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| import os | |
| import gradio as gr | |
| from openai import OpenAI | |
| import json | |
| OPEN_AI_KEY = os.getenv("OPEN_AI_KEY") | |
| OPEN_AI_CLIENT = OpenAI(api_key=OPEN_AI_KEY) | |
| def generate_topics(model, max_tokens, sys_content, scenario, eng_level, user_generate_topics_prompt): | |
| """ | |
| 根据系统提示和用户输入的情境及主题，调用OpenAI API生成相关的主题句。 | |
| """ | |
| user_content = f""" | |
| scenario is {scenario} | |
| english level is {eng_level} | |
| {user_generate_topics_prompt} | |
| """ | |
| messages = [ | |
| {"role": "system", "content": sys_content}, | |
| {"role": "user", "content": user_content} | |
| ] | |
| request_payload = { | |
| "model": model, | |
| "messages": messages, | |
| "max_tokens": max_tokens, | |
| } | |
| response = OPEN_AI_CLIENT.chat.completions.create(**request_payload) | |
| content = response.choices[0].message.content.strip() | |
| return content | |
| def generate_points(model, max_tokens, sys_content, scenario, eng_level, topic, user_generate_points_prompt): | |
| """ | |
| 根据系统提示和用户输入的情境、主题，调用OpenAI API生成相关的主题句。 | |
| """ | |
| user_content = f""" | |
| scenario is {scenario} | |
| english level is {eng_level} | |
| topic is {topic} | |
| {user_generate_points_prompt} | |
| """ | |
| messages = [ | |
| {"role": "system", "content": sys_content}, | |
| {"role": "user", "content": user_content} | |
| ] | |
| request_payload = { | |
| "model": model, | |
| "messages": messages, | |
| "max_tokens": max_tokens, | |
| } | |
| response = OPEN_AI_CLIENT.chat.completions.create(**request_payload) | |
| content = response.choices[0].message.content.strip() | |
| return content | |
| def generate_topic_sentences(model, max_tokens, sys_content, scenario, eng_level, topic, points, user_generate_topic_sentences_prompt): | |
| """ | |
| 根据系统提示和用户输入的情境及要点，调用OpenAI API生成相关的主题句及其合理性解释。 | |
| """ | |
| user_content = f""" | |
| scenario is {scenario} | |
| english level is {eng_level} | |
| topic is {topic} | |
| points is {points} | |
| {user_generate_topic_sentences_prompt} | |
| """ | |
| messages = [ | |
| {"role": "system", "content": sys_content}, | |
| {"role": "user", "content": user_content} | |
| ] | |
| response_format = { "type": "json_object" } | |
| request_payload = { | |
| "model": model, | |
| "messages": messages, | |
| "max_tokens": max_tokens, | |
| "response_format": response_format | |
| } | |
| response = OPEN_AI_CLIENT.chat.completions.create(**request_payload) | |
| json_content = response.choices[0].message.content | |
| content = parse_and_display_topic_sentences(json_content) | |
| return content | |
| def parse_and_display_topic_sentences(json_data): | |
| """ | |
| 解析JSON格式的主题句数据，并转换成易于阅读的格式。 | |
| """ | |
| # 将JSON字符串解析成Python字典 | |
| data = json.loads(json_data) | |
| # 初始化一个空字符串用于存放最终的格式化文本 | |
| formatted_text = "" | |
| # 遍历每个主题句及其评价 | |
| for key, value in data.items(): | |
| topic_sentence = value[0]['topic-sentence'] | |
| appropriate = "適當" if value[0]['appropriate'] == "Y" else "不適當" | |
| reason = value[0]['reason'] | |
| # 将每个主题句的信息添加到格式化文本中 | |
| formatted_text += f"主题句 {int(key)+1}: {topic_sentence}\n" | |
| formatted_text += f"是否適當: {appropriate}\n" | |
| formatted_text += f"原因: {reason}\n\n" | |
| return formatted_text | |
| def generate_supporting_sentences(model, max_tokens, sys_content, scenario, eng_level, topic, points, topic_sentence, user_generate_supporting_sentences_prompt): | |
| """ | |
| 根据系统提示和用户输入的情境、主题、要点、主题句，调用OpenAI API生成相关的支持句。 | |
| """ | |
| user_content = f""" | |
| scenario is {scenario} | |
| english level is {eng_level} | |
| topic is {topic} | |
| points is {points} | |
| topic sentence is {topic_sentence} | |
| {user_generate_supporting_sentences_prompt} | |
| """ | |
| messages = [ | |
| {"role": "system", "content": sys_content}, | |
| {"role": "user", "content": user_content} | |
| ] | |
| request_payload = { | |
| "model": model, | |
| "messages": messages, | |
| "max_tokens": max_tokens, | |
| } | |
| response = OPEN_AI_CLIENT.chat.completions.create(**request_payload) | |
| content = response.choices[0].message.content.strip() | |
| return content | |
| def generate_conclusion_sentences(model, max_tokens, sys_content, scenario, eng_level, topic, points, topic_sentence, user_generate_conclusion_sentence_prompt): | |
| """ | |
| 根据系统提示和用户输入的情境、主题、要点、主题句，调用OpenAI API生成相关的结论句。 | |
| """ | |
| user_content = f""" | |
| scenario is {scenario} | |
| english level is {eng_level} | |
| topic is {topic} | |
| points is {points} | |
| topic sentence is {topic_sentence} | |
| {user_generate_conclusion_sentence_prompt} | |
| """ | |
| messages = [ | |
| {"role": "system", "content": sys_content}, | |
| {"role": "user", "content": user_content} | |
| ] | |
| request_payload = { | |
| "model": model, | |
| "messages": messages, | |
| "max_tokens": max_tokens, | |
| } | |
| response = OPEN_AI_CLIENT.chat.completions.create(**request_payload) | |
| content = response.choices[0].message.content.strip() | |
| return content | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| # basic inputs | |
| gr.Markdown("## 1. Basic Inputs") | |
| model = gr.Radio(["gpt-4-1106-preview", "gpt-3.5-turbo"], label="Model", value="gpt-4-1106-preview") | |
| max_tokens = gr.Slider(minimum=50, maximum=4000, value=1000, label="Max Tokens") | |
| sys_content_input = gr.Textbox(label="System Prompt", value="You are an English teacher who is practicing with me to improve my English writing skill.") | |
| scenario_input = gr.Textbox(label="Scenario") | |
| eng_level_input = gr.Radio(["beginner", "intermediate", "advanced"], label="English Level", value="beginner") | |
| gr.Markdown("## 2. Generate Topic") | |
| default_generate_topics_prompt = """ | |
| Give me 10 topics relevant to Scenario, | |
| for a paragraph. Just the topics, no explanation, use simple English language. | |
| Make sure the vocabulary you use is at english level. | |
| """ | |
| user_generate_topics_prompt = gr.Textbox(label="Topics Prompt", value=default_generate_topics_prompt) | |
| generate_topics_button = gr.Button("Generate Topic Sentences") | |
| gr.Markdown("## 3. Generate Points") | |
| topic_input = gr.Textbox(label="Topic") | |
| default_generate_points_prompt = """ | |
| Please provide main points to develop in a paragraph about topic in the context of scenario, | |
| use simple English language and make sure the vocabulary you use is at eng_level. | |
| No more explanation either no developing these points into a simple paragraph. | |
| """ | |
| user_generate_points_prompt = gr.Textbox(label="Points Prompt", value=default_generate_points_prompt) | |
| generate_points_button = gr.Button("Generate Points") | |
| gr.Markdown("## 4. Generate Topic Sentences") | |
| points_input = gr.Textbox(label="Points") | |
| default_generate_topic_sentences_prompt = """ | |
| Please provide one appropriate topic sentence that aptly introduces the subject for the given scenario and topic. | |
| Additionally, provide two topic sentences that, while related to the topic, | |
| would be considered inappropriate or less effective for the specified context. | |
| Those sentences must include the three main points:". | |
| Use English language and each sentence should not be too long. | |
| For each sentence, explain the reason in Traditional Chinese, Taiwan, 繁體中文 zh-TW. | |
| Make sure the vocabulary you use is at level. | |
| Only return the result in JSON format starting as: | |
| {{ | |
| "0": [ {{ "topic-sentence": "#","appropriate": "Y/N", "reason": "#中文解釋" }} ], | |
| "1": [ {{ "topic-sentence": "#","appropriate": "Y/N", "reason": "#中文解釋" }} ], | |
| "2": [ {{ "topic-sentence": "#","appropriate": "Y/N", "reason": "#中文解釋" }} ] | |
| }} | |
| """ | |
| user_generate_topic_sentences_prompt = gr.Textbox(label="Topic Sentences Prompt", value=default_generate_topic_sentences_prompt) | |
| generate_topic_sentences_button = gr.Button("Generate Topic Sentences") | |
| gr.Markdown("## 5. Generate Supporting Sentence 支持句") | |
| topic_sentence_input = gr.Textbox(label="Topic Sentences") | |
| default_generate_supporting_sentences_prompt = """ | |
| I'm aiming to improve my writing. I have a topic sentence as topic_sentence_input. | |
| Please assist me by "Developing supporting detials" based on the keyword: points to write three sentences as an example. | |
| - Make sure any revised vocabulary aligns with the eng_level. | |
| - Guidelines for Length and Complexity: | |
| Please keep the example concise and straightforward, | |
| avoiding overly technical language. | |
| Total word-count is around 50. no more explanation either no more extra non-relation sentences. | |
| """ | |
| user_generate_supporting_sentences_prompt = gr.Textbox(label="Supporting Sentences Prompt", value=default_generate_supporting_sentences_prompt) | |
| generate_supporting_sentences_button = gr.Button("Generate Supporting Sentences") | |
| gr.Markdown("## 6. Conclusion sentence 結論句") | |
| supporting_sentences_input = gr.Textbox(label="Supporting Sentences") | |
| default_generate_conclusion_sentence_prompt = """ | |
| I'm aiming to improve my writing. | |
| By the topic sentence, please assist me by "Developing conclusion sentences" | |
| based on keywords of points to finish a paragrpah as an example. | |
| - Make sure any revised vocabulary aligns with the correctly level. | |
| - Guidelines for Length and Complexity: | |
| Please keep the example concise and straightforward, | |
| avoiding overly technical language. | |
| Total word-count is around 20. | |
| """ | |
| user_generate_conclusion_sentence_prompt = gr.Textbox(label="Conclusion Sentence Prompt", value=default_generate_conclusion_sentence_prompt) | |
| generate_conclusion_sentence_button = gr.Button("Generate Conclusion Sentence") | |
| with gr.Column(): | |
| topic_output = gr.Textbox(label="Generated Topic 主題") | |
| points_output = gr.Textbox(label="Generated Points 要點") | |
| topic_sentence_output = gr.Textbox(label="Generated Topic Sentences 主題句") | |
| supporting_sentences_output = gr.Textbox(label="Generated Supporting Sentences 支持句") | |
| generate_topics_button.click( | |
| fn=generate_topics, | |
| inputs=[ | |
| model, | |
| max_tokens, | |
| sys_content_input, | |
| scenario_input, | |
| eng_level_input, | |
| user_generate_topics_prompt | |
| ], | |
| outputs=topic_output | |
| ) | |
| generate_points_button.click( | |
| fn=generate_points, | |
| inputs=[ | |
| model, | |
| max_tokens, | |
| sys_content_input, | |
| scenario_input, | |
| eng_level_input, | |
| topic_input, | |
| user_generate_points_prompt | |
| ], | |
| outputs=points_output | |
| ) | |
| generate_topic_sentences_button.click( | |
| fn=generate_topic_sentences, | |
| inputs=[ | |
| model, | |
| max_tokens, | |
| sys_content_input, | |
| scenario_input, | |
| eng_level_input, | |
| topic_input, | |
| points_input, | |
| user_generate_topic_sentences_prompt | |
| ], | |
| outputs=topic_sentence_output | |
| ) | |
| generate_supporting_sentences_button.click( | |
| fn=generate_supporting_sentences, | |
| inputs=[ | |
| model, | |
| max_tokens, | |
| sys_content_input, | |
| scenario_input, | |
| eng_level_input, | |
| topic_input, | |
| points_input, | |
| topic_sentence_input, | |
| user_generate_supporting_sentences_prompt | |
| ], | |
| outputs=supporting_sentences_output | |
| ) | |
| generate_conclusion_sentence_button.click( | |
| fn=generate_conclusion_sentences, | |
| inputs=[ | |
| model, | |
| max_tokens, | |
| sys_content_input, | |
| scenario_input, | |
| eng_level_input, | |
| topic_input, | |
| points_input, | |
| topic_sentence_input, | |
| user_generate_conclusion_sentence_prompt | |
| ], | |
| outputs=supporting_sentences_output | |
| ) | |
| demo.launch() | |