Fix typos

app.py

@@ -5,17 +5,13 @@ 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 PIL import Image
 from streamlit_extras.let_it_rain import rain
 from streamlit_ketcher import st_ketcher
 
@@ -35,9 +31,9 @@ st.set_page_config(
 keep_features = "no"
 keep_sdf = "no"
 classifiers_dict = {
-    "decision trees": "dtree",
+    "decision tree": "dtree",
     "kNN": "knn",
-    "logsistical regression": "logreg",
+    "logistic regression": "logreg",
     "XGBoost": "xgb",
 }
 resample_methods_dict = {
@@ -54,6 +50,7 @@ pandas_display_options = {
 }
 
 
+@st.cache_resource
 def generate_predictions(
     input_fname: str,
     sep: str = "\s+|\t+",
@@ -132,8 +129,24 @@ with info_column:
     # 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.""" #
+        `B3clf` is a Python package for predicting the blood-brain barrier (BBB) permeability of small molecules using imbalanced learning. It supports decision tree, XGBoost, kNN, logistical regression and 5 resampling strategies (SMOTE, Borderline SMOTE, k-means SMOTE and ADASYN). The workflow of `B3clf` is summarized as below. The Source code and more details are available at https://github.com/theochem/B3clf."""
     )
+    # text_body = '''
+    # `B3clf` is a Python package for predicting the blood-brain barrier (BBB) permeability of small molecules using imbalanced learning. It supports decision tree, XGBoost, kNN, logistical regression and 5 resampling strategies (SMOTE, Borderline SMOTE, k-means SMOTE and ADASYN). The workflow of `B3clf` is summarized as below. The Source code and more details are available at https://github.com/theochem/B3clf.
+    # '''
+    # st.markdown(f'<p align="justify">{text_body}</p>',
+    #             unsafe_allow_html=True)
+
+    # image = Image.open("images/b3clf_workflow.png")
+    # st.image(image=image, use_column_width=True)
+
+    # image_path = "images/b3clf_workflow.png"
+    # image_width_percent = 80
+    # info_column.markdown(
+    #     f'<img src="{image_path}" style="max-width: {image_width_percent}%; height: auto;">',
+    #     unsafe_allow_html=True
+    #     )
+
     # fmt: on
     sdf_col, smi_col = st.columns(2)
     with sdf_col:
@@ -165,7 +178,7 @@ with upload_column:
         with algorithm_col:
             classifier = st.selectbox(
                 label="Classification Algorithm:",
-                options=("XGBoost", "kNN", "decision trees", "logsistical regression"),
+                options=("XGBoost", "kNN", "decision tree", "logistic regression"),
             )
         with resampler_col:
             resampler = st.selectbox(
@@ -276,7 +289,10 @@ if submit_job_button:
                 selected_result_rows = np.min(
                     [results.shape[0], pandas_display_options["line_limit"]]
                 )
-                st.dataframe(results.iloc[:selected_result_rows, :], hide_index=True)
+                results_df_display = results.iloc[
+                    :selected_result_rows, :
+                ].style.format({"B3clf_predicted_probability": "{:.6f}".format})
+                st.dataframe(results_df_display, hide_index=True)
                 # Add a button to download the predictions as a CSV file
                 predictions_csv = results.to_csv(index=True)
                 results_file_name = file.name.split(".")[0] + "_b3clf_predictions.csv"