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import os | |
import openai | |
from math import * | |
import math | |
import numpy | |
openai.api_key = os.getenv("OPENAI_API_KEY") | |
def get_prompt(question): | |
# Building the prompt from a question | |
return f"""This file only contains one function, and it's purpose is to answer the following question: | |
Question: "{question}" | |
\"\"\" | |
def answer_question(): | |
\"\"\" | |
This function returns the answer to the question asked in the docstring above. | |
\"\"\"""" | |
def get_response(question): | |
response = openai.Completion.create( | |
engine="davinci", | |
prompt=get_prompt(question), | |
temperature=0.6, | |
max_tokens=512, | |
top_p=1, | |
frequency_penalty=0, | |
presence_penalty=0, | |
stop=["def", "answer_question()", "if __name__", "print"] | |
) | |
return response["choices"][0]["text"] | |
if __name__ == "__main__": | |
while True: | |
question = input("\nGPT-Calc: ") | |
code = get_response(question) | |
eval_function = f"""def answer_question():{code}""" | |
# print(eval_function) | |
exec(eval_function) | |
answer = answer_question() | |
print("Answer: \033[1;32;40m" + str(answer) + "\033[39m \033[0;0m") |
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