app.py

import streamlit as st
import folium
from streamlit_folium import folium_static
import pandas as pd

# Sample data extracted from the notebook's Leaflet map
data = {
    "grid_id": ["11897_2485", "11902_2482", "11904_2481", "11901_2483", "11902_2483"],
    "lat_min": [59.504766, 59.509923, 59.519881, 59.505209, 59.510654],
    "lon_min": [24.810962, 24.820996, 24.809146, 24.826864, 24.827830],
    "lat_max": [59.509766, 59.514923, 59.524881, 59.510209, 59.515654],
    "lon_max": [24.820962, 24.830996, 24.819146, 24.836864, 24.837830],
    "time": ["early_morning", "evening_rush", "evening_rush", "morning_rush", "early_morning"],
    "value": [2.46, 2.45, 2.44, 2.44, 2.44],
    "rides": [15, 21, 16, 16, 36],
    "color": ["blue", "red", "red", "green", "blue"]
}

# Convert to DataFrame
df = pd.DataFrame(data)

# Simple prediction function (simulating the model)
def get_predictions(time_period):
    # Filter data by selected time period
    filtered_df = df[df["time"] == time_period]
    return filtered_df

# Streamlit app
st.title("Ride Value Prediction App")
st.write("Select a time period to see predicted ride values on the map.")

# Time period selector
time_options = ["early_morning", "morning_rush", "evening_rush"]
selected_time = st.selectbox("Choose Time Period", time_options)

# Get predictions
predictions = get_predictions(selected_time)

# Create Folium map centered on Tallinn
m = folium.Map(location=[59.4370, 24.7535], zoom_start=12)

# Add rectangles to the map
for _, row in predictions.iterrows():
    folium.Rectangle(
        bounds=[[row["lat_min"], row["lon_min"]], [row["lat_max"], row["lon_max"]]],
        color=row["color"],
        fill=True,
        fill_opacity=0.4,
        popup=f"Grid: {row['grid_id']}<br>Time: {row['time']}<br>Value: €{row['value']}<br>Rides: {row['rides']}"
    ).add_to(m)

# Display the map
folium_static(m)

# Show raw predictions below the map
st.write("Predicted Ride Values:")
st.dataframe(predictions[["grid_id", "time", "value", "rides"]])