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front.py
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front.py
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import streamlit as st
import folium
from streamlit_folium import folium_static
import pandas as pd
from folium.plugins import MarkerCluster
import matplotlib.pyplot as plt
from logic import (
get_station_data,
color_gradient,
calculate_entropy,
plan_continuous_route,
my_icon_create_function,
get_broken_dockers
)
st. set_page_config(page_title="T2C Real Time Workshop", layout="wide")
def build_broken_docks_map(broken_docks):
map = folium.Map(location=(41.3933173, 2.1812483),
zoom_start=12, tiles="openstreetmap")
for _, station in broken_docks.iterrows():
tooltip_text = f"""
<b>{station['STATION_NAME']}</b><br>
Broken Dockers: {station['BROKEN_DOCKS']}
"""
# f"{station['STATION_NAME']} \n Broken Dockers: {station['BROKEN_DOCKS']}"
folium.Marker(
[station['LAT'], station['LON']],
popup=folium.Popup(tooltip_text, max_width=100)
).add_to(map)
return map
def distribution_page():
# ----------------- BASIC STRUCTURE -----------------
st.title("Bike Redistribution")
st.write("This visualization helps plan the redistribution of bikes to ensure all stations are optimally stocked.")
bus_capacity = 20
color_treshold = 0.1
# ----------------- DATA -----------------
station_data = get_station_data()
route, updated_stations = plan_continuous_route(station_data, bus_capacity)
# ----------------- TODO: Calculate distances properly, otherwise route looks chaotic (you can try uncomment) -----------------
# Load the routes from the file during inference
# with open("nearby_routes.json", "r") as file:
# loaded_routes = json.load(file)
# all_routes = {eval(k): v for k, v in loaded_routes.items()}
steps_description = "Bike Redistribution Plan:\n"
# ----------------- MAPS -----------------
# -------- Map pre + route --------
m = folium.Map(location=[41.3933173, 2.1812483], zoom_start=12, tiles='openstreetmap')
for _, station in station_data.iterrows():
color = color_gradient(station['RATIO'])
folium.Circle(
location=[station["LAT"], station["LON"]],
radius=60,
color=color,
fill_color=color,
tooltip=f"Station ID: {station['STATION_NAME']} - Bikes Available: {station['NUM_BIKES_AVAILABLE']}").add_to(m)
for start, end, amount, action in route:
start_lat_lon = station_data[station_data['STATION_ID'] == start][['LAT', 'LON']].values[0]
end_lat_lon = station_data[station_data['STATION_ID'] == end][['LAT', 'LON']].values[0]
folium.PolyLine(
[start_lat_lon, end_lat_lon],
color="black",
weight=2.5,
opacity=0.8
).add_to(m)
steps_description += f"Bus {action}, then moved from Station {start} to Station {end}.\n"
# -------- Map post rute --------
m_updated = folium.Map(location=[41.3933173, 2.1812483], zoom_start=12, tiles='openstreetmap')
marker_cluster = MarkerCluster(icon_create_function=my_icon_create_function, options={"disableClusteringAtZoom":14}).add_to(m_updated)
for _, station in updated_stations.iterrows():
color = color_gradient(station['NUM_BIKES_AVAILABLE'] / station['TOTAL_CAPACITY'], treshold=color_treshold)
folium.Circle(
location=[station["LAT"], station["LON"]],
radius=60,
color=color,
fill_color=color,
weight = station['NUM_BIKES_AVAILABLE'] / station['TOTAL_CAPACITY']+2,
tooltip=f"Station ID: {station['STATION_ID']} - Bikes Available: {station['NUM_BIKES_AVAILABLE']}"
).add_to(marker_cluster)
# ----------------- HISTOGRAMS -----------------
col1, col2 = st.columns(2)
with col1:
st.subheader("Original Distribution")
folium_static(m, height=400)
fig1, ax1 = plt.subplots()
ax1.hist(station_data['RATIO'], bins=40)
ax1.set_xlabel("Ratio")
ax1.set_ylabel("Frequency")
ax1.set_title("Original Distribution Histogram")
ax1.set_xlim(0, 1)
st.pyplot(fig1)
with col2:
st.subheader("Updated Distribution")
folium_static(m_updated, height=400)
fig2, ax2 = plt.subplots()
ax2.hist(updated_stations['NUM_BIKES_AVAILABLE'] / updated_stations['TOTAL_CAPACITY'], bins=40)
ax2.set_xlabel("Ratio")
ax2.set_ylabel("Frequency")
ax2.set_title("Updated Distribution Histogram")
ax2.set_xlim(0, 1)
st.pyplot(fig2)
# ----------------- ENTROPY (really useful for us; star metric) -----------------
initial_entropy = station_data['RATIO'].apply(calculate_entropy).sum()
final_entropy = updated_stations['NUM_BIKES_AVAILABLE'].divide(updated_stations['TOTAL_CAPACITY']).apply(calculate_entropy).sum()
st.write(f"Initial Entropy: {initial_entropy:.4f}, Final Entropy: {final_entropy:.4f}, Entropy Gain: {final_entropy - initial_entropy:.4f}")
# ----------------- ROUTE DETAILS (can be cleaned and presented more nicely; used for debugging so far) -----------------
with st.expander("Route Details"):
st.write("Updated stations:")
st.write(updated_stations)
st.write("Route:")
st.write(route)
st.text(steps_description)
def broken_docks_page():
st.title("Broken Docks")
col1, col2 = st.columns([2, 1])
broken_docks = get_broken_dockers()
map = build_broken_docks_map(broken_docks)
with col1:
st.subheader("City Bike Stations Map")
folium_static(map, height=400)
with col2: # Display metadata in the second column
st.subheader("Station List")
st.dataframe(broken_docks[["STATION_NAME", "BROKEN_DOCKS"]])
def main():
st.sidebar.title("Navigation")
app_mode = st.sidebar.radio("Go to", ["Bike Distribution", "Broken Docks"])
if app_mode == "Broken Docks":
broken_docks_page()
elif app_mode == "Bike Distribution":
distribution_page()
if __name__ == "__main__":
main()