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import gradio as gr
import os
import tempfile
import pandas as pd
empty_df = pd.DataFrame(
{
"equation": [],
"loss": [],
"complexity": [],
}
)
os.system("bash install_pysr.sh")
def greet(
file_obj: tempfile._TemporaryFileWrapper,
col_to_fit: str,
niterations: int,
binary_operators: list,
unary_operators: list,
force_run: bool,
):
if col_to_fit == "":
return (
empty_df,
"Please enter a column to predict!",
)
if len(binary_operators) == 0 and len(unary_operators) == 0:
return (
empty_df,
"Please select at least one operator!",
)
if file_obj is None:
return (
empty_df,
"Please upload a CSV file!",
)
# Look at some statistics of the file:
df = pd.read_csv(file_obj.name)
if len(df) == 0:
return (
empty_df,
"The file is empty!",
)
if len(df.columns) == 1:
return (
empty_df,
"The file has only one column!",
)
if col_to_fit not in df.columns:
return (
empty_df,
"The column to predict is not in the file!",
)
if len(df) > 1000 and not force_run:
return (
empty_df,
"You have uploaded a file with more than 2000 rows. "
"This will take very long to run. "
"Please upload a subsample of the data, "
"or check the box 'Ignore Warnings'."
)
binary_operators = str(binary_operators).replace("'", '"')
unary_operators = str(unary_operators).replace("'", '"')
os.system(
f"python run_pysr_and_save.py "
f"--niterations {niterations} "
f"--binary_operators '{binary_operators}' "
f"--unary_operators '{unary_operators}' "
f"--col_to_fit {col_to_fit} "
f"--filename {file_obj.name}"
)
df = pd.read_csv("pysr_output.csv")
error_log = open("error.log", "r").read()
return df, error_log
def main():
demo = gr.Interface(
fn=greet,
description="PySR Demo",
inputs=[
gr.inputs.File(label="Upload a CSV File"),
gr.inputs.Textbox(label="Column to Predict", placeholder="y"),
gr.inputs.Slider(
minimum=1,
maximum=1000,
default=40,
label="Number of iterations",
),
gr.inputs.CheckboxGroup(
choices=["+", "-", "*", "/", "^"],
label="Binary Operators",
default=["+", "-", "*", "/"],
),
gr.inputs.CheckboxGroup(
choices=["sin", "cos", "exp", "log"],
label="Unary Operators",
default=[],
),
gr.inputs.Checkbox(
default=False,
label="Ignore Warnings",
)
],
outputs=[
"dataframe",
gr.outputs.Textbox(label="Error Log"),
],
)
# Add file to the demo:
demo.launch()
if __name__ == "__main__":
main()
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