"word_en": "scatter", | |
"meaning_ch": "to walk or move about without a regular or planned route", | |
"usage": "e.g. I like to scatter around the city to explore new places." | |
} | |
] | |
} | |
""" | |
def process_response(response: str) -> (str, list): | |
"""处理LLM响应,提取需要展示的信息""" | |
# response into json | |
response = json.loads(response) | |
eng_translation = response.get("english") | |
important_words_list = response.get("important_words") | |
print("English Translation:", eng_translation) | |
print("Important Words:", important_words_list) | |
return eng_translation, important_words_list | |
# 测试新的函数 | |
if __name__ == "__main__": | |
english_translation, important_words = process_response(RESPONSE) | |
# check type then print | |
print(type(english_translation), english_translation) | |
print(type(important_words), important_words) | |