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Update app.py
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app.py
CHANGED
@@ -1,51 +1,11 @@
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import
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import
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import os
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import re
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MAX_HISTORY = 1
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def CustomChatGPT(user_input):
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global messages
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essay_keywords = ["essay", "エッセイ", "論文"]
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action_keywords = ["write", "make", "create", "生成", "作成", "書く"]
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if any(re.search(f"{action_kw}.*{essay_kw}", user_input.lower()) for action_kw in action_keywords for essay_kw in essay_keywords):
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return "I'm sorry, I cannot write an essay for you."
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# Clear the messages list before adding new messages
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messages = [{"role": "system", "content": "You are an education expert who can correct essays. You are bilingual in English and Japanese"}]
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user_message = {"role": "user", "content": f"Step 1, find make grammar and spelling and corrections. Step 2, show the corrected version. Step 3, Explain in detail all the errors from what I originally wrote. Put each explanation in a bullet point: [{user_input}]"}
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messages.append(user_message)
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while True:
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response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo",
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messages=messages
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)
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total_tokens = response['usage']['total_tokens']
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if total_tokens < MAX_TOKENS:
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break
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ChatGPT_reply = response["choices"][0]["message"]["content"]
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messages.append({"role": "assistant", "content": ChatGPT_reply})
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return ChatGPT_reply
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# Add text instructions on top of the input and output boxes
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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:"
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output_text = "訂正と説明はここに表示されます:"
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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、修正した作文を提供し、それに続いて修正の説明を示します。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。"
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# Modify the Gradio interface to include the text instructions and image
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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 Checking Assistant", description=instructions)
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demo.launch(share=False, debug=True)
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import gradio as gr
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from transformers import pipeline
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def huggingface_model(dummy):
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return "Hello"
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iface = gr.Interface(fn=huggingface_model,
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inputs=gr.inputs.Text(),
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outputs='text')
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iface.launch()
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