Use wide mode

app.py

@@ -5,22 +5,34 @@ 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
 
-# from geometry_opt import geometry_optimize
+st.set_page_config(
+    page_title="BBB Permeability Prediction with Imbalanced Learning",
+    page_icon="🧊",
+    layout="wide",
+    # initial_sidebar_state="expanded",
+    # menu_items={
+    #     'Get Help': 'https://www.extremelycoolapp.com/help',
+    #     'Report a bug': "https://www.extremelycoolapp.com/bug",
+    #     'About': "# This is a header. This is an *extremely* cool app!"
+    # }
+)
 
 # Load the pre-trained model and feature scaler
-model = joblib.load(
-    "pre_trained/b3clf_knn_kmeans_SMOTE.joblib"
-)
-scaler = joblib.load(
-    "pre_trained/b3clf_scaler.joblib"
-)
+model = joblib.load("pre_trained/b3clf_knn_kmeans_SMOTE.joblib")
+scaler = joblib.load("pre_trained/b3clf_scaler.joblib")
 
 
 # Define a function to generate predictions
@@ -82,7 +94,6 @@ def generate_predictions(
     # Get computed descriptors
     X_features, info_df = get_descriptors(df=df_features)
 
-
     # Select descriptors
     X_features = select_descriptors(df=X_features)
 
@@ -124,10 +135,11 @@ st.title("BBB Permeability Prediction with Imbalanced Learning")
 # 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,
-                        )
+file = st.file_uploader(
+    "Upload a CSV or SDF file",
+    type=["csv", "sdf", "txt"],
+    # 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:
@@ -141,7 +153,7 @@ if file:
     # Create a temporary file path for the uploaded file
     temp_file_path = os.path.join(temp_dir, file.name)
     # Save the uploaded file to the temporary file path
-    with open(temp_file_path, 'wb') as temp_file:
+    with open(temp_file_path, "wb") as temp_file:
         temp_file.write(file.read())
     X_features, results = generate_predictions(temp_file_path)
 
@@ -162,5 +174,7 @@ if file:
             predictions_csv = results.to_csv(index=False)
             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
+                "Download predictions as CSV",
+                data=predictions_csv,
+                file_name=results_file_name,
             )