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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, | |
| ): | |
| 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!", | |
| ) | |
| os.system(f"python run_pysr_and_save.py --niterations {niterations} --binary_operators '{binary_operators}' --unary_operators '{unary_operators}' --col_to_fit {col_to_fit} --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=[], | |
| ), | |
| ], | |
| outputs=[ | |
| "dataframe", | |
| gr.outputs.Textbox(label="Error Log"), | |
| ], | |
| ) | |
| # Add file to the demo: | |
| demo.launch() | |
| if __name__ == "__main__": | |
| main() | |