Initial draft (#252)

* Initial Commit

* Added the pipeline workflow for Docker and HuggingFace

* Added Dockerfile

* Added intro and maxmin interface

* Added the configuration file for HuggingFace space

* Update README for webapp

* Removed Selector package from webapp

* Install selector package in the container directly

* Optimize the Dockerfile for simplicity

* Removed unused files

* Optimized the workflow

* Test the workflow

* Fix unused code in Dockerfile

* Added instructions for running webapp in README

* Unified the name of selector

* Added a common module to reduce duplication

* Optimized MaxMin

* Added MaxSum Interface

* Added DISE Interface

* Added OptiSim Interface

* Added GridPartition Interface

* Added Medoid Interface

* Added helper module for converter

* Added Converter Interface

* Fixed some bug with the container environment

* Fixed ordering problem in README

---------

Co-authored-by: Jun Xia <xiaj8@uci.edu>

.github/CODE_OF_CONDUCT.md

@@ -0,0 +1,4 @@
+# Contributor Covenant Code of Conduct
+
+It is recommended to follow the code of conduct as described in
+https://qcdevs.org/guidelines/QCDevsCodeOfConduct/.

.github/CONTRIBUTING.md

@@ -0,0 +1,65 @@
+# How to contribute
+
+We welcome contributions from external contributors, and this document
+describes how to merge code changes into this selector.
+
+## Getting Started
+
+* Make sure you have a [GitHub account](https://github.com/signup/free).
+* [Fork](https://help.github.com/articles/fork-a-repo/) this repository on GitHub.
+* On your local machine,
+  [clone](https://help.github.com/articles/cloning-a-repository/) your fork of
+  the repository.
+
+## Making Changes
+
+* Add some really awesome code to your local fork.  It's usually a
+  [good idea](http://blog.jasonmeridth.com/posts/do-not-issue-pull-requests-from-your-master-branch/)
+  to make changes on a
+  [branch](https://help.github.com/articles/creating-and-deleting-branches-within-your-repository/)
+  with the branch name relating to the feature you are going to add.
+* When you are ready for others to examine and comment on your new feature,
+  navigate to your fork of selector on GitHub and open a
+* [pull request](https://help.github.com/articles/using-pull-requests/) (PR). Note that
+  after you launch a PR from one of your fork's branches, all
+  subsequent commits to that branch will be added to the open pull request
+  automatically.  Each commit added to the PR will be validated for
+  mergability, compilation and test suite compliance; the results of these tests
+  will be visible on the PR page.
+* If you're providing a new feature, you must add test cases and documentation.
+* When the code is ready to go, make sure you run the test suite using pytest.
+* When you're ready to be considered for merging, check the "Ready to go"
+  box on the PR page to let the selector devs know that the changes are complete.
+  The code will not be merged until this box is checked, the continuous
+  integration returns checkmarks,
+  and multiple core developers give "Approved" reviews.
+
+# Python Virtual Environment for Package Development
+
+Here is a list of version information for different packages that we used for
+[selector](https://github.com/theochem/selector),
+
+```bash
+python==3.7.11
+rdkit==2020.09.1.0
+numpy==1.21.2
+scipy==1.7.3
+pytest==6.2.5
+pytest-cov==3.0.0
+tox==3.24.5
+flake8==4.0.1
+pylint==2.12.2
+codecov=2.1.12
+# more to be added
+```
+
+`Conda`, [`venv`](https://docs.python.org/3/library/venv.html#module-venv) and
+[`virtualenv`](https://virtualenv.pypa.io/en/latest/) are your good friends and anyone of them
+is very helpful. I prefer `Miniconda` on my local machine.
+
+# Additional Resources
+
+* [General GitHub documentation](https://help.github.com/)
+* [PR best practices](http://codeinthehole.com/writing/pull-requests-and-other-good-practices-for-teams-using-github/)
+* [A guide to contributing to software packages](http://www.contribution-guide.org)
+* [Thinkful PR example](http://www.thinkful.com/learn/github-pull-request-tutorial/#Time-to-Submit-Your-First-PR)

.github/workflows/interface_auto.yml

@@ -0,0 +1,63 @@
+name: Deployment on DockerHub and Hugging Face
+
+# Trigger the workflow on push and pull request events to the 'main' or 'master' branch
+on:
+  push:
+    branches:
+      - "master"
+      - "main"
+      - "webapp"
+      - "jun-intro-maxmin"
+  pull_request:
+    branches:
+      - "master"
+      - "main"
+      - "webapp"
+
+jobs:
+  build-and-deploy:
+    # Run the job on the latest Ubuntu runner
+    runs-on: ubuntu-latest
+
+    steps:
+    # Step 1: Check out the latest code from the repository
+    - name: Checkout code
+      uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
+        lfs: true
+
+    # Step 2: Set up Python environment with version 3.11
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: 3.11
+
+    # Step 3: Build the Docker image to Docker Hub
+    - name: Build Docker image
+      run: |
+        docker build . -t qcdevs/selector:latest
+
+    # Step 4: Push the Docker image to Docker Hub
+    - name: Push Docker image
+      env:
+        DOCKERHUB_TOKEN: ${{ secrets.DOCKERHUB_TOKEN }}
+      run: |
+        docker login -u qcdevs -p $DOCKERHUB_TOKEN
+        docker push qcdevs/selector:latest
+
+    # Step 5: Replace the README.md file for Hugging Face
+    - name: Replace README for Hugging Face
+      run: |
+        mv README_hf.md README.md
+        git config --global user.name "github-actions[bot]"
+        git config --global user.email "qcdevs@gmail.com"
+        git add README.md
+        git commit -m "Replace README.md with README_hf.md for Hugging Face"
+
+    # Step 6: Push the app to Hugging Face
+    - name: Push to Hugging Face
+      env:
+        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+      run: |
+        git push https://QCDevs:$HF_TOKEN@huggingface.co/spaces/QCDevs/selector HEAD:main --force

.gitignore

@@ -0,0 +1,161 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+doc/_build/
+doc/html/
+doc/latex/
+doc/man/
+doc/xml/
+doc/source
+doc/modules
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# Editor junk
+tags
+[._]*.s[a-v][a-z]
+[._]*.sw[a-p]
+[._]s[a-v][a-z]
+[._]sw[a-p]
+*~
+\#*\#
+.\#*
+.ropeproject
+.idea/
+.spyderproject
+.spyproject
+.vscode/
+# Mac .DS_Store
+.DS_Store
+
+# jupyter notebook checkpoints
+.ipynb_checkpoints
+
+# codecov files
+*.gcno
+*.gcda
+*.gcov
+test_calculator

Dockerfile

@@ -0,0 +1,29 @@
+# Use the official image as a parent image
+FROM python:3.11-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Install system dependencies required for building packages
+RUN apt-get update && \
+    apt-get install -y build-essential git && \
+    apt-get install -y libxrender1 libxext6 && \
+    apt-get clean
+
+# Upgrade pip
+RUN pip install --upgrade pip
+
+# Install dependencies
+RUN pip install streamlit rdkit
+
+# Install the Selector package from the source code
+RUN pip install git+https://github.com/theochem/Selector.git
+
+# Copy the rest of the application code
+COPY . .
+
+# Expose the port the app runs on
+EXPOSE 8501
+
+# Command to run the app
+CMD ["streamlit", "run", "streamlit_app/app.py", "--server.enableXsrfProtection=false"]

LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
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+software and other kinds of works.
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README.md

@@ -1,3 +1,53 @@
-# The `selector` web app
+# The `selector` Webapp
 
+[![This project supports Python 3.7+](https://img.shields.io/badge/Python-3.7+-blue.svg)](https://python.org/downloads)
+[![GPLv3 License](https://img.shields.io/badge/License-GPL%20v3-yellow.svg)](https://opensource.org/licenses/)
+[![GitHub Actions CI Tox Status](https://github.com/theochem/Selector/actions/workflows/ci_tox.yml/badge.svg?branch=main)](https://github.com/theochem/Selector/actions/workflows/ci_tox.yml)
+[![codecov](https://codecov.io/gh/theochem/Selector/graph/badge.svg?token=0UJixrJfNJ)](https://codecov.io/gh/theochem/Selector)
 
+The `selector` library provides methods for selecting a diverse subset of a (molecular) dataset.
+
+🤗 [Selector on HuggingFace](https://huggingface.co/spaces/QCDevs/selector)
+
+Citation
+--------
+
+Please use the following citation in any publication using the `selector` library:
+
+```md
+@article{
+}
+```
+
+Running Web Interface Locally Using Docker
+------------
+
+### Install Docker
+  - Visit this [site](https://docs.docker.com/engine/install/) to install Docker Engine
+  - Open the installed Docker App once to complete the setup
+
+### Build the Docker Image
+- Clone the repository
+```bash
+git clone https://github.com/theochem/Selector.git
+```
+
+- Change to the Selector directory
+```bash
+cd Selector
+```
+
+- Change to the webapp branch
+```bash
+git checkout webapp
+```
+
+- Build the Docker image
+```bash
+docker build -t selector-webapp .
+```
+
+- Run the Webapp inside Docker Container
+```bash
+docker run -p 8501:8501 selector-webapp
+```

README_hf.md

@@ -0,0 +1,8 @@
+---
+title: QC-Selector
+emoji: 🐳
+colorFrom: purple
+colorTo: gray
+sdk: docker
+app_port: 8501
+---

streamlit_app/app.py

@@ -0,0 +1,99 @@
+# The Selector library provides a set of tools for selecting a
+# subset of the dataset and computing diversity.
+#
+# Copyright (C) 2023 The QC-Devs Community
+#
+# This file is part of Selector.
+#
+# Selector is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License
+# as published by the Free Software Foundation; either version 3
+# of the License, or (at your option) any later version.
+#
+# Selector is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, see <http://www.gnu.org/licenses/>
+#
+# --
+
+import streamlit as st
+import os
+
+
+# Get the current directory path
+current_dir = os.path.dirname(os.path.abspath(__file__))
+
+# Construct the path to the assets directory
+assets_dir = os.path.join(current_dir, "assets")
+
+# Set page configuration
+st.set_page_config(
+    page_title = "Selector",
+    page_icon = os.path.join(assets_dir, "QC-Devs.png"),
+)
+
+st.image(os.path.join(assets_dir, "selector_logo.png"))
+
+st.write("# Welcome to Selector! 👋")
+
+st.sidebar.success("Select an algorithm to get started.")
+
+st.info("👈 Select an algorithm from the sidebar to see some examples of what Selector can do!")
+
+st.markdown(
+    """
+    [selector](https://github.com/theochem/Selector) is a free, open-source, and cross-platform
+    Python library designed to help you effortlessly identify the most diverse subset of molecules
+    from your dataset. 
+    Please use the following citation in any publication using selector library:
+
+    **“Selector: A Generic Python Package for Subset Selection”**, Fanwang Meng, Alireza Tehrani, 
+    Valerii Chuiko, Abigail Broscius, Abdul, Hassan, Maximilian van Zyl, Marco Martínez González, 
+    Yang, Ramón Alain Miranda-Quintana, Paul W. Ayers, and Farnaz Heidar-Zadeh”
+
+    The selector source code is hosted on [GitHub](https://github.com/theochem/Selector) 
+    and is released under the [GNU General Public License v3.0](https://github.com/theochem/Selector/blob/main/LICENSE). 
+    We welcome any contributions to the selector library in accordance with our Code of Conduct; 
+    please see our [Contributing Guidelines](https://qcdevs.org/guidelines/qcdevs_code_of_conduct/).
+    Please report any issues you encounter while using 
+    selector library on [GitHub Issues](https://github.com/theochem/Selector/issues). 
+    For further information and inquiries please contact us at qcdevs@gmail.com.
+
+    ### Why QC-Selector?
+    In the world of chemistry, selecting the right subset of molecules is critical for a wide
+    range of applications, including drug discovery, materials science, and molecular optimization. 
+    QC-Selector offers a cutting-edge solution to streamline this process, empowering researchers, 
+    scientists, and developers to make smarter decisions faster.
+
+    ### Key Features
+    1. Import Your Dataset: Simply import your molecule dataset in various file formats, including SDF, SMILES, and InChi, to get started.
+
+    2. Define Selection Criteria: Specify the desired level of diversity and other relevant parameters to tailor the subset selection to your unique requirements.
+
+    3. Run the Analysis: Let QC-Selector’s powerful algorithms process your dataset and efficiently select the most diverse molecules.
+
+    4. Export: Explore the diverse subset and export the results for further analysis and integration into your projects.
+"""
+)
+
+st.sidebar.title("About QC-Devs")
+
+st.sidebar.info("QC-Devs develops various free, open-source, and cross-platform libraries for scientific computing, especially theoretical and computational chemistry. Our goal is to make programming accessible to chemists and promote precepts of sustainable software development. For further information and inquiries please contact us at qcdevs@gmail.com.")
+
+# Add icons to the sidebar
+st.sidebar.markdown(
+    """
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
+    <div style="text-align: center;">
+        <a href="https://qcdevs.org/" target="_blank"><i class="fa fa-home" style="font-size:24px"></i> WEBSITE</a><br>
+        <a href="mailto:qcdevs@gmail.com"><i class="fa fa-envelope" style="font-size:24px"></i> EMAIL</a><br>
+        <a href="https://github.com/theochem" target="_blank"><i class="fa fa-github" style="font-size:24px"></i> GITHUB</a><br>
+        © 2024 QC-Devs. All rights reserved.
+    </div>
+    """,
+    unsafe_allow_html=True
+)

streamlit_app/features.py

@@ -0,0 +1,528 @@
+# -*- coding: utf-8 -*-
+# The DiverseSelector library provides a set of tools to select molecule
+# subset with maximum molecular diversity.
+#
+# Copyright (C) 2022 The QC-Devs Community
+#
+# This file is part of DiverseSelector.
+#
+# DiverseSelector is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License
+# as published by the Free Software Foundation; either version 3
+# of the License, or (at your option) any later version.
+#
+# DiverseSelector is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, see <http://www.gnu.org/licenses/>
+#
+# --
+
+"""Feature generation module."""
+
+import os
+from pathlib import PurePath
+import sys
+from typing import Union
+
+import numpy as np
+import pandas as pd
+from rdkit import Chem
+from rdkit.Chem import AllChem, Descriptors, MACCSkeys, rdMHFPFingerprint, Draw
+
+__all__ = [
+    "DescriptorGenerator",
+    "FingerprintGenerator",
+    "feature_reader",
+    "aug_features",
+]
+
+
+class DescriptorGenerator:
+    """Compute molecular features."""
+
+    def __init__(self,
+                 mols: list = None):
+        """Coinstructor of DescriptorGenerator."""
+        self.mols = mols
+
+    def mordred_desc(self, ignore_3D: bool = False):  # noqa: N803
+        """Mordred molecular descriptor generation.
+
+        Parameters
+        ----------
+        ignore_3D : bool, optional
+            Ignore 3D coordinates. The default=False.
+
+        Returns
+        -------
+        df_features: PandasDataFrame
+            A `pandas.DataFrame` object with compute Mordred descriptors.
+
+        """
+        from mordred import Calculator, descriptors  # pylint: disable=C0415
+        # if only compute 2D descriptors, set ignore_3D=True
+        calc = Calculator(descs=descriptors, ignore_3D=ignore_3D)
+        df_features = pd.DataFrame(calc.pandas(self.mols))
+
+        return df_features
+
+    @staticmethod
+    def padelpy_desc(mol_file: Union[str, PurePath],
+                     keep_csv: bool = False,
+                     maxruntime: int = -1,
+                     waitingjobs: int = -1,
+                     threads: int = -1,
+                     d_2d: bool = True,
+                     d_3d: bool = True,
+                     config: str = None,
+                     convert3d: bool = False,
+                     descriptortypes: str = None,
+                     detectaromaticity: bool = False,
+                     fingerprints: bool = False,
+                     log: bool = False,
+                     maxcpdperfile: int = 0,
+                     removesalt: bool = False,
+                     retain3d: bool = False,
+                     standardizenitro: bool = False,
+                     standardizetautomers: bool = False,
+                     tautomerlist: str = None,
+                     usefilenameasmolname: bool = False,
+                     sp_timeout: int = None,
+                     headless: bool = True):  # pylint: disable=R0201
+        """PADEL molecular descriptor generation.
+
+        Parameters
+        ----------
+        mol_file : str
+            Molecule file name.
+        keep_csv : bool, optional
+            If True, the csv file is kept. Default=False.
+        maxruntime : int, optional
+            Additional keyword arguments.
+            See https://github.com/ecrl/padelpy/blob/master/padelpy/wrapper.py.
+
+        Returns
+        -------
+        df_features: PandasDataFrame
+            A `pandas.DataFrame` object with compute Mordred descriptors.
+
+        """
+        cwd = os.path.dirname(os.path.abspath(__file__))
+        sys.path.append(os.path.join(cwd, "padelpy"))
+
+        from padelpy import padeldescriptor  # pylint: disable=C0415
+        # if only compute 2D descriptors,
+        # ignore_3D=True
+
+        csv_fname = (
+                str(os.path.basename(mol_file)).split(".", maxsplit=1)[0]
+                + "_padel_descriptors.csv"
+        )
+
+        padeldescriptor(
+            maxruntime=maxruntime,
+            waitingjobs=waitingjobs,
+            threads=threads,
+            d_2d=d_2d,
+            d_3d=d_3d,
+            config=config,
+            convert3d=convert3d,
+            descriptortypes=descriptortypes,
+            detectaromaticity=detectaromaticity,
+            mol_dir=mol_file,
+            d_file=csv_fname,
+            fingerprints=fingerprints,
+            log=log,
+            maxcpdperfile=maxcpdperfile,
+            removesalt=removesalt,
+            retain3d=retain3d,
+            retainorder=True,
+            standardizenitro=standardizenitro,
+            standardizetautomers=standardizetautomers,
+            tautomerlist=tautomerlist,
+            usefilenameasmolname=usefilenameasmolname,
+            sp_timeout=sp_timeout,
+            headless=headless,
+        )
+
+        df_features = pd.read_csv(csv_fname, sep=",", index_col="Name")
+
+        if not keep_csv:
+            os.remove(csv_fname)
+
+        return df_features
+
+    def rdkit_desc(self,
+                   use_fragment: bool = True,
+                   ipc_avg: bool = True):
+        """Generation RDKit molecular descriptors.
+
+        Parameters
+        ----------
+        use_fragment : bool, optional
+            If True, the return value includes the fragment binary descriptors like "fr_XXX".
+        ipc_avg : bool, optional
+            If True, the IPC descriptor calculates with avg=True option.
+
+        Returns
+        -------
+        df_features: PandasDataFrame
+            A `pandas.DataFrame` object with compute Mordred descriptors.
+
+        """
+        # parsing descriptor information
+        # parsing descriptor information
+        desc_list = []
+        descriptor_types = []
+        for descriptor, function in Descriptors.descList:
+            if use_fragment is False and descriptor.startswith("fr_"):
+                continue
+            descriptor_types.append(descriptor)
+            desc_list.append((descriptor, function))
+
+        # check initialization
+        assert len(descriptor_types) == len(desc_list)
+
+        arr_features = np.full(shape=(len(self.mols), len(desc_list)),
+                               fill_value=np.nan)
+
+        for idx_row, mol in enumerate(self.mols):
+            # this part is modified from
+            # https://github.com/deepchem/deepchem/blob/master/deepchem/feat/molecule_featurizers/
+            # rdkit_descriptors.py#L11-L98
+            for idx_col, (desc_name, function) in enumerate(desc_list):
+                if desc_name == "Ipc" and ipc_avg:
+                    feature = function(mol, avg=True)
+                else:
+                    feature = function(mol)
+                arr_features[idx_row, idx_col] = feature
+
+        df_features = pd.DataFrame(arr_features, columns=descriptor_types)
+
+        return df_features
+
+    def rdkit_frag_desc(self):
+        """Generation of the RDKit fragment features.
+
+        Returns
+        -------
+        df_features: PandasDataFrame
+            A `pandas.DataFrame` object with compute Mordred descriptors.
+
+        """
+        # http://rdkit.org/docs/source/rdkit.Chem.Fragments.html
+        # this implementation is taken from https://github.com/Ryan-Rhys/FlowMO/blob/
+        # e221d989914f906501e1ad19cd3629d88eac1785/property_prediction/data_utils.py#L111
+        fragments = {d[0]: d[1] for d in Descriptors.descList[115:]}
+        frag_features = np.zeros((len(self.mols), len(fragments)))
+        for idx, mol in enumerate(self.mols):
+            features = [fragments[d](mol) for d in fragments]
+            frag_features[idx, :] = features
+
+        feature_names = [desc[0] for desc in Descriptors.descList[115:]]
+        df_features = pd.DataFrame(data=frag_features, columns=feature_names)
+
+        return df_features
+
+
+class FingerprintGenerator:
+    """Fingerprint generator."""
+
+    def __init__(self, mols: list) -> None:
+        """Fingerprint generator.
+
+        Parameters
+        ----------
+        mols : RDKitMol
+            Molecule object.
+        """
+        self.mols = mols
+
+        # molecule names
+        mol_names = [
+            Chem.MolToSmiles(mol)
+            if mol.GetPropsAsDict().get("_Name") is None
+            else mol.GetProp("_Name")
+            for mol in mols
+        ]
+        self.mol_names = mol_names
+
+    def compute_fingerprint(
+            self,
+            fp_type: str = "SECFP",
+            n_bits: int = 2048,
+            radius: int = 3,
+            min_radius: int = 1,
+            random_seed: int = 12345,
+            rings: bool = True,
+            isomeric: bool = True,
+            kekulize: bool = False,
+    ):
+        """Compute fingerprints.
+
+        Parameters
+        ----------
+        fp_type : str, optional
+            Supported fingerprints: SECFP, ECFP, Morgan, RDKitFingerprint and MACCSkeys.
+            Default="SECFP".
+        n_bits : int, optional
+            Number of bits of fingerprint. Default=2048.
+        radius : int, optional
+            The maximum radius of the substructure that is generated at each atom. Default=3.
+        min_radius : int, optional
+            The minimum radius that is used to extract n-grams.
+        random_seed : int, optional
+            The random seed number. Default=12345.
+        rings : bool, optional
+            Whether the rings (SSSR) are extracted from the molecule and added to the shingling.
+            Default=True.
+        isomeric : bool, optional
+            Whether the SMILES added to the shingling are isomeric. Default=False.
+        kekulize : bool, optional
+            Whether the SMILES added to the shingling are kekulized. Default=True.
+        """
+        if fp_type.upper() in [
+            "SECFP",
+            "ECFP",
+            "MORGAN",
+            "RDKFINGERPRINT",
+            "MACCSKEYS",
+        ]:
+            fps = [
+                self.rdkit_fingerprint_low(
+                    mol,
+                    fp_type=fp_type,
+                    n_bits=n_bits,
+                    radius=radius,
+                    min_radius=min_radius,
+                    random_seed=random_seed,
+                    rings=rings,
+                    isomeric=isomeric,
+                    kekulize=kekulize,
+                )
+                for mol in self.mols
+            ]
+        # todo: add support of e3fp
+
+        # other cases
+        else:
+            raise ValueError(f"{fp_type} is not an supported fingerprint type.")
+
+        df_fps = pd.DataFrame(np.array(fps), index=self.mol_names)
+
+        return df_fps
+
+    @staticmethod
+    def rdkit_fingerprint_low(
+            mol,
+            fp_type: str = "SECFP",
+            n_bits: int = 2048,
+            radius: int = 3,
+            min_radius: int = 1,
+            random_seed: int = 12345,
+            rings: bool = True,
+            isomeric: bool = False,
+            kekulize: bool = False,
+    ):
+        """
+        Generate required molecular fingerprints.
+
+        Parameters
+        ----------
+        mols : RDKitMol
+            Molecule object.
+        fp_type : str, optional
+            Supported fingerprints: SECFP, ECFP, Morgan, RDKitFingerprint and MACCSkeys.
+            Default="SECFP".
+        n_bits : int, optional
+            Number of bits of fingerprint. Default=2048.
+        radius : int, optional
+            The maximum radius of the substructure that is generated at each atom. Default=3.
+        min_radius : int, optional
+            The minimum radius that is used to extract n-grams.
+        random_seed : int, optional
+            The random seed number. Default=12345.
+        rings : bool, optional
+            Whether the rings (SSSR) are extracted from the molecule and added to the shingling.
+            Default=True.
+        isomeric : bool, optional
+            Whether the SMILES added to the shingling are isomeric. Default=False.
+        kekulize : bool, optional
+            Whether the SMILES added to the shingling are kekulized. Default=True.
+
+        Returns
+        -------
+        fp : ExplicitBitVector
+            The computed molecular fingerprint.
+
+        Notes
+        -----
+        fingerprint types:
+        1. topological fingerprints: RDKFingerprint, Tanimoto, Dice, Cosine, Sokal, Russel,
+        Kulczynski, McConnaughey, and Tversky
+        2. MACCS keys:
+        3. Atom pairs and topological torsions
+        4. Morgan fingerprints (circular fingerprints): Morgan, ECFP, FCFP
+
+        """
+        # SECFP: SMILES extended connectivity fingerprint
+        # https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0321-8
+        if fp_type.upper() == "SECFP":
+            secfp_encoder = rdMHFPFingerprint.MHFPEncoder(random_seed)
+            fp = secfp_encoder.EncodeSECFPMol(
+                mol,
+                radius=radius,
+                rings=rings,
+                isomeric=isomeric,
+                kekulize=kekulize,
+                min_radius=min_radius,
+                length=n_bits,
+            )
+        # ECFP
+        # https://github.com/deepchem/deepchem/blob/1a2d2e9ff097fdbf58894d1f91359fe466c65810/deepchem/utils/rdkit_utils.py#L414
+        # https://www.rdkit.org/docs/source/rdkit.Chem.rdMolDescriptors.html
+        elif fp_type.upper() == "ECFP":
+            # radius=3 --> ECFP6
+            fp = AllChem.GetMorganFingerprintAsBitVect(
+                mol=mol,
+                radius=radius,
+                nBits=n_bits,
+                useChirality=isomeric,
+                useFeatures=False,
+            )
+        elif fp_type.upper() == "MORGAN":
+            fp = AllChem.GetMorganFingerprintAsBitVect(
+                mol=mol,
+                radius=radius,
+                nBits=n_bits,
+                useChirality=isomeric,
+                useFeatures=True,
+            )
+        # https://www.rdkit.org/docs/source/rdkit.Chem.rdmolops.html#rdkit.Chem.rdmolops.RDKFingerprint
+        elif fp_type.upper() == "RDKFINGERPRINT":
+            fp = Chem.rdmolops.RDKFingerprint(
+                mol=mol,
+                minPath=1,
+                # maxPath=mol.GetNumBonds(),
+                maxPath=10,
+                fpSize=n_bits,
+                nBitsPerHash=2,
+                useHs=True,
+                tgtDensity=0,
+                minSize=128,
+                branchedPaths=True,
+                useBondOrder=True,
+            )
+        # SMARTS-based implementation of the 166 public MACCS keys
+        # https://www.rdkit.org/docs/GettingStartedInPython.html#fingerprinting-and-molecular-similarity
+        elif fp_type == "MaCCSKeys":
+            fp = MACCSkeys.GenMACCSKeys(mol)
+        else:
+            # todo: add more
+            # https://github.com/keiserlab/e3fp
+            # https://chemfp.readthedocs.io/en/latest/fp_types.html
+            # https://xenonpy.readthedocs.io/en/stable/_modules/xenonpy/descriptor/fingerprint.html
+            raise NotImplementedError(f"{fp_type} is not implemented yet.")
+
+        return fp
+
+
+def feature_reader(file_name: str,
+                   sep: str = ",",
+                   engine: str = "python",
+                   **kwargs):
+    """Load molecule features/descriptors.
+
+    Parameters
+    ----------
+    file_name : str
+        File name that provides molecular features.
+    sep : str, optional
+        Separator use for CSV like files. Default=",".
+    engine : str, optional
+        Engine name used for reading files, where "python" supports regular expression for CSV
+        formats, “xlrd” supports old-style Excel files (.xls), “openpyxl” supports newer Excel file
+        formats, “odf” supports OpenDocument file formats (.odf, .ods, .odt), “pyxlsb” supports
+        binary Excel files. One should note that the dependency should be installed properly to
+        make it work. Default="python".
+    **kwargs
+        Additional keyword arguments passed to
+        `pd.read_csv() <https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html>`_
+        or `pd.read_excel() <https://pandas.pydata.org/docs/reference/api/pandas.read_excel.html>`_.
+
+    Returns
+    -------
+    df : PandasDataFrame
+        A `pandas.DataFrame` object with molecular features.
+    """
+
+    # use `str` function to support PosixPath
+    if str(file_name).lower().endswith((".csv", ".txt")):
+        df = pd.read_csv(file_name, sep=sep, engine=engine, *kwargs)
+    elif (
+        str(file_name)
+        .lower()
+        .endswith((".xlsx", ".xls", "xlsb", ".odf", ".ods", ".odt"))
+    ):
+        df = pd.read_excel(file_name, engine=engine, *kwargs)
+
+    return df
+
+
+def aug_features(features,
+                 target_prop,
+                 weight: Union[np.ndarray, float, int] = None) -> np.ndarray:
+    r"""Augmented features.
+
+    Parameters
+    ----------
+    features : np.ndarray or PandasDataFrame
+        Molecular features.
+    target_prop : np.ndarray or PandasDataFrame
+        Target properties.
+    weight : np.ndarray, optional
+        Weight for each molecule. When weight is not provided (None), the property will be
+        directly augmented to the features without repeats. Default=None.
+
+    Returns
+    -------
+    aug_features : np.ndarray
+        Augmented features matrix.
+
+    Notes
+    -----
+    When the property becomes the focus instead of structural diversity, we can just use the
+    property matrix as the feature matrix without using fingerprints or molecular descriptors.
+
+    The augmented feature matrix is a concatenation of the original features matrix and the
+    property matrix. When a weight matrix is provided, then the repeat-value for each property to
+    be :math:`repeats = \left \lceil weight \times n_{features} \right \rceil`
+    where :math:`n_{features}` denotes the number of features (length of the
+    fingerprint/descriptors), and the final augmented features matrix is of feature matrix
+    augmented by the property matrix for :math:`repeat` times.
+
+    To achieve sampling with respect to target properties and diversity with respect to a
+    fingerprint/feature-vector, you need to append the target properties to the feature vector.
+    For each property, specify the :math:`repeats`: the more times you repeat the property
+    value the higher its weight.
+
+    """
+    if isinstance(features, pd.DataFrame):
+        features = features.to_numpy()
+    if isinstance(target_prop, pd.DataFrame):
+        target_prop = target_prop.to_numpy()
+
+    # define the repeat-value for each property
+    if weight is not None:
+        repeats = np.ceil(np.multiply(weight, features.shape[1]))
+    else:
+        repeats = 1
+
+    features_new = np.hstack((features,
+                              np.repeat(target_prop, repeats=repeats, axis=1)))
+
+    return features_new

streamlit_app/pages/Converter.py

@@ -0,0 +1,112 @@
+import streamlit as st
+from rdkit import Chem
+from rdkit.Chem import Draw
+import tempfile
+import os
+import sys
+
+
+# Add the streamlit_app directory to the Python path
+current_dir = os.path.dirname(os.path.abspath(__file__))
+module_path = os.path.abspath(os.path.join(current_dir, '..', '..'))
+sys.path.append(module_path)
+parent_dir = os.path.join(current_dir, "..")
+sys.path.append(parent_dir)
+
+from utils import *
+from streamlit_app.features import DescriptorGenerator, FingerprintGenerator
+
+
+# Set up the page configuration
+set_page_config(
+    page_title = "Chem Converter",
+    page_icon = os.path.join(parent_dir, "assets", "QC-Devs.png")
+)
+
+st.title("Chemical File Converter")
+
+# Description of the page
+st.markdown("""
+This page allows you to upload raw chemical file formats such as SMILES or SDF,
+and convert them into chemical matrices that can be used as input for selector's various algorithms.
+""")
+
+# File uploader for chemical file
+chemical_file = st.file_uploader("Upload a chemical file (e.g., SMILES, SDF, or TXT)",
+                                 type = ["txt", "smi", "sdf"])
+
+if chemical_file:
+    # User selects the file format
+    file_format = st.selectbox(
+        "Select the format of the provided file",
+        options = ["", "SMILES", "SDF"]
+    )
+
+    if file_format:
+        molecules = []
+        temp_sdf_path = None
+
+        # Process the chemical file based on user selection
+        if file_format == "SMILES":
+            smiles_list = chemical_file.read().decode("utf-8").splitlines()
+            molecules = [Chem.MolFromSmiles(smiles) for smiles in smiles_list]
+        elif file_format == "SDF":
+            # Create a temporary file to save the uploaded SDF content
+            with tempfile.NamedTemporaryFile(delete = False, suffix = ".sdf") as temp_sdf:
+                temp_sdf.write(chemical_file.read())
+                temp_sdf_path = temp_sdf.name
+
+            # Use RDKit's SDMolSupplier to read molecules from the SDF file
+            supplier = Chem.SDMolSupplier(temp_sdf_path)
+            molecules = [mol for mol in supplier if mol is not None]
+
+            # Explicitly close the supplier to release the file
+            del supplier
+
+        # Check for valid molecules
+        valid_molecules = [mol for mol in molecules if mol is not None]
+
+        if not valid_molecules:
+            st.error("No valid molecules found in the uploaded file.")
+        else:
+            st.success(f"Successfully loaded {len(valid_molecules)} valid molecules.")
+
+            # Display the molecules
+            img = Draw.MolsToImage(valid_molecules)
+            st.image(img, caption = "Molecules in the file")
+
+            # Choose the type of matrix to generate
+            matrix_type = st.selectbox("Choose matrix type", ["Descriptors", "Fingerprints"])
+
+            if matrix_type == "Descriptors":
+                # Allow the user to choose the type of descriptors to generate
+                use_fragment = st.checkbox("Whether return value includes the fragment binary descriptors", value = True)
+                ipc_avg = st.checkbox("Whether IPC descriptor calculates with avg", value = True)
+
+                descriptor_generator = DescriptorGenerator(valid_molecules)
+                matrix = descriptor_generator.rdkit_desc(use_fragment, ipc_avg)
+            elif matrix_type == "Fingerprints":
+                # Allow user to choose the type of fingerprint to generate
+                fp_type = st.selectbox("Select Fingerprint Type", options=["SECFP", "ECFP", "Morgan"])
+                n_bits = st.number_input("Number of bits for the fingerprint", min_value = 1, value = 2048)
+                radius = st.number_input("The maximum radius of the substructure that is generated at each atom", min_value = 1, value = 3)
+                min_radius = st.number_input("The minimum radius that is used to extract n-grams", min_value = 1, value = 3)
+                random_seed = st.number_input("Random seed for fingerprint generation", min_value = 0, value = 12345)
+                rings = st.checkbox("Whether the rings (SSSR) are extracted from the molecule and added to the shingling", value = True)
+                isomeric = st.checkbox("Whether the SMILES added to the shingling are isomeric", value = True)
+                kekulize = st.checkbox("Whether the SMILES added to the shingling are kekulized", value = False)
+
+                fp_generator = FingerprintGenerator(valid_molecules)
+                matrix = fp_generator.compute_fingerprint(fp_type = fp_type)
+
+            st.write("Generated Chemical Matrix:")
+            st.dataframe(matrix)
+
+            # Option to download the matrix as CSV
+            csv_data = matrix.to_csv().encode('utf-8')
+            st.download_button("Download Chemical Matrix as CSV", data = csv_data,
+                               file_name = "chemical_matrix.csv", mime = "text/csv")
+
+        # Clean up the temporary file after RDKit is done with it
+        if temp_sdf_path and os.path.exists(temp_sdf_path):
+            os.remove(temp_sdf_path)

streamlit_app/pages/DISE.py

@@ -0,0 +1,112 @@
+# The Selector library provides a set of tools for selecting a
+# subset of the dataset and computing diversity.
+#
+# Copyright (C) 2023 The QC-Devs Community
+#
+# This file is part of Selector.
+#
+# Selector is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License
+# as published by the Free Software Foundation; either version 3
+# of the License, or (at your option) any later version.
+#
+# Selector is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, see <http://www.gnu.org/licenses/>
+#
+# --
+
+import streamlit as st
+import sys
+import os
+
+from selector.methods.distance import DISE
+
+# Add the streamlit_app directory to the Python path
+current_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.join(current_dir, "..")
+sys.path.append(parent_dir)
+
+from utils import *
+
+# Set page configuration
+st.set_page_config(
+    page_title = "DISE",
+    page_icon = os.path.join(parent_dir, "assets" , "QC-Devs.png"),
+)
+
+st.title("Directed Sphere Exclusion (DISE)")
+
+
+description = """
+    In a nutshell, this algorithm iteratively excludes any sample within a given radius from
+    any already selected sample. The radius of the exclusion sphere is an adjustable parameter.
+    Compared to Sphere Exclusion algorithm, the Directed Sphere Exclusion algorithm achieves a
+    more evenly distributed subset selection by abandoning the random selection approach and
+    instead imposing a directed selection.
+
+    Reference sample is chosen based on the `ref_index`, which is excluded from the selected
+    subset. All samples are sorted (ascending order) based on their Minkowski p-norm distance
+    from the reference sample. Looping through sorted samples, the sample is selected if it is
+    not already excluded. If selected, all its neighboring samples within a sphere of radius r
+    (i.e., exclusion sphere) are excluded from being selected. When the selected number of points
+    is greater than specified subset `size`, the selection process terminates. The `r0` is used
+    as the initial radius of exclusion sphere, however, it is optimized to select the desired
+    number of samples.
+    """
+
+references = "Gobbi, A., and Lee, M.-L. (2002). DISE: directed sphere exclusion."\
+             "Journal of Chemical Information and Computer Sciences,"\
+             "43(1), 317–323. https://doi.org/10.1021/ci025554v"
+
+display_sidebar_info("Directed Sphere Exclusion (DISE)", description, references)
+
+# File uploader for feature matrix or distance matrix (required)
+matrix_file = st.file_uploader("Upload a feature matrix or distance matrix (required)",
+                               type=["csv", "xlsx", "npz", "npy"], key="matrix_file", on_change=clear_results)
+
+# Clear selected indices if a new matrix file is uploaded
+if matrix_file is None:
+    clear_results()
+# Load data from matrix file
+else:
+    matrix = load_matrix(matrix_file)
+    num_points = st.number_input("Number of points to select", min_value = 1, step = 1,
+                                 key = "num_points", on_change=clear_results)
+    label_file = st.file_uploader("Upload a cluster label list (optional)", type = ["csv", "xlsx"],
+                                  key = "label_file", on_change=clear_results)
+    labels = load_labels(label_file) if label_file else None
+
+    # Parameters for Directed Sphere Exclusion
+    st.info("The parameters below are optional. If not specified, default values will be used.")
+
+    r0 = st.number_input("Initial guess for radius of exclusion sphere (r0)", value=None, step=0.1,
+                         on_change=clear_results)
+    ref_index = st.number_input("Reference index (ref_index)", value=0, step=1, on_change=clear_results)
+    tol = st.number_input("Percentage tolerance of sample size error (tol)", value=0.05, step=0.05,
+                          on_change=clear_results)
+    n_iter = st.number_input("Number of iterations for optimizing the radius of exclusion sphere (n_iter)",
+                             value=10, step=10, on_change=clear_results)
+    p = st.number_input("Minkowski p-norm distance (p)", value=2.0, step=1.0, on_change=clear_results)
+    eps = st.number_input("Approximate nearest neighbor search parameter (eps)", value=0.0, step=0.1,
+                          on_change=clear_results)
+
+    if st.button("Run DISE Algorithm"):
+        selector = DISE(r0=r0, ref_index=ref_index, tol=tol, n_iter=n_iter, p=p, eps=eps)
+        selected_ids = run_algorithm(selector, matrix, num_points, labels)
+        st.session_state['selector'] = selector
+        st.session_state['selected_ids'] = selected_ids
+
+# Check if the selected indices are stored in the session state
+if 'selected_ids' in st.session_state and matrix_file is not None:
+    selected_ids = st.session_state['selected_ids']
+    st.write("Selected indices:", selected_ids)
+
+    if 'selector' in st.session_state:
+        st.write("Radius of the exclusion sphere:", st.session_state['selector'].r)
+
+    export_results(selected_ids)

streamlit_app/pages/GridPartition.py

@@ -0,0 +1,98 @@
+# The Selector library provides a set of tools for selecting a
+# subset of the dataset and computing diversity.
+#
+# Copyright (C) 2023 The QC-Devs Community
+#
+# This file is part of Selector.
+#
+# Selector is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License
+# as published by the Free Software Foundation; either version 3
+# of the License, or (at your option) any later version.
+#
+# Selector is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, see <http://www.gnu.org/licenses/>
+#
+# --
+
+import streamlit as st
+import sys
+import os
+
+from selector.methods.partition import GridPartition
+
+# Add the streamlit_app directory to the Python path
+current_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.join(current_dir, "..")
+sys.path.append(parent_dir)
+
+from utils import *
+
+# Set page configuration
+st.set_page_config(
+    page_title = "GridPartition",
+    page_icon = os.path.join(parent_dir, "assets" , "QC-Devs.png"),
+)
+
+st.title("Grid Partitioning Method")
+
+
+description = """
+    Given the number of bins along each axis, samples are partitioned using various methods:
+
+    1. The equisized_independent partitions the feature space into bins of equal size along each dimension.
+
+    2. The equisized_dependent partitions the space where the bins can have different length in each dimension. I.e., the `l-`th dimension bins depend on the previous dimensions. So, the order of features affects the outcome.
+
+    3. The equifrequent_independent divides the space into bins with approximately equal number of sample points in each bin.
+
+    4. The equifrequent_dependent is similar to equisized_dependent where the partition in each dimension will depend on the previous dimensions.
+    """
+
+references = "[1] Bayley, Martin J., and Peter Willett. “Binning schemes for partition-based compound selection.” Journal of Molecular Graphics and Modelling 17.1 (1999): 10-18."
+
+display_sidebar_info("Grid Partitioning Method", description, references)
+
+# File uploader for feature matrix or distance matrix (required)
+matrix_file = st.file_uploader("Upload a feature matrix or distance matrix (required)",
+                               type=["csv", "xlsx", "npz", "npy"], key="matrix_file", on_change=clear_results)
+
+# Clear selected indices if a new matrix file is uploaded
+if matrix_file is None:
+    clear_results()
+
+# Load data from matrix file
+else:
+    matrix = load_matrix(matrix_file)
+    num_points = st.number_input("Number of points to select", min_value = 1, step = 1,
+                                 key = "num_points", on_change=clear_results)
+    label_file = st.file_uploader("Upload a cluster label list (optional)", type = ["csv", "xlsx"],
+                                  key = "label_file", on_change=clear_results)
+    labels = load_labels(label_file) if label_file else None
+
+    numb_bins_axis = st.number_input("Number of bins to partition each axis into (numb_bins_axis)", value = 1, step = 1)
+
+    # Parameters for Directed Sphere Exclusion
+    st.info("The parameters below are optional. If not specified, default values will be used.")
+
+    grid_method = st.selectbox("Method used to partition the sample points into bins. (grid_method)", ["equisized_independent",
+                                "equisized_dependent", "equifrequent_independent", "equifrequent_dependent"], on_change=clear_results)
+
+    random_seed = st.number_input("Seed for random selection of sample points from each bin. (random_seed)", value=42, step=1, on_change=clear_results)
+
+    if st.button("Run GridPartition Algorithm"):
+        selector = GridPartition(numb_bins_axis, grid_method, random_seed)
+        selected_ids = run_algorithm(selector, matrix, num_points, labels)
+        st.session_state['selected_ids'] = selected_ids
+
+# Check if the selected indices are stored in the session state
+if 'selected_ids' in st.session_state and matrix_file is not None:
+    selected_ids = st.session_state['selected_ids']
+    st.write("Selected indices:", selected_ids)
+
+    export_results(selected_ids)

streamlit_app/pages/MaxMin.py

@@ -0,0 +1,110 @@
+# The Selector library provides a set of tools for selecting a
+# subset of the dataset and computing diversity.
+#
+# Copyright (C) 2023 The QC-Devs Community
+#
+# This file is part of Selector.
+#
+# Selector is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License
+# as published by the Free Software Foundation; either version 3
+# of the License, or (at your option) any later version.
+#
+# Selector is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, see <http://www.gnu.org/licenses/>
+#
+# --
+
+import streamlit as st
+import os
+import sys
+
+from sklearn.metrics import pairwise_distances
+from selector.methods.distance import MaxMin
+
+# Add the streamlit_app directory to the Python path
+current_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.join(current_dir, "..")
+sys.path.append(parent_dir)
+
+from utils import *
+
+# Set page configuration
+st.set_page_config(
+    page_title = "MaxMin",
+    page_icon = os.path.join(parent_dir, "assets" , "QC-Devs.png"),
+)
+
+st.title("Brute Strength - MaxMin")
+
+description = """
+    MaxMin is possibly the most widely used method for dissimilarity-based
+    compound selection. When presented with a dataset of samples, the
+    initial point is chosen as the dataset's medoid center. Next, the second
+    point is chosen to be that which is furthest from this initial point.
+    Subsequently, all following points are selected via the following
+    logic:
+
+    1. Find the minimum distance from every point to the already-selected ones.
+    2. Select the point which has the maximum distance among those calculated
+       in the previous step.
+
+    In the current implementation, this method requires or computes the full pairwise-distance
+    matrix, so it is not recommended for large datasets.
+    """
+
+
+references = "[1] Ashton, Mark, et al., Identification of diverse database subsets using "\
+             "property‐based and fragment‐based molecular descriptions, "\
+             "Quantitative Structure‐Activity Relationships 21.6 (2002): 598-604."
+
+display_sidebar_info("Brute Strength - MaxMin", description, references)
+
+# File uploader for feature matrix or distance matrix (required)
+matrix_file = st.file_uploader("Upload a feature matrix or distance matrix (required)",
+                               type=["csv", "xlsx", "npz", "npy"], key="matrix_file", on_change=clear_results)
+
+# Clear selected indices if a new matrix file is uploaded
+if matrix_file is None:
+    st.session_state.pop("selected_ids", None)
+# Load data from matrix file
+else:
+    matrix = load_matrix(matrix_file)
+    num_points = st.number_input("Number of points to select", min_value = 1, step = 1,
+                                 key = "num_points", on_change=clear_results)
+    label_file = st.file_uploader("Upload a cluster label list (optional)", type = ["csv", "xlsx"],
+                                  key = "label_file", on_change=clear_results)
+    labels = load_labels(label_file) if label_file else None
+
+    distance_metric = st.selectbox("Select distance metric (optional)",
+                                   [None, "euclidean", "manhattan", "cosine"],
+                                   key = "distance_metric", on_change=clear_results)
+
+    if distance_metric:
+        fun_dist = lambda x: pairwise_distances(x, metric = distance_metric)
+    else:
+        fun_dist = None
+
+    if st.button("Run MaxMin Algorithm"):
+        if fun_dist:
+            selector = MaxMin(fun_dist)
+            selected_ids = run_algorithm(selector, matrix, num_points, labels)
+        else:
+            selector = MaxMin()
+            selected_ids = run_algorithm(selector, matrix, num_points, labels)
+
+        st.session_state['selector'] = selector
+        st.session_state['selected_ids'] = selected_ids
+
+
+# Check if the selected indices are stored in the session state
+if 'selected_ids' in st.session_state and matrix_file is not None:
+    selected_ids = st.session_state['selected_ids']
+    st.write("Selected indices:", selected_ids)
+
+    export_results(selected_ids)

streamlit_app/pages/MaxSum.py

@@ -0,0 +1,109 @@
+# The Selector library provides a set of tools for selecting a
+# subset of the dataset and computing diversity.
+#
+# Copyright (C) 2023 The QC-Devs Community
+#
+# This file is part of Selector.
+#
+# Selector is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License
+# as published by the Free Software Foundation; either version 3
+# of the License, or (at your option) any later version.
+#
+# Selector is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, see <http://www.gnu.org/licenses/>
+#
+# --
+
+import streamlit as st
+import os
+import sys
+
+from sklearn.metrics import pairwise_distances
+
+from selector.methods.distance import MaxSum
+
+# Add the streamlit_app directory to the Python path
+current_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.join(current_dir, "..")
+sys.path.append(parent_dir)
+
+
+from utils import *
+
+# Set page configuration
+st.set_page_config(
+    page_title = "MaxSum",
+    page_icon = os.path.join(parent_dir, "assets" , "QC-Devs.png"),
+)
+
+st.title("Brute Strength - MaxSum")
+
+description = """
+    Whereas the goal of the MaxMin algorithm is to maximize the minimum distance between any pair
+    of distinct elements in the selected subset of a dataset, the MaxSum algorithm aims to maximize
+    the sum of distances between all pairs of elements in the selected subset. When presented with
+    a dataset of samples, the initial point is chosen as the dataset’s medoid center. Next, 
+    the second point is chosen to be that which is furthest from this initial point. 
+    Subsequently, all following points are selected via the following logic:
+
+    1. Determine the sum of distances from every point to the already-selected ones.
+    2. Select the point which has the maximum sum of distances among those calculated in the previous step.
+
+    In the current implementation, this method requires or computes the full pairwise-distance
+    matrix, so it is not recommended for large datasets.
+    """
+
+
+references = "[1] Borodin, Allan, Hyun Chul Lee, and Yuli Ye, Max-sum diversification, "\
+             "monotone submodular functions and dynamic updates, Proceedings of the 31st ACM "\
+             "SIGMOD-SIGACT-SIGAI symposium on Principles of Database Systems. 2012."
+
+display_sidebar_info("Brute Strength - MaxSum", description, references)
+
+# File uploader for feature matrix or distance matrix (required)
+matrix_file = st.file_uploader("Upload a feature matrix or distance matrix (required)",
+                               type=["csv", "xlsx", "npz", "npy"], key="matrix_file", on_change=clear_results)
+
+# Clear selected indices if a new matrix file is uploaded
+if matrix_file is None:
+    st.session_state.pop("selected_ids", None)
+
+# Load data from matrix file
+else:
+    matrix = load_matrix(matrix_file)
+    num_points = st.number_input("Number of points to select", min_value = 1, step = 1,
+                                 key = "num_points", on_change=clear_results)
+    label_file = st.file_uploader("Upload a cluster label list (optional)", type = ["csv", "xlsx"],
+                                  key = "label_file", on_change=clear_results)
+    labels = load_labels(label_file) if label_file else None
+
+    distance_metric = st.selectbox("Select distance metric (optional)",
+                                   [None, "euclidean", "manhattan", "cosine"],
+                                   key = "distance_metric", on_change=clear_results)
+
+    if distance_metric:
+        fun_dist = lambda x: pairwise_distances(x, metric = distance_metric)
+    else:
+        fun_dist = None
+
+    if st.button("Run MaxSum Algorithm"):
+        if fun_dist:
+            selector = MaxSum(fun_dist)
+            selected_ids = run_algorithm(selector, matrix, num_points, labels)
+        else:
+            selector = MaxSum()
+            selected_ids = run_algorithm(selector, matrix, num_points, labels)
+
+
+# Check if the selected indices are stored in the session state
+if 'selected_ids' in st.session_state and matrix_file is not None:
+    selected_ids = st.session_state['selected_ids']
+    st.write("Selected indices:", selected_ids)
+
+    export_results(selected_ids)

streamlit_app/pages/Medoid.py

@@ -0,0 +1,95 @@
+# The Selector library provides a set of tools for selecting a
+# subset of the dataset and computing diversity.
+#
+# Copyright (C) 2023 The QC-Devs Community
+#
+# This file is part of Selector.
+#
+# Selector is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License
+# as published by the Free Software Foundation; either version 3
+# of the License, or (at your option) any later version.
+#
+# Selector is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, see <http://www.gnu.org/licenses/>
+#
+# --
+
+import streamlit as st
+import sys
+import os
+
+import scipy
+from selector.methods.partition import Medoid
+
+# Add the streamlit_app directory to the Python path
+current_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.join(current_dir, "..")
+sys.path.append(parent_dir)
+
+from utils import *
+
+# Set page configuration
+st.set_page_config(
+    page_title = "Medoid",
+    page_icon = os.path.join(parent_dir, "assets" , "QC-Devs.png"),
+)
+
+st.title("Medoid Method")
+
+
+description = """
+    Points are initially used to construct a KDTree. Eucleidean distances are used for this
+    algorithm. The first point selected is based on the starting_idx provided and becomes the first
+    query point. An approximation of the furthest point to the query point is found using
+    find_furthest_neighbor and is selected. find_nearest_neighbor is then done to eliminate close
+    neighbors to the new selected point. Medoid is then calculated from previously selected points
+    and is used as the new query point for find_furthest_neighbor, repeating the process. Terminates
+    upon selecting requested number of points or if all available points exhausted.
+    """
+
+references = "Adapted from: https://en.wikipedia.org/wiki/K-d_tree#Construction"
+
+display_sidebar_info("Medoid Method", description, references)
+
+# File uploader for feature matrix or distance matrix (required)
+matrix_file = st.file_uploader("Upload a feature matrix or distance matrix (required)",
+                               type=["csv", "xlsx", "npz", "npy"], key="matrix_file", on_change=clear_results)
+
+# Clear selected indices if a new matrix file is uploaded
+if matrix_file is None:
+    clear_results()
+
+# Load data from matrix file
+else:
+    matrix = load_matrix(matrix_file)
+    num_points = st.number_input("Number of points to select", min_value = 1, step = 1,
+                                 key = "num_points", on_change=clear_results)
+    label_file = st.file_uploader("Upload a cluster label list (optional)", type = ["csv", "xlsx"],
+                                  key = "label_file", on_change=clear_results)
+    labels = load_labels(label_file) if label_file else None
+
+    # Parameters for Medoid
+    st.info("The parameters below are optional. If not specified, default values will be used.")
+
+    start_id = st.number_input("Index for the first point to be selected. (start_id)", value = 0, step = 1, on_change=clear_results)
+
+    scaling = st.number_input("Percent of average maximum distance to use when eliminating the closest points. (scaling)",
+                              value=10.0, step=1.0, on_change=clear_results)
+
+    if st.button("Run Medoid Algorithm"):
+        selector = Medoid(start_id=start_id, func_distance = lambda x, y: scipy.spatial.minkowski_distance(x, y) ** 2, scaling=scaling)
+        selected_ids = run_algorithm(selector, matrix, num_points, labels)
+        st.session_state['selected_ids'] = selected_ids
+
+# Check if the selected indices are stored in the session state
+if 'selected_ids' in st.session_state and matrix_file is not None:
+    selected_ids = st.session_state['selected_ids']
+    st.write("Selected indices:", selected_ids)
+
+    export_results(selected_ids)

streamlit_app/pages/OptiSim.py

@@ -0,0 +1,105 @@
+# The Selector library provides a set of tools for selecting a
+# subset of the dataset and computing diversity.
+#
+# Copyright (C) 2023 The QC-Devs Community
+#
+# This file is part of Selector.
+#
+# Selector is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License
+# as published by the Free Software Foundation; either version 3
+# of the License, or (at your option) any later version.
+#
+# Selector is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, see <http://www.gnu.org/licenses/>
+#
+# --
+
+import streamlit as st
+import sys
+import os
+
+from selector.methods.distance import OptiSim
+
+# Add the streamlit_app directory to the Python path
+current_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.join(current_dir, "..")
+sys.path.append(parent_dir)
+
+from utils import *
+
+# Set page configuration
+st.set_page_config(
+    page_title = "OptiSim",
+    page_icon = os.path.join(parent_dir, "assets" , "QC-Devs.png"),
+)
+
+st.title("Adapted Optimizable K-Dissimilarity Selection (OptiSim)")
+
+
+description = """
+    The OptiSim algorithm selects samples from a dataset by first choosing the medoid center as the
+    initial point. Next, points are randomly chosen and added to a subsample if they exist
+    outside of radius r from all previously selected points (otherwise, they are discarded). Once k
+    number of points have been added to the subsample, the point with the greatest minimum distance
+    to the previously selected points is chosen. Then, the subsample is cleared and the process is
+    repeated.
+    """
+
+references = "[1] J. Chem. Inf. Comput. Sci. 1997, 37, 6, 1181–1188. https://doi.org/10.1021/ci970282v"
+
+display_sidebar_info("Adapted Optimizable K-Dissimilarity Selection (OptiSim)", description, references)
+
+# File uploader for feature matrix or distance matrix (required)
+matrix_file = st.file_uploader("Upload a feature matrix or distance matrix (required)",
+                               type=["csv", "xlsx", "npz", "npy"], key="matrix_file", on_change=clear_results)
+
+# Clear selected indices if a new matrix file is uploaded
+if matrix_file is None:
+    clear_results()
+
+# Load data from matrix file
+else:
+    matrix = load_matrix(matrix_file)
+    num_points = st.number_input("Number of points to select", min_value = 1, step = 1,
+                                 key = "num_points", on_change=clear_results)
+    label_file = st.file_uploader("Upload a cluster label list (optional)", type = ["csv", "xlsx"],
+                                  key = "label_file", on_change=clear_results)
+    labels = load_labels(label_file) if label_file else None
+
+    # Parameters for Directed Sphere Exclusion
+    st.info("The parameters below are optional. If not specified, default values will be used.")
+
+    r0 = st.number_input("Initial guess of radius for OptiSim algorithm (r0)", value=None, step=0.1,
+                         on_change=clear_results)
+    ref_index = st.number_input("Index for the sample to start selection from (ref_index)", value=0, step=1, on_change=clear_results)
+    k = st.number_input("Amount of points to add to subsample (k)", value=10, step=1,
+                          on_change=clear_results)
+    tol = st.number_input("Percentage error of number of samples selected (tol)", value=0.01, step=0.01, on_change=clear_results)
+    n_iter = st.number_input("Number of iterations to execute when optimizing the size of exclusion radius. (n_iter)",
+                             value=10, step=1, on_change=clear_results)
+    p = st.number_input("Minkowski p-norm distance (p)", value=2.0, step=1.0, on_change=clear_results)
+    eps = st.number_input("Approximate nearest neighbor search parameter (eps)", value=0.0, step=0.1,
+                          on_change=clear_results)
+    random_seed = st.number_input("Seed for random selection of points be evaluated. (random_seed)", value=42, step=1, on_change=clear_results)
+
+    if st.button("Run OptiSim Algorithm"):
+        selector = OptiSim(r0=r0, ref_index=ref_index, k=k, tol=tol, n_iter=n_iter, eps=eps, p=p, random_seed=random_seed)
+        selected_ids = run_algorithm(selector, matrix, num_points, labels)
+        st.session_state['selector'] = selector
+        st.session_state['selected_ids'] = selected_ids
+
+# Check if the selected indices are stored in the session state
+if 'selected_ids' in st.session_state and matrix_file is not None:
+    selected_ids = st.session_state['selected_ids']
+    st.write("Selected indices:", selected_ids)
+
+    if 'selector' in st.session_state:
+        st.write("Radius of the exclusion sphere:", st.session_state['selector'].r)
+
+    export_results(selected_ids)

streamlit_app/utils.py

@@ -0,0 +1,148 @@
+# The Selector library provides a set of tools for selecting a
+# subset of the dataset and computing diversity.
+#
+# Copyright (C) 2023 The QC-Devs Community
+#
+# This file is part of Selector.
+#
+# Selector is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License
+# as published by the Free Software Foundation; either version 3
+# of the License, or (at your option) any later version.
+#
+# Selector is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, see <http://www.gnu.org/licenses/>
+#
+# --
+
+import streamlit as st
+import numpy as np
+import pandas as pd
+import json
+import os
+
+from sklearn.metrics import pairwise_distances
+
+def set_page_config(page_title, page_icon):
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    assets_dir = os.path.join(current_dir, "..", "assets")
+
+    st.set_page_config(
+        page_title=page_title,
+        page_icon=os.path.join(assets_dir, page_icon)
+    )
+
+def display_sidebar_info(title, description, references):
+    st.sidebar.header(title)
+    st.sidebar.info(description)
+    st.sidebar.title("References")
+    st.sidebar.info(references)
+
+# Load data from matrix file
+def load_matrix(matrix_file):
+    try:
+        header_option = None
+        if matrix_file.name.endswith(".csv") or matrix_file.name.endswith(".xlsx"):
+            header_option = st.checkbox("Does the file have a header?", key="header_option",
+                                        on_change = clear_results())
+            st.warning("Warning: This will affect the final output if not specified correctly.")
+
+        if matrix_file.name.endswith(".csv") or matrix_file.name.endswith(".xlsx"):
+            if header_option:
+                # Load the matrix with header
+                matrix = pd.read_csv(matrix_file)
+            else:
+                # Load the matrix without header
+                matrix = pd.read_csv(matrix_file, header=None)
+            st.write("Matrix shape:", matrix.shape)
+            st.write(matrix.values)
+
+
+        elif matrix_file.name.endswith(".npz"):
+            matrix_data = np.load(matrix_file)
+            array_names = matrix_data.files # Select the array in the .npz file
+            selected_array = st.selectbox("Select the array to use", array_names)
+            matrix = matrix_data[selected_array]
+            st.write("Matrix shape:", matrix.shape)
+            st.write(matrix)
+        elif matrix_file.name.endswith(".npy"):
+            matrix = np.load(matrix_file)
+            st.write("Matrix shape:", matrix.shape)
+            st.write(matrix)
+        return matrix
+    except Exception as e:
+        st.error(f'An error occurred while loading matrix file: {e}')
+        return None
+
+def load_labels(label_file):
+    try:
+        label_header_option = None
+        if label_file.name.endswith(".csv") or label_file.name.endswith(".xlsx"):
+            label_header_option = st.checkbox("Does the file have a header?", key="label_header_option",
+                                              on_change = clear_results())
+            st.warning("Warning: This will affect the final output if not specified correctly.")
+
+        if label_file.name.endswith(".csv") or label_file.name.endswith(".xlsx"):
+            if label_header_option:
+                labels = pd.read_csv(label_file).values.flatten()
+            else:
+                labels = pd.read_csv(label_file, header=None).values.flatten()
+            st.write("Cluster labels shape:", labels.shape)
+            st.write(labels)
+        return labels
+    except Exception as e:
+        st.error(f'An error occurred while loading cluster label file: {e}')
+        return None
+
+def run_algorithm(selector, matrix, num_points, labels):
+    try:
+        # Separate the non-numeric first column (element names) and the numeric data
+        element_names = matrix.iloc[:, 0].values  # Assuming the first column contains the names
+        numeric_matrix = matrix.select_dtypes(include=[np.number]).values
+
+        if labels is not None:
+            selected_ids = selector.select(numeric_matrix, size = num_points, labels = labels)
+        else:
+            selected_ids = selector.select(numeric_matrix, size = num_points)
+
+        selected_ids = [(element_names[i], i) for i in selected_ids]
+        st.session_state['selected_ids'] = selected_ids
+        return selected_ids
+    except ValueError as ve:
+        st.error(f"An error occurred while running the algorithm: {ve}")
+    except Exception as e:
+        st.error(f"An error occurred while running the algorithm: {e}")
+    return None
+
+def export_results(selected_ids):
+    export_format = st.selectbox("Select export format", ["CSV", "JSON"], key="export_format")
+
+    if export_format == "CSV":
+        csv_data = pd.DataFrame(selected_ids, columns=["Element", "Index"])
+        csv = csv_data.to_csv(index=False).encode('utf-8')
+        st.download_button(
+            label="Download as CSV",
+            data=csv,
+            file_name='selected_indices.csv',
+            mime='text/csv',
+        )
+    else:
+        json_data = json.dumps([{"Element": elem, "Index": i} for i, elem in selected_ids])
+        st.download_button(
+            label="Download as JSON",
+            data=json_data,
+            file_name='selected_indices.json',
+            mime='application/json',
+        )
+
+# Function to clear selected indices from session state
+def clear_results():
+    if 'selected_ids' in st.session_state:
+        del st.session_state['selected_ids']
+    if 'selector' in st.session_state:
+        del st.session_state['selector']