Move everything to app.py

gui/app.py

@@ -1,7 +1,10 @@
 import gradio as gr
+import numpy as np
 import os
-import tempfile
 import pandas as pd
+import pysr
+import tempfile
+from typing import Optional
 
 empty_df = pd.DataFrame(
     {
@@ -13,7 +16,7 @@ empty_df = pd.DataFrame(
 
 
 def greet(
-    file_obj: tempfile._TemporaryFileWrapper,
+    file_obj: Optional[tempfile._TemporaryFileWrapper],
     col_to_fit: str,
     niterations: int,
     maxsize: int,
@@ -65,19 +68,39 @@ def greet(
 
     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"--maxsize {maxsize} "
-        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(file_obj)
+    y = np.array(df[col_to_fit])
+    X = df.drop([col_to_fit], axis=1)
+
+    model = pysr.PySRRegressor(
+        progress=False,
+        verbosity=0,
+        maxsize=maxsize,
+        niterations=niterations,
+        binary_operators=binary_operators,
+        unary_operators=unary_operators,
     )
-    df = pd.read_csv("pysr_output.csv")
-    error_log = open("error.log", "r").read()
-    return df, error_log
+    model.fit(X, y)
+
+    df = model.equations_[["equation", "loss", "complexity"]]
+    # Convert all columns to string type:
+    df = df.astype(str)
+    msg = (
+            "Success!\n"
+            f"You may run the model locally (faster) with "
+            f"the following parameters:"
+            +f"""
+model = PySRRegressor(
+    niterations={niterations},
+    binary_operators={str(binary_operators)},
+    unary_operators={str(unary_operators)},
+    maxsize={maxsize},
+)
+model.fit(X, y)""")
 
+    df.to_csv("pysr_output.csv", index=False)
+    return df, msg
 
 def main():
     demo = gr.Interface(

gui/run_pysr_and_save.py

@@ -1,71 +0,0 @@
-import os
-import pandas as pd
-import traceback as tb
-import numpy as np
-from pysr import PySRRegressor
-from argparse import ArgumentParser
-
-# Args:
-# niterations
-# binary_operators
-# unary_operators
-# col_to_fit
-
-empty_df = pd.DataFrame(
-    {
-        "equation": [],
-        "loss": [],
-        "complexity": [],
-    }
-)
-
-if __name__ == "__main__":
-    parser = ArgumentParser()
-    parser.add_argument("--niterations", type=int)
-    parser.add_argument("--maxsize", type=int)
-    parser.add_argument("--binary_operators", type=str)
-    parser.add_argument("--unary_operators", type=str)
-    parser.add_argument("--col_to_fit", type=str)
-    parser.add_argument("--filename", type=str)
-    args = parser.parse_args()
-    niterations = args.niterations
-    binary_operators = eval(args.binary_operators)
-    unary_operators = eval(args.unary_operators)
-    col_to_fit = args.col_to_fit
-    filename = args.filename
-    maxsize = args.maxsize
-
-
-    df = pd.read_csv(filename)
-    y = np.array(df[col_to_fit])
-    X = df.drop([col_to_fit], axis=1)
-
-    model = PySRRegressor(
-        progress=False,
-        verbosity=0,
-        maxsize=maxsize,
-        niterations=niterations,
-        binary_operators=binary_operators,
-        unary_operators=unary_operators,
-    )
-    model.fit(X, y)
-
-    df = model.equations_[["equation", "loss", "complexity"]]
-    # Convert all columns to string type:
-    df = df.astype(str)
-    error_message = (
-            "Success!\n"
-            f"You may run the model locally (faster) with "
-            f"the following parameters:"
-            +f"""
-model = PySRRegressor(
-    niterations={niterations},
-    binary_operators={str(binary_operators)},
-    unary_operators={str(unary_operators)},
-    maxsize={maxsize},
-)
-model.fit(X, y)""")
-
-    df.to_csv("pysr_output.csv", index=False)
-    with open("error.log", "w") as f:
-        f.write(error_message)