Update layout

app.py

@@ -5,17 +5,12 @@ from io import StringIO
 import joblib
 import numpy as np
 import pandas as pd
-
 # page set up
 import streamlit as st
 from b3clf.descriptor_padel import compute_descriptors
 from b3clf.geometry_opt import geometry_optimize
-from b3clf.utils import (
-    get_descriptors,
-    predict_permeability,
-    scale_descriptors,
-    select_descriptors,
-)
+from b3clf.utils import (get_descriptors, predict_permeability,
+                         scale_descriptors, select_descriptors)
 from streamlit_ketcher import st_ketcher
 
 st.set_page_config(
@@ -147,38 +142,47 @@ def generate_predictions(
 st.title(":blue[BBB Permeability Prediction with Imbalanced Learning]")
 info_column, upload_column = st.columns(2)
 
+# download sample files
+with info_column:
+    st.subheader("About `B3clf`")
+    # fmt: off
+    st.markdown(
+        """
+        `B3clf` is a Python package for predicting the blood-brain barrier (BBB) permeability of small molecules using imbalanced learning. Source code is available at https://github.com/theochem/B3clf.""" #
+    )
+    # fmt: on
+    sdf_col, smi_col = st.columns(2)
+    with sdf_col:
+        # uneven columns
+        # st.columns((2, 1, 1, 1))
+        # two subcolumns for sample input files
+        # download sample sdf
+        # st.markdown(" \n \n")
+        with open("sample_input.sdf", "r") as file_sdf:
+            btn = st.download_button(
+                label="Download SDF sample file",
+                data=file_sdf,
+                file_name="sample_input.sdf",
+            )
+    with smi_col:
+        with open("sample_input_smiles.csv", "r") as file_smi:
+            btn = st.download_button(
+                label="Download SMILES sample file",
+                data=file_smi,
+                file_name="sample_input_smiles.csv",
+            )
 # Create a file uploader
 
 with upload_column:
-    st.subheader("Molecule Input")
+    st.subheader("Model Selection")
     with st.container():
-        # uneven columns
-        # st.columns((2, 1, 1, 1))
-        # two subcolumns for sample input files
-        sample_sdf_column, classifier_col = st.columns(2)
-        with sample_sdf_column:
-            # download sample sdf
-            with open("sample_input.sdf", "r") as file_sdf:
-                btn = st.download_button(
-                    label="Download SDF sample file",
-                    data=file_sdf,
-                    file_name="sample_input.sdf",
-                )
-        with classifier_col:
+        algorithm_col, resampler_col = st.columns(2)
+        # algorithm and resampling method selection column
+        with algorithm_col:
             classifier = st.selectbox(
                 label="Classification algorithm:",
                 options=("XGBoost", "kNN", "decision trees", "logsistical regression"),
             )
-
-        sample_smiles_column, resampler_col = st.columns(2)
-        with sample_smiles_column:
-            # download sample smiles
-            with open("sample_input_smiles.csv", "r") as file_smi:
-                btn = st.download_button(
-                    label="Download SMILES sample file",
-                    data=file_smi,
-                    file_name="sample_input_smiles.csv",
-                )
         with resampler_col:
             resampler = st.selectbox(
                 label="Resampling method:",
@@ -206,14 +210,7 @@ with upload_column:
 #     st.write(file.read().decode("utf-8"))
 # st.write(file)
 
-with info_column:
-    st.subheader("About `B3clf`")
-    # fmt: off
-    st.markdown(
-        """
-        `B3clf` is a Python package for predicting the blood-brain barrier (BBB) permeability of small molecules using imbalanced learning. Source code is available at https://github.com/theochem/B3clf.""" #
-    )
-    # fmt: on
+
 
 feature_column, prediction_column = st.columns(2)
 with feature_column: