Adding multi points distances calculator

app.py

@@ -15,9 +15,13 @@ pages = {
     "Apps": [
         st.Page("apps/database_page.py", title="Generate Databases"),
         st.Page("apps/core_dump_page.py", title="Parse dump core"),
-        st.Page("apps/import_physical_db.py", title="Physical Database Verification"),
         st.Page("apps/gps_converter.py", title="GPS Converter"),
         st.Page("apps/distance.py", title="Distance Calculator"),
+        st.Page(
+            "apps/multi_points_distance_calculator.py",
+            title="Multi Points Distance Calculator",
+        ),
+        st.Page("apps/import_physical_db.py", title="Physical Database Verification"),
     ],
     "Documentations": [
         st.Page("documentations/database_doc.py", title="Databases Documentation"),

apps/multi_points_distance_calculator.py

@@ -0,0 +1,101 @@
+import pandas as pd
+import streamlit as st
+from geopy.distance import geodesic
+
+
+# Function to calculate distances while preserving all original columns
+def calculate_distances(
+    df1: pd.DataFrame,
+    df2: pd.DataFrame,
+    code_col1,
+    lat_col1,
+    long_col1,
+    code_col2,
+    lat_col2,
+    long_col2,
+):
+    distances = []
+
+    for _, row1 in df1.iterrows():
+        for _, row2 in df2.iterrows():
+            coord1 = (row1[lat_col1], row1[long_col1])
+            coord2 = (row2[lat_col2], row2[long_col2])
+            distance_km = geodesic(coord1, coord2).kilometers  # Compute distance
+
+            # Combine all original columns + distance
+            combined_row = {
+                **row1.to_dict(),  # Keep all columns from Dataset1
+                **{
+                    f"{col}_Dataset2": row2[col] for col in df2.columns
+                },  # Keep all columns from Dataset2
+                "Distance_km": distance_km,
+            }
+            distances.append(combined_row)
+
+    df_distances = pd.DataFrame(distances)
+
+    # Find the closest point for each Point1
+    df_closest: pd.DataFrame = df_distances.loc[
+        df_distances.groupby(code_col1)["Distance_km"].idxmin()
+    ]
+
+    return df_distances, df_closest
+
+
+# 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.
+                      """
+)
+
+# Upload Dataset 1
+st.subheader("Upload Dataset 1 (Reference Points)")
+file1 = st.file_uploader("Upload first dataset (Excel)", type=["xlsx"], key="file1")
+
+# 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:
+    try:
+        # Read the datasets
+        df1 = pd.read_excel(file1)
+        df2 = pd.read_excel(file2)
+
+        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")
+
+        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")
+
+        # Calculate distances when button is clicked
+        if st.button("Calculate Distances"):
+            df_distances, df_closest = calculate_distances(
+                df1, df2, code_col1, lat_col1, long_col1, code_col2, lat_col2, long_col2
+            )
+
+            # Display all distances
+            st.subheader("All Distances")
+            st.dataframe(df_distances)
+
+            # Display closest points
+            st.subheader("Closest Matches")
+            st.dataframe(df_closest)
+
+            # Downloadable 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",
+            )
+
+    except Exception as e:
+        st.error(f"Error processing files: {e}")