Removed duplicate code

streamlit_app/pages/{2_MaxSum.py → page_MaxSum.py}

@@ -21,32 +21,22 @@
 # --
 
 import streamlit as st
-import numpy as np
-import pandas as pd
-import json
 import os
+import sys
 
-from sklearn.metrics import pairwise_distances
 from selector.methods.distance import MaxSum
 
-# Get the current directory path
+# 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)
 
-# Construct the path to the assets directory
-assets_dir = os.path.join(current_dir, "..", "assets")
+from utils import display_sidebar_info, load_matrix, load_labels, run_algorithm, export_results
 
-# Set page configuration
-st.set_page_config(
-    page_title="MaxSum",
-    page_icon=os.path.join(assets_dir, "QC-Devs.png")
-)
 
 st.title("Brute Strength - MaxSum")
 
-st.sidebar.header("Brute Strength - MaxSum")
-
-st.sidebar.info(
-    """
+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
@@ -60,16 +50,17 @@ st.sidebar.info(
     In the current implementation, this method requires or computes the full pairwise-distance
     matrix, so it is not recommended for large datasets.
     """
-)
 
-st.sidebar.title("References")
 
-st.sidebar.info("[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.")
+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")
+matrix_file = st.file_uploader("Upload a feature matrix or distance matrix (required)",
+                               type=["csv", "xlsx", "npz", "npy"], key="matrix_file")
 
 # Clear selected indices if a new matrix file is uploaded
 if matrix_file is None:
@@ -77,87 +68,15 @@ if matrix_file is None:
 
 # Load data from matrix file
 else:
-    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")
-            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).values
-            else:
-                # Load the matrix without header
-                matrix = pd.read_csv(matrix_file, header = None).values
-            st.write("Matrix shape:", matrix.shape)
-            st.write(matrix)
-
-        elif matrix_file.name.endswith(".npz"):
-            matrix_data = np.load(matrix_file)
-            # Select the array in the .npz file
-            array_names = matrix_data.files
-            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)
-    except Exception as e:
-        st.error(f'An error occurred while loading matrix file: {e}')
-        matrix = None
-
-
-    # Input for number of points to select (required)
-    num_points = st.number_input("Number of points to select", min_value=1, step=1, key="num_points")
-
-    # Input for cluster label list (optional)
-    label_file = st.file_uploader("Upload a cluster label list (optional)", type=["csv", "xlsx"], key="label_file")
-    labels = None
-    if label_file is not None:
-        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")
-                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)
-        except Exception as e:
-            st.error(f'An error occurred while loading cluster label file: {e}')
-            labels = None
-
+    matrix = load_matrix(matrix_file)
+    num_points = st.number_input("Number of points to select", min_value = 1, step = 1,
+                                 key = "num_points")
+    label_file = st.file_uploader("Upload a cluster label list (optional)", type = ["csv", "xlsx"],
+                                  key = "label_file")
+    labels = load_labels(label_file) if label_file else None
 
     if st.button("Run MaxSum Algorithm"):
-        try:
-            # Check if the input matrix is a feature matrix or a distance matrix
-            if matrix.shape[0] == matrix.shape[1]:
-                # Distance matrix
-                selector = MaxSum()
-                selected_ids = selector.select(matrix, size = num_points, labels = labels)
-            else:
-                # Feature matrix
-                selector = MaxSum(lambda x: pairwise_distances(x, metric = "euclidean"))
-                selected_ids = selector.select(matrix, size = num_points, labels = labels)
-
-            # Convert selected indices to a list of integers
-            selected_ids = [int(i) for i in selected_ids]
-
-            # Save selected indices to session state
-            st.session_state['selected_ids'] = selected_ids
-        except ValueError as ve:
-            st.error(f"An error occurred while running the MaxMin algorithm: {ve}")
-        except Exception as e:
-            st.error(f"An error occurred while running the MaxMin algorithm: {e}")
+        selected_ids = run_algorithm(MaxSum, matrix, num_points, labels)
 
 
 # Check if the selected indices are stored in the session state
@@ -165,23 +84,4 @@ 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 format
-    export_format = st.selectbox("Select export format", ["CSV", "JSON"], key="export_format")
-
-    if export_format == "CSV":
-        csv_data = pd.DataFrame(selected_ids, columns = ["Selected Indices"])
-        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({"Selected Indices": selected_ids})
-        st.download_button(
-            label = "Download as JSON",
-            data = json_data,
-            file_name = 'selected_indices.json',
-            mime = 'application/json',
-        )
+    export_results(selected_ids)

streamlit_app/pages/page_maxmin.py

@@ -21,32 +21,21 @@
 # --
 
 import streamlit as st
-import numpy as np
-import pandas as pd
-import json
 import os
+import sys
 
-from sklearn.metrics import pairwise_distances
 from selector.methods.distance import MaxMin
 
-# Get the current directory path
+# 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)
 
-# Construct the path to the assets directory
-assets_dir = os.path.join(current_dir, "..", "assets")
-
-# Set page configuration
-st.set_page_config(
-    page_title="MaxMin",
-    page_icon=os.path.join(assets_dir, "QC-Devs.png")
-)
+from utils import display_sidebar_info, load_matrix, load_labels, run_algorithm, export_results
 
 st.title("Brute Strength - MaxMin")
 
-st.sidebar.header("Brute Strength - MaxMin")
-
-st.sidebar.info(
-    """
+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
@@ -61,127 +50,37 @@ st.sidebar.info(
     In the current implementation, this method requires or computes the full pairwise-distance
     matrix, so it is not recommended for large datasets.
     """
-)
 
-st.sidebar.title("References")
 
-st.sidebar.info("[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.")
+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")
+matrix_file = st.file_uploader("Upload a feature matrix or distance matrix (required)",
+                               type=["csv", "xlsx", "npz", "npy"], key="matrix_file")
 
 # 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:
-    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")
-            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).values
-            else:
-                # Load the matrix without header
-                matrix = pd.read_csv(matrix_file, header = None).values
-            st.write("Matrix shape:", matrix.shape)
-            st.write(matrix)
-
-        elif matrix_file.name.endswith(".npz"):
-            matrix_data = np.load(matrix_file)
-            # Select the array in the .npz file
-            array_names = matrix_data.files
-            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)
-    except Exception as e:
-        st.error(f'An error occurred while loading matrix file: {e}')
-        matrix = None
-
-
-    # Input for number of points to select (required)
-    num_points = st.number_input("Number of points to select", min_value=1, step=1, key="num_points")
-
-    # Input for cluster label list (optional)
-    label_file = st.file_uploader("Upload a cluster label list (optional)", type=["csv", "xlsx"], key="label_file")
-    labels = None
-    if 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")
-                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)
-        except Exception as e:
-            st.error(f'An error occurred while loading cluster label file: {e}')
-            labels = None
-
+    matrix = load_matrix(matrix_file)
+    num_points = st.number_input("Number of points to select", min_value = 1, step = 1,
+                                 key = "num_points")
+    label_file = st.file_uploader("Upload a cluster label list (optional)", type = ["csv", "xlsx"],
+                                  key = "label_file")
+    labels = load_labels(label_file) if label_file else None
 
     if st.button("Run MaxMin Algorithm"):
-        try:
-            # Check if the input matrix is a feature matrix or a distance matrix
-            if matrix.shape[0] == matrix.shape[1]:
-                # Distance matrix
-                selector = MaxMin()
-                selected_ids = selector.select(matrix, size = num_points, labels = labels)
-            else:
-                # Feature matrix
-                selector = MaxMin(lambda x: pairwise_distances(x, metric = "euclidean"))
-                selected_ids = selector.select(matrix, size = num_points, labels = labels)
-
-            # Convert selected indices to a list of integers
-            selected_ids = [int(i) for i in selected_ids]
-
-            # Save selected indices to session state
-            st.session_state['selected_ids'] = selected_ids
-        except ValueError as ve:
-            st.error(f"An error occurred while running the MaxMin algorithm: {ve}")
-        except Exception as e:
-            st.error(f"An error occurred while running the MaxMin algorithm: {e}")
+        selected_ids = run_algorithm(MaxMin, 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 format
-    export_format = st.selectbox("Select export format", ["CSV", "JSON"], key="export_format")
-
-    if export_format == "CSV":
-        csv_data = pd.DataFrame(selected_ids, columns = ["Selected Indices"])
-        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({"Selected Indices": selected_ids})
-        st.download_button(
-            label = "Download as JSON",
-            data = json_data,
-            file_name = 'selected_indices.json',
-            mime = 'application/json',
-        )
+    export_results(selected_ids)