Create app.py

app.py

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+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"]])