Adding clustering App

Changelog.md

@@ -5,6 +5,7 @@
 
 - Add paging analysis App
 - Add capacity analysis App
+- Add automatic site clustering App
 
 ## [0.2.8] - 2025-04-22
 

app.py

@@ -129,6 +129,10 @@ if check_password():
                 "apps/sector_kml_generator.py",
                 title="📡 Sector KML Generator",
             ),
+            st.Page(
+                "apps/clustering.py",
+                title="📡 Automatic Site Clustering",
+            ),
             st.Page(
                 "apps/import_physical_db.py", title="🌏Physical Database Verification"
             ),

apps/clustering.py

@@ -0,0 +1,103 @@
+from io import BytesIO
+
+import numpy as np
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+from sklearn.cluster import KMeans
+
+
+def cluster_sites(
+    df: pd.DataFrame,
+    lat_col: str,
+    lon_col: str,
+    region_col: str,
+    max_sites: int = 25,
+    mix_regions: bool = False,
+):
+    clusters = []
+    cluster_id = 0
+
+    if not mix_regions:
+        grouped = df.groupby(region_col)
+    else:
+        grouped = [("All", df)]
+
+    for region, group in grouped:
+        coords = group[[lat_col, lon_col]].to_numpy()
+        n_clusters = max(1, int(np.ceil(len(group) / max_sites)))
+
+        if len(group) < max_sites:
+            labels = np.zeros(len(group), dtype=int)
+        else:
+            kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
+            labels = kmeans.fit_predict(coords)
+
+        group = group.copy()
+        group["Cluster"] = [f"C{cluster_id + l}" for l in labels]
+        clusters.append(group)
+        cluster_id += len(set(labels))
+
+    return pd.concat(clusters)
+
+
+def to_excel(df: pd.DataFrame) -> bytes:
+    output = BytesIO()
+    with pd.ExcelWriter(output, engine="xlsxwriter") as writer:
+        df.to_excel(writer, index=False, sheet_name="Clusters")
+    return output.getvalue()
+
+
+st.title("Automatic Site Clustering App")
+
+uploaded_file = st.file_uploader("Upload your Excel file", type=["xlsx"])
+
+if uploaded_file:
+    df = pd.read_excel(uploaded_file)
+    st.write("Sample of uploaded data:", df.head())
+
+    columns = df.columns.tolist()
+
+    with st.form("clustering_form"):
+        lat_col = st.selectbox("Select Latitude column", columns)
+        lon_col = st.selectbox("Select Longitude column", columns)
+        region_col = st.selectbox("Select Region column", columns)
+        code_col = st.selectbox("Select Site Code column", columns)
+        max_sites = st.number_input(
+            "Max sites per cluster", min_value=5, max_value=100, value=25
+        )
+        mix_regions = st.checkbox(
+            "Allow mixing different regions in clusters", value=False
+        )
+        submitted = st.form_submit_button("Run Clustering")
+
+    if submitted:
+        clustered_df = cluster_sites(
+            df, lat_col, lon_col, region_col, max_sites, mix_regions
+        )
+        st.success("Clustering completed!")
+        st.write(clustered_df.head())
+
+        # Plot
+        fig = px.scatter_map(
+            clustered_df,
+            lat=lat_col,
+            lon=lon_col,
+            color="Cluster",
+            hover_name=code_col,
+            hover_data=[region_col],
+            zoom=5,
+            height=600,
+        )
+        fig.update_layout(mapbox_style="open-street-map")
+        st.plotly_chart(fig)
+
+        # Download button
+        st.download_button(
+            label="Download clustered Excel file",
+            data=to_excel(clustered_df),
+            file_name="clustered_sites.xlsx",
+            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+            on_click="ignore",
+            type="primary",
+        )