Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,150 +1,126 @@
 import os
 
-# ✅ FIX HuggingFace PermissionError
-os.environ["STREAMLIT_HOME"] = "/tmp"
+# 🛡️ SUPPRESS Streamlit telemetry and permission errors
+os.environ["HOME"] = "/tmp"
 os.environ["XDG_CONFIG_HOME"] = "/tmp"
 os.environ["XDG_CACHE_HOME"] = "/tmp"
+os.environ["STREAMLIT_HOME"] = "/tmp"
+os.environ["STREAMLIT_GATHER_USAGE_STATS"] = "false"
 
 import streamlit as st
 import pandas as pd
-import numpy as np
 import networkx as nx
 import folium
 from streamlit_folium import st_folium
 from geopy.distance import geodesic
 from datetime import datetime, timedelta
+import warnings
+warnings.filterwarnings("ignore")
 
-# Dapatkan path absolut direktori saat ini (yaitu src/)
-BASE_DIR = os.path.dirname(__file__)
-
-# Konfigurasi halaman Streamlit
+# Page config
 st.set_page_config(
-    page_title="MODA",
+    page_title="MODA Multimodal Routing",
     layout="wide",
     initial_sidebar_state="expanded"
 )
 
-# Fungsi untuk memuat data CSV dengan caching
+# ─── Data Loading ───────────────────────────────────────────────────────────
 @st.cache_data
-def load_csv_from_local(file_path):
-    try:
-        full_path = os.path.join(BASE_DIR, file_path)
-        df = pd.read_csv(full_path)
-        df.columns = [col.lower() for col in df.columns]
-        return df
-    except Exception as e:
-        st.error(f"Error memuat data dari {file_path}: {e}")
-        return pd.DataFrame()
-
-@st.cache_data
-def load_all_data():
-    file_paths = {
-        'buildings': '../data/poi_places.csv',
-        'road_nodes': '../data/road_nodes.csv',
-        'road_edges': '../data/road_edges.csv'
+def load_data():
+    """Load CSVs from data/ folder in repo root."""
+    dfs = {
+        "gdf_poi": pd.read_csv("data/poi_places.csv"),
+        "gdf_nodes": pd.read_csv("data/road_nodes.csv"),
+        "gdf_edges": pd.read_csv("data/road_edges.csv")
     }
-    data = {}
-    for key, path in file_paths.items():
-        data[key] = load_csv_from_local(path)
-    return data
+    # normalize column names
+    for k, df in dfs.items():
+        df.columns = [c.lower() for c in df.columns]
+    return dfs
 
+# ─── Build Graph ────────────────────────────────────────────────────────────
 @st.cache_data
 def build_graph(gdf_nodes, gdf_edges, gdf_poi):
     G = nx.Graph()
-    for _, row in gdf_nodes.iterrows():
-        G.add_node(row['osmid'], x=row.geometry.x, y=row.geometry.y)
-
-    for _, row in gdf_edges.iterrows():
-        if pd.notna(row['length']):
-            G.add_edge(row['u'], row['v'], length=row['length'])
-
-    for idx, row in gdf_poi.iterrows():
+    # add road nodes
+    for _, r in gdf_nodes.iterrows():
+        G.add_node(r.osmid, x=r.geometry.x, y=r.geometry.y)
+    # add edges
+    for _, r in gdf_edges.iterrows():
+        if pd.notna(r.length):
+            G.add_edge(r.u, r.v, length=r.length)
+    # add POI nodes
+    for idx, r in gdf_poi.iterrows():
         nid = f"poi_{idx}"
-        G.add_node(nid, x=row['longitude'], y=row['latitude'], name=row.get('name', f"POI {idx}"))
-
-    road_nodes_list = [(n, data['y'], data['x']) for n, data in G.nodes(data=True) if isinstance(n, (int, float))]
-
-    for idx, row in gdf_poi.iterrows():
-        bnode = f"poi_{idx}"
-        b_lat, b_lon = row.latitude, row.longitude
-        nearest = min(road_nodes_list, key=lambda item: geodesic((b_lat, b_lon), (item[1], item[2])).meters)[0]
-        dist = geodesic((b_lat, b_lon), (G.nodes[nearest]['y'], G.nodes[nearest]['x'])).meters
-        G.add_edge(bnode, nearest, length=dist)
-        G.add_edge(nearest, bnode, length=dist)
-
+        G.add_node(nid, x=r.longitude, y=r.latitude, name=r.name)
+    # connect POIs to nearest road
+    road_list = [(n, d['y'], d['x']) for n, d in G.nodes(data=True) if isinstance(n, (int, float))]
+    for idx, r in gdf_poi.iterrows():
+        nid = f"poi_{idx}"
+        lat, lon = r.latitude, r.longitude
+        nearest = min(road_list, key=lambda x: geodesic((lat, lon), (x[1], x[2])).meters)[0]
+        dist = geodesic((lat, lon), (G.nodes[nearest]['y'], G.nodes[nearest]['x'])).meters
+        G.add_edge(nid, nearest, length=dist)
+        G.add_edge(nearest, nid, length=dist)
     return G
 
-def get_node_id_by_name(name, gdf):
-    if 'name' not in gdf.columns:
-        return None
-    match = gdf[gdf['name'].str.lower().str.strip() == name.lower().strip()]
-    return f"poi_{match.index[0]}" if not match.empty else None
-
-def plot_folium_path(G, path, start_name, end_name):
-    m = folium.Map(location=[-6.18, 106.83], zoom_start=14)
-    coords = [(G.nodes[n]['y'], G.nodes[n]['x']) for n in path if 'x' in G.nodes[n] and 'y' in G.nodes[n]]
-    folium.PolyLine(coords, color='blue', weight=5, popup='Rute').add_to(m)
-    folium.Marker(coords[0], tooltip='Start', icon=folium.Icon(color='green')).add_to(m)
-    folium.Marker(coords[-1], tooltip='Destination', icon=folium.Icon(color='red')).add_to(m)
-    return m
+# ─── Routing Helpers ────────────────────────────────────────────────────────
+def get_poi_id(name, gdf_poi):
+    df = gdf_poi[gdf_poi.name.str.lower().str.strip() == name.lower().strip()]
+    return f"poi_{df.index[0]}" if not df.empty else None
 
-def compute_drive(G, start_id, end_id):
+# ─── Compute Paths ──────────────────────────────────────────────────────────
+def compute_route(G, start_id, end_id, speed_kmh):
     path = nx.shortest_path(G, start_id, end_id, weight='length')
     dist_m = nx.shortest_path_length(G, start_id, end_id, weight='length')
-    dist_km = dist_m / 1000
-    est_time = (dist_km / 30) * 60
-    cost = int(dist_km * 3000)
-    return path, dist_km, est_time, cost
-
-def compute_bus(G, start_id, end_id, time_now):
-    path = nx.shortest_path(G, start_id, end_id, weight='length')
-    dist_m = nx.shortest_path_length(G, start_id, end_id, weight='length')
-    dist_km = dist_m / 1000
-    est_time = (dist_km / 25) * 60
-    arrival = datetime.strptime(time_now, "%H:%M") + timedelta(minutes=est_time)
-    return path, dist_km, est_time, arrival.strftime("%H:%M")
-
-# ==== UI ====
-st.title("🚏 Multimodal Shortest Path Jakarta")
-st.markdown("Bandingkan rute tercepat antara **Drive** dan **Bus** dari titik-titik penting di Jakarta Pusat.")
-
-with st.sidebar:
-    st.header("📍 Input Rute")
-    data = load_all_data()
-    gdf_nodes = data["road_nodes"]
-    gdf_edges = data["road_edges"]
-    gdf_poi = data["buildings"]
-
-    st.write("📋 Kolom tersedia di gdf_poi:", gdf_poi.columns.tolist())
-
-    if 'name' in gdf_poi.columns:
-        name_list = sorted(gdf_poi['name'].dropna().unique().tolist())
-        start_name = st.selectbox("Pilih Lokasi Awal:", name_list)
-        end_name = st.selectbox("Pilih Lokasi Tujuan:", name_list, index=1)
-        time_now = st.time_input("Jam Keberangkatan", value=datetime.now().time())
-    else:
-        st.error("Kolom 'name' tidak ditemukan di poi_places.csv. Kolom yang tersedia: " + ", ".join(gdf_poi.columns))
-        st.stop()
-
-if st.button("🔍 Cari Rute"):
+    km = dist_m/1000
+    time_min = km / speed_kmh * 60
+    return path, km, time_min
+
+# ─── Streamlit UI ───────────────────────────────────────────────────────────
+# load data
+dfs = load_data()
+gdf_poi = dfs['gdf_poi']
+gdf_nodes = dfs['gdf_nodes']
+gdf_edges = dfs['gdf_edges']
+
+# sidebar inputs
+st.sidebar.header("📍 Input Rute")
+poi_list = gdf_poi['name'].tolist()
+start = st.sidebar.selectbox("Dari:", poi_list)
+end = st.sidebar.selectbox("Ke:", poi_list, index=1)
+time_input = st.sidebar.time_input("Waktu Berangkat", value=datetime.now().time())
+
+if st.sidebar.button("🔍 Cari Rute"):
     G = build_graph(gdf_nodes, gdf_edges, gdf_poi)
-    src = get_node_id_by_name(start_name, gdf_poi)
-    dst = get_node_id_by_name(end_name, gdf_poi)
-
-    if src is None or dst is None:
-        st.error("Lokasi tidak ditemukan dalam daftar POI.")
+    s_id = get_poi_id(start, gdf_poi)
+    e_id = get_poi_id(end, gdf_poi)
+    if not s_id or not e_id:
+        st.sidebar.error("Pilih lokasi yang valid.")
     else:
+        # Drive
+        path_d, d_km, t_min = compute_route(G, s_id, e_id, speed_kmh=30)
+        cost = int(d_km*3000)
         st.subheader("🚗 Drive Mode")
-        drive_path, drive_dist, drive_time, drive_cost = compute_drive(G, src, dst)
-        st.write(f"**Jarak:** {drive_dist:.2f} km")
-        st.write(f"**Estimasi Waktu:** {drive_time:.1f} menit")
-        st.write(f"**Biaya:** {drive_cost:,} IDR")
-        st_folium(plot_folium_path(G, drive_path, start_name, end_name), width=700)
-
+        st.write(f"Jarak: **{d_km:.2f} km**")
+        st.write(f"Waktu: **{t_min:.1f} menit**")
+        st.write(f"Biaya: **{cost:,} IDR**")
+        m1 = folium.Map(location=[gdf_poi.latitude.mean(), gdf_poi.longitude.mean()], zoom_start=13)
+        coords = [(G.nodes[n]['y'], G.nodes[n]['x']) for n in path_d]
+        folium.PolyLine(coords, color='blue', weight=4).add_to(m1)
+        st_folium(m1, width=700, height=500)
+        # Bus
+        path_b, b_km, b_min = compute_route(G, s_id, e_id, speed_kmh=25)
+        arrival = (datetime.combine(datetime.today(), time_input) + timedelta(minutes=b_min)).time()
         st.subheader("🚌 Bus Mode")
-        bus_path, bus_dist, bus_time, bus_arrival = compute_bus(G, src, dst, time_now.strftime("%H:%M"))
-        st.write(f"**Jarak:** {bus_dist:.2f} km")
-        st.write(f"**Estimasi Waktu:** {bus_time:.1f} menit")
-        st.write(f"**Waktu Tiba:** {bus_arrival}")
-        st.write("**Biaya:** 3.500 IDR")
-        st_folium(plot_folium_path(G, bus_path, start_name, end_name), width=700)
+        st.write(f"Jarak: **{b_km:.2f} km**")
+        st.write(f"Waktu: **{b_min:.1f} menit**")
+        st.write(f"Tiba: **{arrival.strftime('%H:%M')}**")
+        st.write("Biaya: **3500 IDR**")
+        m2 = folium.Map(location=[gdf_poi.latitude.mean(), gdf_poi.longitude.mean()], zoom_start=13)
+        coords2 = [(G.nodes[n]['y'], G.nodes[n]['x']) for n in path_b]
+        folium.PolyLine(coords2, color='green', weight=4).add_to(m2)
+        st_folium(m2, width=700, height=500)
+
+else:
+    st.info("Pilih rute dan klik **Cari Rute** di sidebar.")