Update layout

.streamlit/config.toml

@@ -1,2 +1,5 @@
 [theme]
 base="light"
+
+[server]
+maxUploadSize=2

app.py

@@ -5,22 +5,17 @@ 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(
     page_title="BBB Permeability Prediction with Imbalanced Learning",
-    page_icon="🧊",
+    # page_icon="🧊",
     layout="wide",
     # initial_sidebar_state="expanded",
     # menu_items={
@@ -130,21 +125,48 @@ def generate_predictions(
 
 
 # Create the Streamlit app
-st.title("BBB Permeability Prediction with Imbalanced Learning")
+st.title(":blue[BBB Permeability Prediction with Imbalanced Learning]")
+info_column, upload_column = st.columns(2)
 
 # Create a file uploader
 
-st.subheader("Input Data")
-file = st.file_uploader(
-    "Upload a CSV or SDF file",
-    type=["csv", "sdf", "txt"],
-    # accept_multiple_files=False,
-)
+with upload_column:
+    st.subheader("Molecule Input")
+    file = st.file_uploader(
+        label="Upload a CSV, SDF or TXT file",
+        type=["csv", "sdf", "txt"],
+        help="Input molecule file and only text files are supported.",
+        # accept_multiple_files=False,
+    )
 # st.write("The content of the file will be displayed below once uploaded.")
-if file:
-    # if "csv" in file.name or "txt" in file.name:
-    #     st.write(file.read().decode("utf-8"))
-    st.write(file)
+# if file:
+# if "csv" in file.name or "txt" in file.name:
+#     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:
+    st.subheader("Features")
+    placeholder_features = st.empty()
+    # placeholder_features = pd.DataFrame(index=[1, 2, 3, 4],
+    #                                     columns=["ID", "nAcid", "ALogP", "Alogp2",
+    #                                              "AMR", "naAromAtom", "nH", "nN"])
+    # st.dataframe(placeholder_features)
+    placeholder_features.text("molecular features")
+
+with prediction_column:
+    st.subheader("Predictions")
+    # placeholder_predictions = st.empty()
+    # placeholder_predictions.text("prediction")
 
 
 # Generate predictions when the user uploads a file
@@ -157,24 +179,39 @@ if file:
         temp_file.write(file.read())
     X_features, results = generate_predictions(temp_file_path)
 
-    feature_column, prediction_column = st.columns(2)
-
     # feture table
     with feature_column:
-        st.subheader("Features")
         st.dataframe(X_features)
+        # placeholder_features.dataframe(X_features, hide_index=False)
+        feature_file_name = file.name.split(".")[0] + "_b3clf_features.csv"
+        features_csv = X_features.to_csv(index=True)
+        st.download_button(
+            "Download features as CSV",
+            data=features_csv,
+            file_name=feature_file_name,
+        )
 
     # prediction table
     with prediction_column:
-        st.subheader("Predictions")
+        # st.subheader("Predictions")
         if results is not None:
             # Display the predictions in a table
-            st.dataframe(results)
+            st.dataframe(results, hide_index=True)
             # Add a button to download the predictions as a CSV file
-            predictions_csv = results.to_csv(index=False)
+            predictions_csv = results.to_csv(index=True)
             results_file_name = file.name.split(".")[0] + "_b3clf_predictions.csv"
             st.download_button(
                 "Download predictions as CSV",
                 data=predictions_csv,
                 file_name=results_file_name,
             )
+
+# hide footer
+# https://github.com/streamlit/streamlit/issues/892
+hide_streamlit_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)