apps/multi_points_distance_calculator.py

import pandas as pd
import streamlit as st

from utils.utils_functions import calculate_distances

# Streamlit UI
st.title("Multi Points Distance Calculator")

st.write(
    """This app allows you to calculate the distance between multiple points in 2 datasets.
    Please choose a file containing the latitude and longitude columns for each dataset.
                      """
)

dataset1_sample_file_path = "samples/Dataset1.xlsx"
dataset2_sample_file_path = "samples/Dataset2.xlsx"

download_col1, download_col2 = st.columns(2)

with download_col1:
    st.download_button(
        label="Dataset1 Sample File",
        data=open(dataset1_sample_file_path, "rb").read(),
        file_name="Dataset1.xlsx",
        mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
    )
with download_col2:
    st.download_button(
        label="Dataset2 Sample File",
        data=open(dataset2_sample_file_path, "rb").read(),
        file_name="Dataset2.xlsx",
        mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
    )

upload_data1_col, upload_data2_col = st.columns(2)

with upload_data1_col:
    # Upload Dataset 1
    st.subheader("Upload Dataset 1 (Reference Points)")
    file1 = st.file_uploader("Upload first dataset (Excel)", type=["xlsx"], key="file1")

with upload_data2_col:
    # Upload Dataset 2
    st.subheader("Upload Dataset 2 (Comparison Points)")
    file2 = st.file_uploader(
        "Upload second dataset (Excel)", type=["xlsx"], key="file2"
    )


if file1 and file2:
    param_col1, param_col2 = st.columns(2)
    try:
        # Read the datasets
        df1 = pd.read_excel(file1)
        df2 = pd.read_excel(file2)

        with param_col1:
            st.subheader("Select Columns for Dataset 1")
            code_col1 = st.selectbox("Select 'CODE' Column", df1.columns, key="code1")
            lat_col1 = st.selectbox("Select 'Latitude' Column", df1.columns, key="lat1")
            long_col1 = st.selectbox(
                "Select 'Longitude' Column", df1.columns, key="long1"
            )

        with param_col2:
            st.subheader("Select Columns for Dataset 2")
            code_col2 = st.selectbox("Select 'CODE' Column", df2.columns, key="code2")
            lat_col2 = st.selectbox("Select 'Latitude' Column", df2.columns, key="lat2")
            long_col2 = st.selectbox(
                "Select 'Longitude' Column", df2.columns, key="long2"
            )
        min_distance = st.number_input(
            "Minimum Distance (km)", min_value=0.0, value=5.0
        )
        # Calculate distances when button is clicked
        if st.button("Calculate Distances"):
            df_distances, df_closest, df_closest_min_distance = calculate_distances(
                df1,
                df2,
                code_col1,
                lat_col1,
                long_col1,
                code_col2,
                lat_col2,
                long_col2,
                min_distance,
            )

            # # Display all distances
            # st.subheader("All Distances")
            # st.dataframe(df_distances)

            # Display closest points
            st.subheader("Closest Matches")
            st.dataframe(df_closest)
            st.subheader("Closest Matches below Min Distance")
            st.dataframe(df_closest_min_distance)

            # Downloadable All distances CSV
            st.download_button(
                label="Download All Distances as CSV",
                data=df_distances.to_csv(index=False),
                file_name="all_distances.csv",
                mime="text/csv",
                on_click="ignore",
                type="primary",
            )

            # Downloadable Closest matches CSV
            st.download_button(
                label="Download Closest Matches as CSV",
                data=df_closest.to_csv(index=False),
                file_name="closest_matches.csv",
                mime="text/csv",
                on_click="ignore",
                type="primary",
            )

            # Downloadable Closest matches below Min Distance CSV
            st.download_button(
                label=f"Download Closest Matches below {min_distance}km as CSV",
                data=df_closest_min_distance.to_csv(index=False),
                file_name=f"closest_matches_{min_distance}km.csv",
                mime="text/csv",
                on_click="ignore",
                type="primary",
            )

    except Exception as e:
        st.error(f"Error processing files: {e}")