steve7909 commited on
Commit
0b37375
1 Parent(s): 650a41f

before share button attempt

Browse files
Files changed (1) hide show
  1. app.py +2 -9
app.py CHANGED
@@ -1,11 +1,3 @@
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- # -*- coding: utf-8 -*-
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- """translation practice.ipynb
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-
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- Automatically generated by Colaboratory.
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-
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- Original file is located at
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- https://colab.research.google.com/drive/1KrnodZGBZrUFdaJ9FIn8IhtWtCL7peoE
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- """
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  import requests
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  import gradio as gr
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  from dotenv import load_dotenv
@@ -90,6 +82,7 @@ def predict(message, history):
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  Encourage the student by specifying the strengths of their writing.
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  DO NOT PROVIDE THE CORRECT ENGLISH TRANSLATION until the student gets the correct translation. Let the student work it out.
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  Provide your feedback as a list in the format: a, b, c etc.
 
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  Execute the following tasks step by step:
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  1. Ask the student to translate the following sentence from Japanese to English: {japanese_sentence}. Here is the English translation for reference: {english_sentence}
@@ -114,7 +107,7 @@ def predict(message, history):
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  history_langchain_format.append(HumanMessage(content=message))
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- debug_print("### Full history: ", history_langchain_format)
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  gpt_response = llm(history_langchain_format)
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  return gpt_response.content
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  import requests
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  import gradio as gr
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  from dotenv import load_dotenv
 
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  Encourage the student by specifying the strengths of their writing.
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  DO NOT PROVIDE THE CORRECT ENGLISH TRANSLATION until the student gets the correct translation. Let the student work it out.
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  Provide your feedback as a list in the format: a, b, c etc.
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+ Do not respond in Japanese - always respond in English even if the student uses Japanese to with you.
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  Execute the following tasks step by step:
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  1. Ask the student to translate the following sentence from Japanese to English: {japanese_sentence}. Here is the English translation for reference: {english_sentence}
 
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  history_langchain_format.append(HumanMessage(content=message))
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+ #debug_print("### Full history: ", history_langchain_format)
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  gpt_response = llm(history_langchain_format)
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  return gpt_response.content
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