import streamlit as st
import geopandas as gpd
import folium
from streamlit_folium import folium_static
from dotenv import load_dotenv
from utils import get_provider_data
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from folium.features import CustomIcon

# Load environment variables
load_dotenv()

# Set page config for dark theme
st.set_page_config(
    page_title="Europeana Data Explorer",
    page_icon="🌍",
    layout="wide",
    initial_sidebar_state="expanded",
)

# Load CSS file
def load_css(file_name):
    with open(file_name) as f:
        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)

# Load the CSS
load_css("style.css")

# Ensure that the API key is set
if 'EUROPEANA_API_KEY' not in os.environ:
    st.error("EUROPEANA_API_KEY is not set in the environment variables. Please set it and restart the app.")
    st.stop()

# Load world map dataset
world = gpd.read_file("lowres_world.geojson")

# Set up the Streamlit app
st.title('🌍 Europeana Data Explorer')

# Sidebar
st.sidebar.header("About")
st.sidebar.info("This app allows you to explore data from Europeana providers. Enter a provider name to fetch and visualize their data across different countries.")

# Add Europeana logo below the "About" section
logo_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/0/02/Europeana_logo_2015_basic.svg/800px-Europeana_logo_2015_basic.svg.png"
st.sidebar.image(logo_url, width=50)

# Input for provider name
provider_name = st.text_input('Enter the name of the data provider:', help="e.g., 'Rijksmuseum'")

# Button to trigger data fetch
if st.button('🔍 Fetch Data'):
    if provider_name:
        # Show loading message
        with st.spinner('Fetching data...'):
            # Get the data
            df, world_counts = get_provider_data(provider_name, world)

        # Display the data
        st.subheader(f'📊 Data from {provider_name}')
        st.write(f'Number of items retrieved: {len(df):,}')

        # Display the first few rows
        st.subheader('🔎 Sample Data:')
        st.dataframe(df.head(), use_container_width=True)

        # Create and display the choropleth map
        with st.expander('Object Counts by Country'):
            choropleth_map = folium.Map(location=[20, 0], zoom_start=2, tiles="cartodbpositron")

            # Apply log transformation to object_count
            world_counts['log_count'] = np.log1p(world_counts['object_count'])

            # Use a custom colormap
            colormap = plt.colormaps['Blues']  # Access the 'Blues' colormap
            norm = mcolors.Normalize(
                vmin=world_counts['log_count'].min(),
                vmax=world_counts['log_count'].max())

            # Function to map log_count to a color using the colormap
            def get_color(log_count):
                return mcolors.to_hex(colormap(norm(log_count)))

            # Add the choropleth layer
            choropleth = folium.Choropleth(
                geo_data=world_counts,
                name='choropleth',
                data=world_counts,
                columns=['name', 'log_count'],
                key_on='feature.properties.name',
                fill_opacity=0.7,
                line_opacity=0.2,
                legend_name=None
            )

            # Remove the existing color map from the choropleth object
            for key in choropleth._children:
                if key.startswith('color_map'):
                    del (choropleth._children[key])

            choropleth.add_to(choropleth_map)

            # Manually set the colors for each feature based on log_count using the
            # custom function
            for feature in choropleth.geojson.data['features']:
                log_count = feature['properties']['log_count']
                feature['properties']['style'] = {
                    'fillColor': get_color(log_count)}

            # Add hover functionality
            def style_function(x): return {'fillColor': '#ffffff',
                                           'color': '#000000',
                                           'fillOpacity': 0.1,
                                           'weight': 0.1}

            def highlight_function(x): return {'fillColor': '#ffffff',
                                               'color': '#000000',
                                               'fillOpacity': 0.50,
                                               'weight': 0.1}
            info = folium.features.GeoJson(
                world_counts,
                style_function=style_function,
                control=False,
                highlight_function=highlight_function,
                tooltip=folium.features.GeoJsonTooltip(
                    fields=['name', 'object_count'],
                    aliases=['Country:', 'Number of Objects:'],
                    style=(
                        "background-color: #FFFFFF; color: #333333; font-family: arial; font-size: 12px; padding: 10px;")
                )
            )
            choropleth_map.add_child(info)
            choropleth_map.keep_in_front(info)

            folium_static(choropleth_map, width=500, height=500)

        # Sample the DataFrame to get 20 random entries
        sample_df = df.sample(n=min(20, len(df)), random_state=1)

        # Create and display the marker map
        with st.expander('Collection Objects on the Map'):
            marker_map = folium.Map(location=[20, 0], zoom_start=2, tiles="cartodbpositron")

            # Function to create a marker with an image
            def add_marker(row):
                image_url = row['edmPreview']  # URL to the image
                popup_html = f'<img src="{image_url}" width="100" height="100">'
                folium.Marker(
                    location=[row['edmPlaceLatitude'], row['edmPlaceLongitude']],
                    icon=CustomIcon(icon_image=image_url, icon_size=(50, 50)),
                    popup=folium.Popup(popup_html, max_width=250)
                ).add_to(marker_map)

            # Apply the function to each row in the sampled DataFrame
            sample_df.apply(add_marker, axis=1)

            folium_static(marker_map, width=500, height=500)

        # Option to download the full dataset
        csv = df.to_csv(index=False)
        st.download_button(
            label="📥 Download full dataset as CSV",
            data=csv,
            file_name=f"{provider_name}_data.csv",
            mime="text/csv",
        )
    else:
        st.warning('⚠️ Please enter a provider name.')

# Run this app with: streamlit run app.py