Move more parts to other files

gui/app.py

@@ -2,7 +2,7 @@ import gradio as gr
 
 from .data import test_equations
 from .plots import replot, replot_pareto
-from .processing import process
+from .processing import processing
 
 
 def _data_layout():
@@ -196,7 +196,7 @@ def main():
                 blocks["run"] = gr.Button()
 
         blocks["run"].click(
-            process,
+            processing,
             inputs=[
                 blocks[k]
                 for k in [

gui/data.py

@@ -20,3 +20,25 @@ def generate_data(s: str, num_points: int, noise_level: float, data_seed: int):
     noise = rstate.normal(0, noise_level, y.shape)
     y_noisy = y + noise
     return pd.DataFrame({"x": x}), y_noisy
+
+
+def read_csv(file_input: str, force_run: bool):
+    # Look at some statistics of the file:
+    df = pd.read_csv(file_input)
+    if len(df) == 0:
+        raise ValueError("The file is empty!")
+    if len(df.columns) == 1:
+        raise ValueError("The file has only one column!")
+    if len(df) > 10_000 and not force_run:
+        raise ValueError(
+            "You have uploaded a file with more than 10,000 rows. "
+            "This will take very long to run. "
+            "Please upload a subsample of the data, "
+            "or check the box 'Ignore Warnings'.",
+        )
+
+    col_to_fit = df.columns[-1]
+    y = np.array(df[col_to_fit])
+    X = df.drop([col_to_fit], axis=1)
+
+    return X, y

gui/processing.py

@@ -7,7 +7,7 @@ from pathlib import Path
 import numpy as np
 import pandas as pd
 
-from .data import generate_data
+from .data import generate_data, read_csv
 
 EMPTY_DF = lambda: pd.DataFrame(
     {
@@ -18,7 +18,7 @@ EMPTY_DF = lambda: pd.DataFrame(
 )
 
 
-def process(
+def processing(
     file_input,
     force_run,
     test_equation,
@@ -43,30 +43,10 @@ def process(
 ):
     """Load data, then spawn a process to run the greet function."""
     if file_input is not None:
-        # Look at some statistics of the file:
-        df = pd.read_csv(file_input)
-        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 len(df) > 10_000 and not force_run:
-            return (
-                EMPTY_DF(),
-                "You have uploaded a file with more than 10,000 rows. "
-                "This will take very long to run. "
-                "Please upload a subsample of the data, "
-                "or check the box 'Ignore Warnings'.",
-            )
-
-        col_to_fit = df.columns[-1]
-        y = np.array(df[col_to_fit])
-        X = df.drop([col_to_fit], axis=1)
+        try:
+            X, y = read_csv(file_input, force_run)
+        except ValueError as e:
+            return (EMPTY_DF(), str(e))
     else:
         X, y = generate_data(test_equation, num_points, noise_level, data_seed)