Leaflet maps

app.py

@@ -1,100 +1,22 @@
+import geopandas as gpd
 import streamlit as st
 import pandas as pd
-import random
 import plotly.express as px
 from geopy.distance import geodesic
+import leafmap.foliumap as leafmap
 from PIL import Image
 
-# Define the DMO names
-dmo_names = ["DMO" + str(i) for i in range(1, 31)]
+m = leafmap.Map(center=(47.903671, 15.483612), zoom=5)
+cities = gpd.read_file("https://raw.githubusercontent.com/idris-maps/map-of-europe/master/data/cities.json")
 
-# Dictionary of large European cities with their coordinates
-cities = {
-    "London": (51.5074, -0.1278),
-    "Paris": (48.8566, 2.3522),
-    "Berlin": (52.5200, 13.4050),
-    "Madrid": (40.4168, -3.7038),
-    "Rome": (41.9028, 12.4964),
-    "Vienna": (48.2082, 16.3738),
-    "Amsterdam": (52.3667, 4.8945),
-    "Athens": (37.9838, 23.7275),
-    "Stockholm": (59.3293, 18.0686),
-    "Brussels": (50.8503, 4.3517),
-    "Helsinki": (60.1699, 24.9384),
-    "Warsaw": (52.2297, 21.0122),
-    "Copenhagen": (55.6761, 12.5683),
-    "Lisbon": (38.7223, -9.1393),
-    "Dublin": (53.3498, -6.2603),
-    "Budapest": (47.4979, 19.0402),
-    "Prague": (50.0755, 14.4378),
-    "Sofia": (42.6977, 23.3219),
-    "Bucharest": (44.4396, 26.0963),
-    "Luxembourg": (49.6116, 6.1319),
-    "Riga": (56.9496, 24.1052),
-    "Valletta": (35.8989, 14.5146),
-    "Vilnius": (54.6872, 25.2797),
-    "Bratislava": (48.1486, 17.1077),
-    "Ljubljana": (46.0569, 14.5058),
-    "Nicosia": (35.1856, 33.3823),
-    "Tallinn": (59.4370, 24.7536),
-    "Zagreb": (45.8150, 15.9819),
-    "Nicosia": (35.1856, 33.3823),
-    "Valletta": (35.8989, 14.5146)
-}
+selected_cities = st.multiselect(
+    'Select cities:',
+    ['Lisbon', 'London', 'Budapest', 'Rome', 'Madrid', 'Barcelona'], None)
 
-# Coordinates of beach and mountain areas
-beach_coordinates = (45.0, 10.0)  # Example beach coordinates
-mountain_coordinates = (48.0, 10.0)  # Example mountain coordinates
 
-# Function to determine the area based on proximity to beach or mountain
-def determine_area(latitude, longitude):
-    city_coordinates = (latitude, longitude)
-    beach_distance = geodesic(city_coordinates, beach_coordinates).km
-    mountain_distance = geodesic(city_coordinates, mountain_coordinates).km
-    
-    if beach_distance <= 200:
-        return "Beach"
-    elif mountain_distance <= 200:
-        return "Mountain"
-    else:
-        return "City"
-
-# Create an empty DataFrame
-df = pd.DataFrame(columns=["DMO", "City", "Latitude", "Longitude", "Area", "Environmental", "Cultural",
-                           "Community", "Socio-economic", "Infrastructure", "Stakeholder",
-                           "Visitor_education", "Monitoring"])
-
-# Populate the DataFrame
-for i in range(30):
-    dmo = dmo_names[i]
-    city = random.choice(list(cities.keys()))
-    latitude, longitude = cities[city]
-    area = determine_area(latitude, longitude)
-    environmental = round(random.uniform(0, 10), 2)
-    cultural = round(random.uniform(0, 10), 2)
-    community = round(random.uniform(0, 10), 2)
-    socio_economic = round(random.uniform(0, 10), 2)
-    infrastructure = round(random.uniform(0, 10), 2)
-    stakeholder = round(random.uniform(0, 10), 2)
-    visitor_education = round(random.uniform(0, 10), 2)
-    monitoring = round(random.uniform(0, 10), 2)
-    
-    df.loc[i] = [dmo, city, latitude, longitude, area, environmental, cultural, community,
-                 socio_economic, infrastructure, stakeholder, visitor_education, monitoring]
-
-df.loc[df['DMO'].isin(['DMO14', 'DMO19', 'DMO27', 'DMO15', 'DMO20', 'DMO21']), 'Area'] = 'Beach'
-df.loc[df['DMO'].isin(['DMO4', 'DMO23', 'DMO8']), 'Area'] = 'Mountain'
-
-fig = px.scatter_mapbox(df, lat="Latitude", lon="Longitude", hover_name="DMO", hover_data=["Environmental", "Cultural",
-                                                                                         "Community", "Socio-economic",
-                                                                                         "Infrastructure", "Stakeholder",
-                                                                                         "Visitor_education", "Monitoring"],
-                        color="Area", color_discrete_sequence=["blue", "green", "red"], zoom=3)
-
-fig.update_layout(mapbox_style="open-street-map")
-fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
-
-st.plotly_chart(fig, use_container_width=True)
+if selected_cities:
+    m.add_gdf(cities[cities['name'].isin(selected_cities)], layer_name="cities", fill_colors="red")
+    m.to_streamlit()
 
 img = Image.open('mastercard_logo.png')
 
@@ -107,4 +29,4 @@ with col2:
     st.write("")
 
 with col3:
-    st.image(img)
+    st.image(img)

app_paused.py

@@ -0,0 +1,110 @@
+import streamlit as st
+import pandas as pd
+import random
+import plotly.express as px
+from geopy.distance import geodesic
+from PIL import Image
+
+# Define the DMO names
+dmo_names = ["DMO" + str(i) for i in range(1, 31)]
+
+# Dictionary of large European cities with their coordinates
+cities = {
+    "London": (51.5074, -0.1278),
+    "Paris": (48.8566, 2.3522),
+    "Berlin": (52.5200, 13.4050),
+    "Madrid": (40.4168, -3.7038),
+    "Rome": (41.9028, 12.4964),
+    "Vienna": (48.2082, 16.3738),
+    "Amsterdam": (52.3667, 4.8945),
+    "Athens": (37.9838, 23.7275),
+    "Stockholm": (59.3293, 18.0686),
+    "Brussels": (50.8503, 4.3517),
+    "Helsinki": (60.1699, 24.9384),
+    "Warsaw": (52.2297, 21.0122),
+    "Copenhagen": (55.6761, 12.5683),
+    "Lisbon": (38.7223, -9.1393),
+    "Dublin": (53.3498, -6.2603),
+    "Budapest": (47.4979, 19.0402),
+    "Prague": (50.0755, 14.4378),
+    "Sofia": (42.6977, 23.3219),
+    "Bucharest": (44.4396, 26.0963),
+    "Luxembourg": (49.6116, 6.1319),
+    "Riga": (56.9496, 24.1052),
+    "Valletta": (35.8989, 14.5146),
+    "Vilnius": (54.6872, 25.2797),
+    "Bratislava": (48.1486, 17.1077),
+    "Ljubljana": (46.0569, 14.5058),
+    "Nicosia": (35.1856, 33.3823),
+    "Tallinn": (59.4370, 24.7536),
+    "Zagreb": (45.8150, 15.9819),
+    "Nicosia": (35.1856, 33.3823),
+    "Valletta": (35.8989, 14.5146)
+}
+
+# Coordinates of beach and mountain areas
+beach_coordinates = (45.0, 10.0)  # Example beach coordinates
+mountain_coordinates = (48.0, 10.0)  # Example mountain coordinates
+
+# Function to determine the area based on proximity to beach or mountain
+def determine_area(latitude, longitude):
+    city_coordinates = (latitude, longitude)
+    beach_distance = geodesic(city_coordinates, beach_coordinates).km
+    mountain_distance = geodesic(city_coordinates, mountain_coordinates).km
+    
+    if beach_distance <= 200:
+        return "Beach"
+    elif mountain_distance <= 200:
+        return "Mountain"
+    else:
+        return "City"
+
+# Create an empty DataFrame
+df = pd.DataFrame(columns=["DMO", "City", "Latitude", "Longitude", "Area", "Environmental", "Cultural",
+                           "Community", "Socio-economic", "Infrastructure", "Stakeholder",
+                           "Visitor_education", "Monitoring"])
+
+# Populate the DataFrame
+for i in range(30):
+    dmo = dmo_names[i]
+    city = random.choice(list(cities.keys()))
+    latitude, longitude = cities[city]
+    area = determine_area(latitude, longitude)
+    environmental = round(random.uniform(0, 10), 2)
+    cultural = round(random.uniform(0, 10), 2)
+    community = round(random.uniform(0, 10), 2)
+    socio_economic = round(random.uniform(0, 10), 2)
+    infrastructure = round(random.uniform(0, 10), 2)
+    stakeholder = round(random.uniform(0, 10), 2)
+    visitor_education = round(random.uniform(0, 10), 2)
+    monitoring = round(random.uniform(0, 10), 2)
+    
+    df.loc[i] = [dmo, city, latitude, longitude, area, environmental, cultural, community,
+                 socio_economic, infrastructure, stakeholder, visitor_education, monitoring]
+
+df.loc[df['DMO'].isin(['DMO14', 'DMO19', 'DMO27', 'DMO15', 'DMO20', 'DMO21']), 'Area'] = 'Beach'
+df.loc[df['DMO'].isin(['DMO4', 'DMO23', 'DMO8']), 'Area'] = 'Mountain'
+
+fig = px.scatter_mapbox(df, lat="Latitude", lon="Longitude", hover_name="DMO", hover_data=["Environmental", "Cultural",
+                                                                                         "Community", "Socio-economic",
+                                                                                         "Infrastructure", "Stakeholder",
+                                                                                         "Visitor_education", "Monitoring"],
+                        color="Area", color_discrete_sequence=["blue", "green", "red"], zoom=3)
+
+fig.update_layout(mapbox_style="open-street-map")
+fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
+
+st.plotly_chart(fig, use_container_width=True)
+
+img = Image.open('mastercard_logo.png')
+
+col1, col2, col3 = st.columns([1,6,1])
+
+with col1:
+    st.write("")
+
+with col2:
+    st.write("")
+
+with col3:
+    st.image(img)

requirements.txt

@@ -1,5 +1,6 @@
 geopy
 joblib
+leafmap
 pandas
 plotly
 scikit-learn==1.0.2