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address_risk_validation
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import numpy as np
import streamlit as st
import requests
import pydeck as pdk
import pandas as pd
import geopandas as gpd
import plotly.express as px
import folium
import webbrowser
from shapely.geometry import Point
from folium import plugins
from streamlit_folium import st_folium

def load_polygon(filepath):
    return gpd.read_file(filepath)
path='Z:/Shared/Axeria Shared/Pricing/Immopolis Pricing Review/DATA/'
# Polygon1 = load_polygon(path + 'risk_zones.shp')
# Polygon2 = load_polygon(path + 'Flooding/n_inondable_01_01for_s.shp')
# Polygon3 = load_polygon(path + 'ZUS/ZUS_FRM_BDA09_L93.shp')
#

# Initialize polygons if not already in session state
if 'polygons' not in st.session_state:
    st.session_state.polygons = {
        "Polygon1": load_polygon(path+'risk_zones.shp'),
        "Polygon2": load_polygon(path+'Flooding/n_inondable_01_01for_s.shp'),
        "Polygon3": load_polygon(path+'ZUS/ZUS_FRM_BDA09_L93.shp')
    }

if 'polygons'  in st.session_state:
    st.session_state.polygons["Polygon1"]['geometry'] = st.session_state.polygons["Polygon1"]['geometry'].to_crs(epsg=4326)
    st.session_state.polygons["Polygon2"]['geometry'] = st.session_state.polygons["Polygon2"]['geometry'].to_crs(epsg=4326)
    st.session_state.polygons["Polygon3"]['geometry'] = st.session_state.polygons["Polygon3"]['geometry'].to_crs(epsg=4326)

    #Polygon1['geometry'] = Polygon1['geometry'].to_crs(epsg=4326)
    #Polygon2=load_polygon(path+'Flooding/n_inondable_01_01for_s.shp')
    # Polygon1=load_polygon(path+'risk_zones.shp')
    # Polygon1['geometry'] = Polygon1['geometry'].to_crs(epsg=4326)
    # Polygon2=load_polygon(path+'Flooding/n_inondable_01_01for_s.shp')
    # Polygon3=load_polygon(path+'ZUS/ZUS_FRM_BDA09_L93.shp')

# # Function to plot an interactive histogram
# # fig = px.histogram(polygon_gdf['poverty'], nbins=20)
# # st.plotly_chart(fig)
# fig = px.ecdf(polygon_gdf['poverty'])
# fig.show()
# # # #28% --> 5% of squares
# # #
# # #
# fig = px.ecdf(polygon_gdf['densite'])
#  # #9000 --> 5% of squares
# fig.show()
# #
# #
# #
# # #Load geographical layers
#

# zus=gpd.read_file(path+'ZUS/ZUS_FRM_BDA09_L93.shp')
# polygon_gdf = gpd.read_file(path+'Geo_metropole/Filosofi2017_carreaux_nivNaturel_met.shp')
# polygon_gdf2 = gpd.read_file(path+'Filosofi2017_carreaux_1km_shp/Filosofi2017_carreaux_1km_met.shp')
# polygon_gdf2['densite']=polygon_gdf2['Ind']
# polygon_gdf2['poverty']=polygon_gdf2['Men_pauv']/polygon_gdf2['Men']
# polygon_gdf['tmaille']=pd.to_numeric(polygon_gdf['tmaille'])
# polygon_gdf['tmaillem2']=polygon_gdf['tmaille']**2
# polygon_gdf['densite']=1000000*polygon_gdf['Ind']/polygon_gdf['tmaillem2']
# polygon_gdf['poverty']=polygon_gdf['Men_pauv']/polygon_gdf['Men']


# risk_zones2=polygon_gdf2[polygon_gdf2.poverty>=0.30]
# risk_zones2=risk_zones2[risk_zones2.densite>=7000]
#risk_zones2.to_file(filename=path+'risk_zones2.shp', driver='ESRI Shapefile')

# risk_zones=gpd.read_file(filename=path+'risk_zones.shp')
# flooding = gpd.read_file(path+'Flooding/n_inondable_01_01for_s.shp')
#
# #
# #
# # #FLooding zones
#risk_zones=polygon_gdf[polygon_gdf.poverty>=0.28]
# risk_zones=risk_zones[risk_zones.densite>=7000]
#
# risk_zones.to_file(filename=path+'risk_zones.shp', driver='ESRI Shapefile')
#
# # #
# m =folium.Map(location = [48.885805,2.366191], zoom_start = 6)
# folium.GeoJson(Polygon2[Polygon2.index<1000],color='blue').add_to(m)
#folium.CircleMarker([48.885805, 2.366191],radius=1,color='red').add_to(m)
# folium.GeoJson(flooding[flo,color='yellow').add_to(m)
# #folium.GeoJson(risk_zones2,color='orange').add_to(m)
# folium.GeoJson(zus).add_to(m)
# #
# #
# # # m.save(path+"map2.html")
# # # webbrowser.open_new_tab(path+"map2.html")
# # #
# # #
# policies = pd.read_pickle(path+"DB_immoplus.pkl")
# geometry = [Point(xy) for xy in zip(policies['longitude'], policies['latitude'])]
# policies_geo = gpd.GeoDataFrame(policies, geometry=geometry,crs="EPSG:4326")
# #
# large_claims=policies_geo[policies_geo.Charge>20000]
# large_claims=large_claims.dropna(subset=['latitude'])
# # #
# for arr in large_claims["geometry"]:
#      lat=arr.y
#      lon=arr.x
#      folium.CircleMarker([lat, lon],radius=1,color='red').add_to(m)
# m.save(path+"map2.html")
# webbrowser.open_new_tab(path+"map2.html")
#
#
# sum(risk_zones['tmaille'])/sum(polygon_gdf['tmaille'])*100
# sum(risk_zones['Ind'])/sum(polygon_gdf['Ind'])*100

# #
# #
# #
# # flooding["zone_inond_freq"]=1
# # zus['flag_ZUS']=1
# # Function to get address suggestions from the Autocomplete API
def create_geodataframe(longitude, latitude):
    geometry = [Point(longitude, latitude)]
    gdf = gpd.GeoDataFrame(geometry=geometry, crs="EPSG:4326")
    return gdf

def get_address_suggestions(query):
    if not query:
        return []

    url = "https://api-adresse.data.gouv.fr/search/"
    params = {'q': query, 'autocomplete': 1, 'limit': 5}
    response = requests.get(url, params=params)

    if response.status_code == 200:
        data = response.json()
        suggestions = [{'label': feature['properties']['label'], 'coordinates': feature['geometry']['coordinates']}
                       for feature in data['features']]
        return suggestions
    else:
        return []


# Function to create a map
def create_map(latitude, longitude):
    map_data = pd.DataFrame({
        'lat': [latitude],
        'lon': [longitude]
    })

    st.pydeck_chart(pdk.Deck(
        map_style='mapbox://styles/mapbox/light-v9',
        initial_view_state=pdk.ViewState(
            latitude=latitude,
            longitude=longitude,
            zoom=11,
            pitch=50,
        ),
        layers=[
            pdk.Layer(
                'ScatterplotLayer',
                data=map_data,
                get_position='[lon, lat]',
                get_color='[200, 30, 0, 160]',
                get_radius=200,
            ),
        ],
    ))


# Streamlit app layout
def main():
    st.title("Immopolis Adress validation APP")

    # Session state to store the current suggestions
    if 'suggestions' not in st.session_state:
        st.session_state.suggestions = []

    # Text input for address with on_change callback
    query = st.text_input("Enter your address", "", key="query")

    # Update suggestions when query changes
    st.session_state.suggestions = get_address_suggestions(query)

    # Display autocomplete suggestions
    if query:
        selected_suggestion = st.selectbox("Did you mean:", [s['label'] for s in st.session_state.suggestions], index=0,
                                           key="selected_suggestion")
    else:
        selected_suggestion = ""


    if selected_suggestion:
        selected_data = next((item for item in st.session_state.suggestions if item['label'] == selected_suggestion),
                             None)
        if selected_data and 'coordinates' in selected_data:
            longitude, latitude = selected_data['coordinates']
            st.write(f"Latitude: {latitude}, Longitude: {longitude}")
            #m = folium.Map(location=[longitude, latitude], zoom_start=6)
            gdf = create_geodataframe(longitude, latitude)
            gdf['geometry'] = gdf['geometry'].to_crs(epsg=4326)
            st.write(gdf)
            #polygon_name1 = gdf.within(st.session_state.polygons["Polygon1"])
            #st.write(polygon_name1[polygon_name1.isna()])
            #polygon_name2 = gdf.within(st.session_state.polygons["Polygon2"])
            polygon_name1=gpd.sjoin(gdf, st.session_state.polygons["Polygon1"], how="left", predicate="within")['index_right']
            st.write(polygon_name1)
            polygon_name2=gpd.sjoin(gdf, st.session_state.polygons["Polygon2"], how="left", predicate="within")['index_right']
            st.write(polygon_name2)

            polygon_name3=gpd.sjoin(gdf, st.session_state.polygons["Polygon3"], how="left", predicate="within")['index_right']
            st.write(polygon_name3)

            #st.write(polygon_name2)
            #st.write(np.isnan(polygon_name2[0]))

            #st.write(polygon_name2)
            #latitude=48.885805
            # longitude=2.366191
            #polygon_name3 = gdf.within(st.session_state.polygons["Polygon3"])
#           folium.GeoJson(risk_zones).add_to(m)
            #gdf=policies_geo[1:1]
            #m =folium.Map(location = [longitude, latitude], zoom_start = 10)
            m=folium.Map([gdf['geometry'].y, gdf['geometry'].x],zoom_start = 15)
            folium.CircleMarker([gdf['geometry'].y, gdf['geometry'].x], radius=1, color='red').add_to(m)
            #folium.GeoJson(st.session_state.polygons["Polygon2"], color='blue').add_to(m)
            folium.GeoJson(st.session_state.polygons["Polygon3"], color='orange').add_to(m)

            if not np.isnan(polygon_name1[0]):
                folium.GeoJson(st.session_state.polygons["Polygon1"][st.session_state.polygons["Polygon1"].index==polygon_name1[0]],color='yellow').add_to(m)
            if not np.isnan(polygon_name2[0]):
                folium.GeoJson(st.session_state.polygons["Polygon2"][st.session_state.polygons["Polygon2"].index==polygon_name2[0]],color='blue').add_to(m)
            if not np.isnan(polygon_name3[0]):
                folium.GeoJson(st.session_state.polygons["Polygon3"][st.session_state.polygons["Polygon3"].index==polygon_name3[0]],color='orange').add_to(m)

            point = Point(longitude, latitude)
            st_folium(m, width=700, height=500)


            if not np.isnan(polygon_name1[0]):
                st.markdown(f"Address is High risk zone", unsafe_allow_html=True)

            if not np.isnan(polygon_name2[0]):
                st.markdown(f"Address is in flooding area", unsafe_allow_html=True)

            if not np.isnan(polygon_name3[0]):
                st.markdown(f"Address is in sensitive area", unsafe_allow_html=True)

            if np.isnan(polygon_name1[0]) and np.isnan(polygon_name2[0])  and np.isnan(polygon_name3[0]):
                st.markdown(f"Risk check OK", unsafe_allow_html=True)


# Run the app
if __name__ == "__main__":
    main()