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)