第五章 推断¶

prompt = f"""
What is the sentiment of the following product review, 
which is delimited with triple backticks?

Review text: ```{lamp_review}```
"""
response = get_completion(prompt)
print(response)

# 中文
prompt = f"""
以下用三个反引号分隔的产品评论的情感是什么？

评论文本: ```{lamp_review_zh}```
"""
response = get_completion(prompt)
print(response)

prompt = f"""
What is the sentiment of the following product review, 
which is delimited with triple backticks?

Give your answer as a single word, either "positive" \
or "negative".

Review text: ```{lamp_review}```
"""
response = get_completion(prompt)
print(response)

prompt = f"""
以下用三个反引号分隔的产品评论的情感是什么？

用一个单词回答：「正面」或「负面」。

评论文本: ```{lamp_review_zh}```
"""
response = get_completion(prompt)
print(response)

prompt = f"""
Identify a list of emotions that the writer of the \
following review is expressing. Include no more than \
five items in the list. Format your answer as a list of \
lower-case words separated by commas.

Review text: ```{lamp_review}```
"""
response = get_completion(prompt)
print(response)

# 中文
prompt = f"""
识别以下评论的作者表达的情感。包含不超过五个项目。将答案格式化为以逗号分隔的单词列表。

评论文本: ```{lamp_review_zh}```
"""
response = get_completion(prompt)
print(response)

prompt = f"""
Is the writer of the following review expressing anger?\
The review is delimited with triple backticks. \
Give your answer as either yes or no.

Review text: ```{lamp_review}```
"""
response = get_completion(prompt)
print(response)

# 中文
prompt = f"""
以下评论的作者是否表达了愤怒？评论用三个反引号分隔。给出是或否的答案。

评论文本: ```{lamp_review_zh}```
"""
response = get_completion(prompt)
print(response)

prompt = f"""
Identify the following items from the review text: 
- Item purchased by reviewer
- Company that made the item

The review is delimited with triple backticks. \
Format your response as a JSON object with \
"Item" and "Brand" as the keys. 
If the information isn't present, use "unknown" \
as the value.
Make your response as short as possible.
  
Review text: ```{lamp_review}```
"""
response = get_completion(prompt)
print(response)

# 中文
prompt = f"""
从评论文本中识别以下项目：
- 评论者购买的物品
- 制造该物品的公司

评论文本用三个反引号分隔。将你的响应格式化为以 “物品” 和 “品牌” 为键的 JSON 对象。
如果信息不存在，请使用 “未知” 作为值。
让你的回应尽可能简短。
  
评论文本: ```{lamp_review_zh}```
"""
response = get_completion(prompt)
print(response)

prompt = f"""
Identify the following items from the review text: 
- Sentiment (positive or negative)
- Is the reviewer expressing anger? (true or false)
- Item purchased by reviewer
- Company that made the item

The review is delimited with triple backticks. \
Format your response as a JSON object with \
"Sentiment", "Anger", "Item" and "Brand" as the keys.
If the information isn't present, use "unknown" \
as the value.
Make your response as short as possible.
Format the Anger value as a boolean.

Review text: ```{lamp_review}```
"""
response = get_completion(prompt)
print(response)

# 中文
prompt = f"""
从评论文本中识别以下项目：
- 情绪（正面或负面）
- 审稿人是否表达了愤怒？（是或否）
- 评论者购买的物品
- 制造该物品的公司

评论用三个反引号分隔。将您的响应格式化为 JSON 对象，以 “Sentiment”、“Anger”、“Item” 和 “Brand” 作为键。
如果信息不存在，请使用 “未知” 作为值。
让你的回应尽可能简短。
将 Anger 值格式化为布尔值。

评论文本: ```{lamp_review_zh}```
"""
response = get_completion(prompt)
print(response)

prompt = f"""
Determine five topics that are being discussed in the \
following text, which is delimited by triple backticks.

Make each item one or two words long. 

Format your response as a list of items separated by commas.

Text sample: ```{story}```
"""
response = get_completion(prompt)
print(response)

response.split(sep=',')

# 中文
prompt = f"""
确定以下给定文本中讨论的五个主题。

每个主题用1-2个单词概括。

输出时用逗号分割每个主题。

给定文本: ```{story_zh}```
"""
response = get_completion(prompt)
print(response)

prompt = f"""
Determine whether each item in the following list of \
topics is a topic in the text below, which
is delimited with triple backticks.

Give your answer as list with 0 or 1 for each topic.\

List of topics: {", ".join(topic_list)}

Text sample: ```{story}```
"""
response = get_completion(prompt)
print(response)

topic_dict = {i.split(': ')[0]: int(i.split(': ')[1]) for i in response.split(sep='\n')}
if topic_dict['nasa'] == 1:
    print("ALERT: New NASA story!")

# 中文
prompt = f"""
判断主题列表中的每一项是否是给定文本中的一个话题，

以列表的形式给出答案，每个主题用 0 或 1。

主题列表：美国航空航天局、当地政府、工程、员工满意度、联邦政府

给定文本: ```{story_zh}```
"""
response = get_completion(prompt)
print(response)

topic_dict = {i.split('：')[0]: int(i.split('：')[1]) for i in response.split(sep='\n')}
if topic_dict['美国航空航天局'] == 1:
    print("提醒: 关于美国航空航天局的新消息")