import openai import gradio import os import re openai.api_key = os.environ.get("API_TOKEN") messages = [{"role": "system", "content": "You are an education expert who can explain grammar mistakes. You are bilingual in English and Japanese"}] MAX_TOKENS = 8192 MAX_HISTORY = 1 def CustomChatGPT(user_input): global messages essay_keywords = ["essay", "エッセイ", "論文"] action_keywords = ["write", "make", "create", "生成", "作成", "書く"] if any(re.search(f"{action_kw}.*{essay_kw}", user_input.lower()) for action_kw in action_keywords for essay_kw in essay_keywords): return "I'm sorry, I cannot write an essay for you." # Clear the messages list before adding new messages messages = [{"role": "system", "content": "You are an education expert who can explain grammar mistakes. You are bilingual in English and Japanese"}] user_message = {"role": "user", "content": f"Step 1: Correct the text I give. Be especially strict when it comes to sentence fragments. Step 2: Explain all the grammar and spelling mistakes for an EFL student. Put each explanation in a bullet point: [{user_input}]"} messages.append(user_message) while True: response = openai.ChatCompletion.create( model="gpt-3.5-turbo-16k", messages=messages ) total_tokens = response['usage']['total_tokens'] if total_tokens < MAX_TOKENS: break ChatGPT_reply = response["choices"][0]["message"]["content"] messages.append({"role": "assistant", "content": ChatGPT_reply}) return ChatGPT_reply # Add text instructions on top of the input and output boxes input_text = "ここに訂正してほしい英語の作文を置いてください。そして「Submit」を押してください:" output_text = "訂正と説明はここに表示されます:" instructions = "このアプリケーションは、文法と綴りをチェックするために使用できます。アプリは、1つのパラグラフずつ入力する場合に最適に機能します。例えば、3つのパラグラフから成る作文をチェックしたい場合は、それぞれのパラグラフを「Submit」してください。つまり、プログラムを3回実行し、各パラグラフごとに1回ずつ実行してください。" # Modify the Gradio interface to include the text instructions and image demo = gradio.Interface(fn=CustomChatGPT, inputs=gradio.inputs.Textbox(lines=5, label=input_text), outputs=gradio.outputs.Textbox(label=output_text), title="Teacher Jihan's Grammar Checker", description=instructions) demo.launch(share=False, debug=True)