Initial commit

app.py

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+import streamlit as st
+import pandas as pd
+import random
+import plotly.express as px
+from geopy.distance import geodesic
+
+# 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)

requirements.txt

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+geopy
+joblib
+pandas
+plotly
+scikit-learn==1.0.2