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# + tags=["hide_inp"]
desc = """
### Word Problem Solver
Chain that solves a math word problem by first generating and then running Python code. [](https://colab.research.google.com/github/srush/MiniChain/blob/master/examples/math_demo.ipynb)
(Adapted from Dust [maths-generate-code](https://dust.tt/spolu/a/d12ac33169))
"""
# -
# $
from minichain import show, prompt, OpenAI, Python, GradioConf
import gradio as gr
@prompt(OpenAI(), template_file="math.pmpt.tpl",
gradio_conf=GradioConf(block_input=gr.Markdown))
def math_prompt(model, question
):
"Prompt to call GPT with a Jinja template"
return model(dict(question=question))
@prompt(Python(), template="import math\n{{code}}")
def python(model, code):
"Prompt to call Python interpreter"
code = "\n".join(code.strip().split("\n")[1:-1])
return model(dict(code=code))
def math_demo(question):
"Chain them together"
return python(math_prompt(question))
# $
# + tags=["hide_inp"]
gradio = show(math_demo,
examples=["What is the sum of the powers of 3 (3^i) that are smaller than 100?",
"What is the sum of the 10 first positive integers?",],
# "Carla is downloading a 200 GB file. She can download 2 GB/minute, but 40% of the way through the download, the download fails. Then Carla has to restart the download from the beginning. How load did it take her to download the file in minutes?"],
subprompts=[math_prompt, python],
description=desc,
code=open("math_demo.py", "r").read().split("$")[1].strip().strip("#").strip(),
)
if __name__ == "__main__":
gradio.queue().launch()
# -
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