app.py

import itertools as it
import os
import tempfile
from io import StringIO

import joblib
import numpy as np
import pandas as pd
import pkg_resources
# 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, scale_descriptors, select_descriptors
# from PIL import Image
from streamlit_extras.let_it_rain import rain
from streamlit_ketcher import st_ketcher

from utils import generate_predictions, load_all_models

st.cache_data.clear()

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!"
    # }
)


keep_features = "no"
keep_sdf = "no"
classifiers_dict = {
    "decision tree": "dtree",
    "kNN": "knn",
    "logistic regression": "logreg",
    "XGBoost": "xgb",
}
resample_methods_dict = {
    "random undersampling": "classic_RandUndersampling",
    "SMOTE": "classic_SMOTE",
    "Borderline SMOTE": "borderline_SMOTE",
    "k-means SMOTE": "kmeans_SMOTE",
    "ADASYN": "classic_ADASYN",
    "no resampling": "common",
}

pandas_display_options = {
    "line_limit": 50,
}
mol_features = None
info_df = None
results = None
temp_file_path = None
all_models = load_all_models()

# Create the Streamlit app
st.title(":blue[BBB Permeability Prediction with Imbalanced Learning]")
info_column, upload_column = st.columns(2)

# inatialize the molecule features and info dataframe session state
if "mol_features" not in st.session_state:
    st.session_state.mol_features = None
if "info_df" not in st.session_state:
    st.session_state.info_df = None


# 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. 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. This project is supported by Digital Research Alliance of Canada (originally known as Compute Canada) and NSERC. This project is maintained by QC-Dev comminity. For further information and inquiries please contact us at qcdevs@gmail.com."""
    )
    st.text(" \n")
    # 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:
        # 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("Model Selection")
    with st.container():
        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 tree", "logistic regression"),
            )
        with resampler_col:
            resampler = st.selectbox(
                label="Resampling Method:",
                options=(
                    "ADASYN",
                    "random undersampling",
                    "Borderline SMOTE",
                    "k-means SMOTE",
                    "SMOTE",
                    "no resampling",
                ),
            )

        # horizontal line
        st.divider()
        # upload_col, submit_job_col = st.columns((2, 1))
        upload_col, _, submit_job_col, _ = st.columns((4, 0.05, 1, 0.05))
        # upload file column
        with upload_col:
            # session state tracking of the file uploader
            if "uploaded_file" not in st.session_state:
                st.session_state.uploaded_file = None
            if "uploaded_file_changed" not in st.session_state:
                st.session_state.uploaded_file_changed = False

            # def update_uploader_session_info():
            #     """Update the session state of the file uploader."""
            #     st.session_state.uploaded_file = uploaded_file

            uploaded_file = st.file_uploader(
                label="Upload a CSV, SDF, TXT or SMI file",
                type=["csv", "sdf", "txt", "smi"],
                help="Input molecule file only supports *.csv, *.sdf, *.txt and *.smi.",
                accept_multiple_files=False,
                # key="uploaded_file",
                # on_change=update_uploader_session_info,
            )

                # when new file is the same as the previous one
                # else:
                #     st.session_state.uploaded_file_changed = False
                # st.session_state.uploaded_file = uploaded_file

            # set session state for the file uploader
            # st.write(f"the state of uploaded file: {st.session_state.uploaded_file}")
            # st.write(f"the state of uploaded file changed: {st.session_state.uploaded_file_changed}")

        # submit job column
        with submit_job_col:
            st.text(" \n")
            st.text(" \n")
            st.markdown(
                "<div style='display: flex; justify-content: center;'>",
                unsafe_allow_html=True,
            )
            submit_job_button = st.button(
                label="Submit Job", type="secondary", key="job_button"
            )
        # submit_job_col.markdown("<div style="display: flex; justify-content: center;">",
        #                         unsafe_allow_html=True)
        # submit_job_button = submit_job_col.button(
        #     label="Submit job", key="submit_job_button", type="secondary"
        # )
        # submit_job_col.markdown("</div>", unsafe_allow_html=True)


# 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)


feature_column, prediction_column = st.columns(2)
with feature_column:
    st.subheader("Molecular 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")


st.write(
    f"the state of uploaded file changed before checking: {st.session_state.uploaded_file_changed}"
)
# Generate predictions when the user uploads a file
# if submit_job_button:

if submit_job_button:
    if uploaded_file:
        # st.write(f"the uploaded file: {uploaded_file}")
        # when new file is uploaded is different from thprevious one
        if st.session_state.uploaded_file != uploaded_file:
            st.session_state.uploaded_file_changed = True
        else:
            st.session_state.uploaded_file_changed = False
        st.session_state.uploaded_file = uploaded_file
    # when new file is uploaded
    # update_uploader_session_info()
    st.write(
        f"the state of uploaded file changed after checking: {st.session_state.uploaded_file_changed}"
    )
    if st.session_state.uploaded_file_changed:
        temp_dir = tempfile.mkdtemp()
        # Create a temporary file path for the uploaded file
        temp_file_path = os.path.join(temp_dir, uploaded_file.name)
        # Save the uploaded file to the temporary file path
        with open(temp_file_path, "wb") as temp_file:
            temp_file.write(uploaded_file.read())

        mol_features, info_df, results = generate_predictions(
            input_fname=temp_file_path,
            sep="\s+|\t+",
            clf=classifiers_dict[classifier],
            _models_dict=all_models,
            sampling=resample_methods_dict[resampler],
            time_per_mol=120,
            mol_features=None,
            info_df=None,
        )
        st.session_state.mol_features = mol_features
        st.session_state.info_df = info_df
    else:
        mol_features, info_df, results = generate_predictions(
            input_fname=None,
            sep="\s+|\t+",
            clf=classifiers_dict[classifier],
            _models_dict=all_models,
            sampling=resample_methods_dict[resampler],
            time_per_mol=120,
            mol_features=st.session_state.mol_features,
            info_df=st.session_state.info_df,
        )

    # feture table
    with feature_column:
        if mol_features is not None:
            selected_feature_rows = np.min(
                [mol_features.shape[0], pandas_display_options["line_limit"]]
            )
            st.dataframe(mol_features.iloc[:selected_feature_rows, :], hide_index=False)
            # placeholder_features.dataframe(mol_features, hide_index=False)
            feature_file_name = uploaded_file.name.split(".")[0] + "_b3clf_features.csv"
            features_csv = mol_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")
        if results is not None:
            # Display the predictions in a table
            selected_result_rows = np.min(
                [results.shape[0], pandas_display_options["line_limit"]]
            )
            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 = (
                uploaded_file.name.split(".")[0] + "_b3clf_predictions.csv"
            )
            st.download_button(
                "Download predictions as CSV",
                data=predictions_csv,
                file_name=results_file_name,
            )
            # indicate the success of the job
            # rain(
            #     emoji="🎈",
            #     font_size=54,
            #     falling_speed=5,
            #     animation_length=10,
            # )
    st.balloons()


# 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)

# add google analytics
st.markdown(
    """
    <!-- Google tag (gtag.js) -->
    <script async src="https://www.googletagmanager.com/gtag/js?id=G-WG8QYRELP9"></script>
    <script>
      window.dataLayer = window.dataLayer || [];
      function gtag(){dataLayer.push(arguments);}
      gtag("js", new Date());

      gtag("config", "G-WG8QYRELP9");
    </script>
    """,
    unsafe_allow_html=True,
)