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| import os | |
| import gradio as gr | |
| from openai import OpenAI | |
| import json | |
| import tempfile | |
| 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 | |
| def generate_paragraph(topic_sentence, supporting_sentences, conclusion_sentence): | |
| """ | |
| 根据用户输入的主题句、支持句、结论句，生成完整的段落。 | |
| """ | |
| paragraph = f"{topic_sentence}\n{supporting_sentences}\n{conclusion_sentence}" | |
| return paragraph | |
| def generate_paragraph_evaluate(paragraph, user_generate_paragraph_evaluate_prompt): | |
| """ | |
| 根据用户输入的段落，调用OpenAI API生成相关的段落分析。 | |
| """ | |
| user_content = f""" | |
| paragraph is {paragraph} | |
| {user_generate_paragraph_evaluate_prompt} | |
| """ | |
| messages = [ | |
| {"role": "system", "content": paragraph}, | |
| {"role": "user", "content": user_content} | |
| ] | |
| response_format = { "type": "json_object" } | |
| request_payload = { | |
| "model": "gpt-3.5-turbo", | |
| "messages": messages, | |
| "max_tokens": 500, | |
| "response_format": response_format | |
| } | |
| response = OPEN_AI_CLIENT.chat.completions.create(**request_payload) | |
| content = response.choices[0].message.content | |
| return content | |
| def generate_correct_grammatical_spelling_errors(eng_level, paragraph, user_correct_grammatical_spelling_errors_prompt): | |
| """ | |
| 根据用户输入的段落，调用OpenAI API生成相关的文法和拼字错误修正。 | |
| """ | |
| user_content = f""" | |
| level is {eng_level} | |
| paragraph is {paragraph} | |
| {user_correct_grammatical_spelling_errors_prompt} | |
| """ | |
| messages = [ | |
| {"role": "system", "content": paragraph}, | |
| {"role": "user", "content": user_content} | |
| ] | |
| response_format = { "type": "json_object" } | |
| request_payload = { | |
| "model": "gpt-3.5-turbo", | |
| "messages": messages, | |
| "max_tokens": 500, | |
| "response_format": response_format | |
| } | |
| response = OPEN_AI_CLIENT.chat.completions.create(**request_payload) | |
| content = response.choices[0].message.content | |
| return content | |
| def generate_refine_paragraph(eng_level, paragraph, user_refine_paragraph_prompt): | |
| """ | |
| 根据用户输入的段落，调用OpenAI API生成相关的段落改善建议。 | |
| """ | |
| user_content = f""" | |
| eng_level is {eng_level} | |
| paragraph is {paragraph} | |
| {user_refine_paragraph_prompt} | |
| """ | |
| messages = [ | |
| {"role": "system", "content": paragraph}, | |
| {"role": "user", "content": user_content} | |
| ] | |
| response_format = { "type": "json_object" } | |
| request_payload = { | |
| "model": "gpt-3.5-turbo", | |
| "messages": messages, | |
| "max_tokens": 500, | |
| "response_format": response_format | |
| } | |
| response = OPEN_AI_CLIENT.chat.completions.create(**request_payload) | |
| content = response.choices[0].message.content | |
| return content | |
| def paragraph_save_and_tts(paragraph_text): | |
| """ | |
| Saves the paragraph text and generates an audio file using OpenAI's TTS. | |
| """ | |
| try: | |
| # Call OpenAI's TTS API to generate speech from text | |
| response = OPEN_AI_CLIENT.audio.speech.create( | |
| model="tts-1", | |
| voice="alloy", | |
| input=paragraph_text, | |
| ) | |
| with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as temp_file: | |
| temp_file.write(response.content) | |
| # Get the file path of the temp file | |
| audio_path = temp_file.name | |
| # Return the path to the audio file along with the text | |
| return paragraph_text, audio_path | |
| except Exception as e: | |
| print(f"An error occurred while generating TTS: {e}") | |
| # Handle the error appropriately (e.g., return an error message or a default audio path) | |
| return paragraph_text, None | |
| 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 主題") | |
| 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("AI Generate Topic Sentences") | |
| topic_output = gr.Textbox(label="AI Generated Topic 主題") | |
| topic_input = gr.Textbox(label="Topic") | |
| gr.Markdown("## 3. Generate Points 要點") | |
| 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("AI Generate Points") | |
| points_output = gr.Textbox(label="AI Generated Points 要點") | |
| points_input = gr.Textbox(label="Points") | |
| gr.Markdown("## 4. Generate Topic Sentences 主題句") | |
| 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("AI Generate Topic Sentences") | |
| topic_sentence_output = gr.Textbox(label="AI Generated Topic Sentences 主題句") | |
| topic_sentence_input = gr.Textbox(label="Topic Sentences") | |
| gr.Markdown("## 5. Generate Supporting Sentence 支持句") | |
| 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("AI Generate Supporting Sentences") | |
| supporting_sentences_output = gr.Textbox(label="AI Generated Supporting Sentences 支持句", show_copy_button=True) | |
| supporting_sentences_input = gr.Textbox(label="Supporting Sentences") | |
| gr.Markdown("## 6. Conclusion sentence 結論句") | |
| 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 eng_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("AI Generate Conclusion Sentence") | |
| conclusion_sentence_output = gr.Textbox(label="AI Generated Conclusion Sentence 結論句", show_copy_button=True) | |
| conclusion_sentence_input = gr.Textbox(label="Conclusion Sentence") | |
| gr.Markdown("## 7. Paragraph Integration and Revision 段落確認與修訂") | |
| generate_paragraph_button = gr.Button("Generate Paragraph") | |
| paragraph_output = gr.Textbox(label="Generated Paragraph 完整段落", show_copy_button=True) | |
| paragraph_input = gr.Textbox(label="Paragraph") | |
| gr.Markdown("## 8. Evaluate 分析") | |
| default_user_generate_paragraph_evaluate_prompt = """ | |
| Based on the final paragraph provided, evaluate the writing in terms of content, organization, grammar, and vocabulary. Provide feedback in simple and supportive language. | |
| -- 根據上述的文章，以「內容（content）」層面評分。 | |
| - 評分等級有三級：beginner, intermediate, advanced. - 以繁體中文解釋 | |
| 評分結果以 JSON 格式輸出: content: { | |
| "content_level": "#beginner/intermediate/advanced", | |
| "content_explanation": "#中文解釋" | |
| } | |
| -- 根據上述的文章，以「組織（organization）」層面評分。 | |
| - 評分等級有三級：beginner, intermediate, advanced. - 以繁體中文解釋 | |
| 評分結果以 JSON 格式輸出: organization: { | |
| "organization_level": "#beginner/intermediate/advanced", | |
| "organization_explanation": "#中文解釋" | |
| } | |
| -- 根據上述的文章，以「文法和用法（Grammar and usage）」層面評分。 | |
| - 評分等級有三級：beginner, intermediate, advanced. - 以繁體中文解釋 | |
| 評分結果以 JSON 格式輸出: grammar_and_usage: { | |
| "GrammarAndUsage_level": "#beginner/intermediate/advanced", | |
| "GrammarAndUsage_explanation": "#中文解釋" | |
| } | |
| -- 根據上述的文章，以「詞彙（Vocabulary ）」層面評分。 | |
| - 評分等級有三級：beginner, intermediate, advanced. - 以繁體中文解釋 | |
| 評分結果以 JSON 格式輸出: vocabulary: { | |
| "Vocabulary_level": "#beginner/intermediate/advanced", | |
| "Vocabulary_explanation": "#中文解釋" | |
| } | |
| -- 根據上述的文章，以「連貫性和連接詞（Coherence and Cohesion）」層面評分。 | |
| - 評分等級有三級：beginner, intermediate, advanced. - 以繁體中文解釋 | |
| 評分結果以 JSON 格式輸出: coherence_and_cohesion: { | |
| "CoherenceAndCohesion_level": "#beginner/intermediate/advanced", | |
| "CoherenceAndCohesion_explanation": "#中文解釋" | |
| } | |
| 將上述的輸出為 JSON: | |
| {{ | |
| “content“: {content’s dict}, | |
| “organization“: {organization dict}, | |
| “grammar_and_usage“: {grammar_and_usage dict}, | |
| “vocabulary“: {vocabulary dict}, | |
| “coherence_and_cohesion“: {coherence_and_cohesion dict} | |
| }} | |
| """ | |
| user_generate_paragraph_evaluate_prompt = gr.Textbox(label="Paragraph evaluate Prompt", value=default_user_generate_paragraph_evaluate_prompt) | |
| generate_paragraph_evaluate_button = gr.Button("Save and Evaluate") | |
| paragraph_evaluate_output = gr.Textbox(label="Generated Paragraph evaluate 完整段落分析", show_copy_button=True) | |
| gr.Markdown("## 9. Correct Grammatical and Spelling Errors 修訂文法與拼字錯誤") | |
| default_user_correct_grammatical_spelling_errors_prompt = """ | |
| I'm aiming to improve my writing. | |
| Please assist me by "Correcting Grammatical and Spelling Errors" in the provided paragraph. | |
| For every correction you make, I'd like an "Explanation" to understand the reasoning behind it. | |
| - Paragraph for Correction: [paragraph split by punctuation mark] | |
| - The sentence to remain unchanged: [sentence_to_remain_unchanged] | |
| - When explaining, use Traditional Chinese (Taiwan, 繁體中文) for clarity. | |
| - But others(original, Correction, revised_paragraph) in English. | |
| - Make sure any revised vocabulary aligns with the eng_level. | |
| - Guidelines for Length and Complexity: Please keep explanations concise and straightforward, | |
| - Avoiding overly technical language. | |
| The response should strictly be in the below JSON format and nothing else: | |
| { | |
| "Corrections and Explanations": [ | |
| { "original": "# original_sentence1", "Correction": "#correction_1", "Explanation": "#explanation_1(in_traditional_chinese)" }, | |
| { "original": "# original_sentence2", "Correction": "#correction_2", "Explanation": "#explanation_2(in_traditional_chinese)" }, | |
| ... | |
| ], | |
| "Revised Paragraph": "#revised_paragraph" | |
| } | |
| """ | |
| user_correct_grammatical_spelling_errors_prompt = gr.Textbox(label="Correct Grammatical and Spelling Errors Prompt", value=default_user_correct_grammatical_spelling_errors_prompt) | |
| generate_correct_grammatical_spelling_errors_button = gr.Button("Correct Grammatical and Spelling Errors") | |
| correct_grammatical_spelling_errors_output = gr.Textbox(label="Correct Grammatical and Spelling Errors 修訂文法與拼字錯誤") | |
| paragraph_correct_grammatical_spelling_errors_input = gr.Textbox(label="Paragraph") | |
| gr.Markdown("## 10. Refine Paragraph 段落改善建議") | |
| default_user_refine_paragraph_prompt = """ | |
| I need assistance with revising a paragraph. Please "Refine" the "Revised Version 1" and immediately "Provide Explanations" for each suggestion you made. | |
| - Revised Version 1 (for correction): paragraph_ai_modification(split by 標點符號) | |
| - Do not modify the sentence: topicSentence" | |
| - Make sure any revised vocabulary aligns with the eng_level. | |
| - When explaining, use Traditional Chinese (Taiwan, 繁體中文) for clarity. | |
| - But others(Origin, Suggestion, revised_paragraph_v2) use English, that's very important. | |
| - Guidelines for Length and Complexity: | |
| Please keep explanations concise and straightforward, | |
| avoiding overly technical language. | |
| The response should strictly be in the below JSON format and nothing else: | |
| { | |
| "Suggestions and Explanations": [ | |
| { "Origin": "#original_text_1", "Suggestion": "#suggestion_1", "Explanation": "#explanation_1(in_traditional_chinese)" }, | |
| { "Origin": "#original_text_2", "Suggestion": "#suggestion_2", "Explanation": "#explanation_2(in_traditional_chinese)" }, | |
| ... ], | |
| "Revised Paragraph": "#revised_paragraph_v2" | |
| } | |
| """ | |
| user_refine_paragraph_prompt = gr.Textbox(label="Refine Paragraph Prompt", value=default_user_refine_paragraph_prompt) | |
| generate_refine_paragraph_button = gr.Button("Refine Paragraph") | |
| refine_output = gr.Textbox(label="Refine Paragraph 段落改善建議", show_copy_button=True) | |
| paragraph_refine_input = gr.Textbox(label="Paragraph 段落改善", show_copy_button=True) | |
| gr.Markdown("## 11. Save and Share") | |
| paragraph_save_button = gr.Button("Save and Share") | |
| paragraph_save_output = gr.Textbox(label="Save and Share") | |
| audio_output = gr.Audio(label="Generated Speech", type="filepath") | |
| 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=conclusion_sentence_output | |
| ) | |
| generate_paragraph_button.click( | |
| fn=generate_paragraph, | |
| inputs=[ | |
| topic_sentence_input, | |
| supporting_sentences_input, | |
| conclusion_sentence_input | |
| ], | |
| outputs=paragraph_output | |
| ) | |
| generate_paragraph_evaluate_button.click( | |
| fn=generate_paragraph_evaluate, | |
| inputs=[ | |
| paragraph_input, | |
| user_generate_paragraph_evaluate_prompt | |
| ], | |
| outputs=paragraph_evaluate_output | |
| ) | |
| generate_correct_grammatical_spelling_errors_button.click( | |
| fn=generate_correct_grammatical_spelling_errors, | |
| inputs=[ | |
| eng_level_input, | |
| paragraph_input, | |
| user_correct_grammatical_spelling_errors_prompt | |
| ], | |
| outputs=correct_grammatical_spelling_errors_output | |
| ) | |
| generate_refine_paragraph_button.click( | |
| fn=generate_refine_paragraph, | |
| inputs=[ | |
| eng_level_input, | |
| paragraph_correct_grammatical_spelling_errors_input, | |
| user_refine_paragraph_prompt | |
| ], | |
| outputs=refine_output | |
| ) | |
| paragraph_save_button.click( | |
| fn=paragraph_save_and_tts, | |
| inputs=[ | |
| paragraph_refine_input | |
| ], | |
| outputs=[ | |
| paragraph_save_output, | |
| audio_output | |
| ] | |
| ) | |
| demo.launch() | |