import streamlit as st
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import numpy_financial as npf
import pandas as pd
from streamlit_folium import folium_static
import leafmap.foliumap as leafmap
import folium
from shapely.geometry import Point, Polygon
import geopandas
import geopy
from geopy.geocoders import Nominatim
from geopy.extra.rate_limiter import RateLimiter
from scipy.spatial import cKDTree

#-----------------------------------------
# Set page settings
st.set_page_config(layout="wide")

#-----------------------------------------
# Sidebar
with st.sidebar:
  st.header('Welcome to the Airbnb Investment Tool!')
  nav = st.selectbox('Navigation', ['Heuristic Pricing', 
                                    'Investment Analysis'])

#-----------------------------------------
# Additional Functions

def p_title(title):
    st.markdown(f'<h3 style="text-align: left; color:#F63366; font-size:28px;">{title}</h3>', unsafe_allow_html=True)


# Function to return a GeoPandas DataFrame containing the listings
# that are within a specified radius from a specified lat, long.
def getNearbyListings(gdf_proj, input_long, input_lat, radius):
  # Build Tree
  airbnbCoords = np.array(list(gdf_proj.geometry.apply(lambda x: (x.x, x.y))))
  airbnbTree = cKDTree(airbnbCoords)

  # Convert lat-long to projected coords
  gdf_input = geopandas.GeoSeries.from_xy(x=[input_long], y=[input_lat], crs=4326)
  gdf_input_proj = gdf_input.to_crs(crs=32634)

  coords = np.array(list((gdf_input_proj.x[0], gdf_input_proj.y[0])))

  # Returns list of indices whose distance is <= radius
  neighbours_indices = airbnbTree.query_ball_point(coords, radius)
  gdf_neighbours_proj = gdf_proj.iloc[neighbours_indices, :]
  gdf_neighbours = gdf_neighbours_proj.to_crs(crs=4326)

  return gdf_neighbours

# Function to return IRR.
# Financial Modelling Tool.
def investment_tool(house_price, loan_amount, loan_period, percentage_loan_interest_annual, 
                    rental_charged_monthly, percentage_rental_tax, percentage_increase_in_rental_yearly, utilisation_rate, 
                    yearly_refurbishment_costs, percentage_increase_in_refurbishment_yearly, ending_value_of_house):
    
  #expected format of percentage parameters is whole number and not decimals i.e., 5 instead of 0.05
  #all non-% parameters are expected to be positive

  house_price = int(house_price)
  loan_amount = int(loan_amount)
  loan_period = int(loan_period)
  percentage_loan_interest_annual = int(percentage_loan_interest_annual)
  rental_charged_monthly = int(rental_charged_monthly)
  percentage_rental_tax = int(percentage_rental_tax)
  percentage_increase_in_rental_yearly = int(percentage_increase_in_rental_yearly)
  percentage_utilisation_rate = int(utilisation_rate)
  yearly_refurbishment_costs = int(yearly_refurbishment_costs)
  percentage_increase_in_refurbishment_yearly = int(percentage_increase_in_refurbishment_yearly)
  ending_value_of_house = int(ending_value_of_house)

  #ensuring the figures make sense
  if loan_amount > house_price:
    return print("Loan Amount cannot exceed House Price")

  #creating the list of cash flows to be used to calculate internal rate of return
  initial_cashflow = -(1 - loan_amount/house_price) * house_price
  cashflow_list = [initial_cashflow]

  #finding the annual mortgage assuming equal amortization
  mortgage = npf.pmt(percentage_loan_interest_annual / 100, loan_period,  loan_amount) #the np.pmt function will automatically put mortgage as a negative cashflow

  #finding the annual cashflows & loan balance changes during the loan period and appending them to the respective lists
  for i in range(loan_period):
    rental = 12 * rental_charged_monthly * ((1 + (percentage_increase_in_rental_yearly / 100)) ** i) * (1 - (percentage_rental_tax / 100)) * utilisation_rate / 100
    refurbishment_cost = -1 * yearly_refurbishment_costs * ((1 + (percentage_increase_in_refurbishment_yearly / 100)) ** i)
    
    #the condition here is to include the salvage/ending value of the house to cashflows after loan repayments are finished
    if i == (loan_period-1):
      yearly_cashflow = ending_value_of_house + rental + mortgage + refurbishment_cost
    else:
      yearly_cashflow = rental + mortgage + refurbishment_cost
    cashflow_list.append(yearly_cashflow)
  
  #finding the internal rate of return
  irr = round(npf.irr(cashflow_list), 4)

  #-----------------------------------
  #Dataframe for plotting of graph
  loan_dict = {'Year': [0], 'Starting Loan Balance': [0], 'Cumulative Interest Paid': [0], 'Cumulative Principal Paid': [0], 'Remaining Loan Balance': [0]}
    
  # Create DataFrame  
  loan_dataframe = pd.DataFrame(loan_dict)
    
  #finding the annual mortgage assuming equal amortization
  mortgage = npf.pmt(percentage_loan_interest_annual / 100, loan_period,  loan_amount) #the np.pmt function will automatically put mortgage as a negative cashflow

  #updating the global dataframe
  loan_dataframe.loc[0,'Starting Loan Balance'] = loan_amount

  for i in range(loan_period):
    loan_dataframe.loc[i,'Year'] = i+1

    #the condition here is to calculate principal and interest paid
    if i == 0:
      loan_dataframe.loc[i,'Cumulative Interest Paid'] = loan_dataframe.loc[i,'Starting Loan Balance'] * (percentage_loan_interest_annual / 100)
      loan_dataframe.loc[i,'Cumulative Principal Paid'] = (-1 * mortgage) - (loan_dataframe.loc[i,'Starting Loan Balance'] * (percentage_loan_interest_annual / 100))
    else:
      loan_dataframe.loc[i,'Cumulative Interest Paid'] = loan_dataframe.loc[i,'Starting Loan Balance'] * (percentage_loan_interest_annual / 100) + loan_dataframe.loc[i-1,'Cumulative Interest Paid']
      loan_dataframe.loc[i,'Cumulative Principal Paid'] = (-1 * mortgage) - (loan_dataframe.loc[i,'Starting Loan Balance'] * (percentage_loan_interest_annual / 100)) + loan_dataframe.loc[i-1,'Cumulative Principal Paid']
          
    loan_dataframe.loc[i,'Remaining Loan Balance'] = loan_dataframe.loc[i,'Starting Loan Balance'] + (loan_dataframe.loc[i,'Starting Loan Balance'] * (percentage_loan_interest_annual / 100)) + mortgage
      
      #condition to update starting loan balance
    if i != loan_period-1:
      loan_dataframe.loc[i+1,'Starting Loan Balance'] = loan_dataframe.loc[i,'Remaining Loan Balance']

  loan_dataframe['Remaining Loan Balance'] = pd.to_numeric(loan_dataframe['Remaining Loan Balance'])
 
  return irr, loan_dataframe

#-----------------------------------------
# Load Airbnb listings data
df_raw = pd.read_csv("data/listings_sf_withamenities.csv")
df = df_raw.copy()
gdf = geopandas.GeoDataFrame(
    df, 
    geometry=geopandas.points_from_xy(df.longitude, df.latitude),
    crs=4326)
gdf_proj = gdf.to_crs(crs=32634)

#-----------------------------------------
# Tab 1: Heuristic Pricing
if nav == 'Heuristic Pricing':
    
    st.markdown("<h3 style='text-align: center; color:grey;'>Airbnb &#127968;</h3>", unsafe_allow_html=True)
    st.text('')
    p_title('Heuristic Pricing')
    st.text('')

    # Get address inputs
    st.caption('Enter your address:')
    with st.form("heuristics_form"):
      col1, col2 = st.columns(2)
      with col1:
        postalcode = st.text_input("Postal Code", "94109")
        street = st.text_input("Street", "1788 Clay Street")
        city = st.selectbox("City", ["San Francisco"])
      with col2:
        state = st.selectbox("State", ["California"])
        country = st.selectbox("Country", ["United States"])
        radius = st.slider("Distance of nearest listings (metres)", min_value=500, max_value=2000, value=500, step=500)
      submitted = st.form_submit_button("Submit")

      if submitted:
        # Get geolocation
        geolocator = Nominatim(user_agent="GTA Lookup")
        geocode = RateLimiter(geolocator.geocode, min_delay_seconds=1)
        location = geolocator.geocode({"postalcode": postalcode, "street": street, "city": city, "state": state, "country": country})

        # If the search address yields no result, set to default coords of San Fran
        if location is None:
          lat = 37.773972
          lon = -122.431297
          st.error("Address is not found. Please try again.")
        else:
          lat = location.latitude
          lon = location.longitude

          # Compute Stats
          st.markdown('___')
          st.caption('Recommended Pricing:')
          gdf_nearby_listings = getNearbyListings(gdf_proj, lon, lat, radius=radius)
          if len(gdf_nearby_listings) == 0:
            st.error("There are no nearby listings.")
          else:
            col3, col4 = st.columns(2)
            with col3:
              df_nearby_stats = gdf_nearby_listings[["price"]].describe().round(2)
              df_nearby_stats.columns = pd.Index(["Price"])
              st.table(df_nearby_stats.style.format("{:.2f}"))
            with col4:
              # Plot Stats
              fig = plt.figure(figsize=(10, 4))
              sns.boxplot(x="price", data=gdf_nearby_listings, showfliers=False)
              st.pyplot(fig)

            # Plot using leafmap. Responsive width.
            m = leafmap.Map(tiles="OpenStreetMap", location=[lat, lon], zoom_start=15)
            m.add_marker(location=[lat, lon])
            m.add_points_from_xy(gdf_nearby_listings, x="longitude", y="latitude", 
                     popup=["id", "price", "review_scores_rating"],
                     color_options=['red'])
            m.add_heatmap(data=gdf_nearby_listings, 
                          latitude="latitude", longitude="longitude", 
                          value="price", min_opacity=0.1,
                          name="Price Heatmap", blue=50)
            m.to_streamlit()

#-----------------------------------------
# Tab 2: Investment Analysis
if nav == 'Investment Analysis':
    st.markdown("<h3 style='text-align: center; color:grey;'>Airbnb &#127968;</h3>", unsafe_allow_html=True)
    st.text('')
    p_title('Investment Analysis')
    
    # Financial Projections
    st.caption("Enter data here")
    with st.form("investment_form"):
      col1_2, col2_2, col3_2 = st.columns(3)
      with col1_2:
        house_price = st.number_input("Purchase Price of House ($)", min_value=0, value=250000)
        loan_amount = st.number_input("Loan Amount ($)", min_value=0, value=150000)
        loan_period = st.number_input("Loan Period (Years)", min_value=0, value=15)
        percentage_loan_interest_annual = st.number_input("Annual Loan I/R (%)", min_value=0.0, max_value=100.0, value=2.1)
      with col2_2:
        rental_charged_monthly = st.number_input("Monthly Rental ($)", min_value=0, value=2000)
        percentage_rental_tax = st.number_input("Rental Tax (%)", min_value=0.0, value=0.0)
        percentage_increase_in_rental_yearly = st.number_input("Annual Rental Increase (%)", min_value=0.0, value=1.0)
        utilisation_rate = st.number_input("Utilisation Rate (%)", min_value=0.0, value=50.0)
      with col3_2:
        yearly_refurbishment_costs = st.number_input("Yearly Refurbishment Costs ($)", min_value=0, value=3000)
        percentage_increase_in_refurbishment_yearly = st.number_input("Yearly Refurbishment Costs Increase (%)", min_value=0.0, value=2.0)
        ending_value_of_house = st.number_input("Ending Value of House ($)", min_value=0, value=300000)
      submitted2 = st.form_submit_button("Submit")

      if submitted2:
        irr, loan_dataframe = investment_tool(house_price, loan_amount, loan_period, percentage_loan_interest_annual, 
                rental_charged_monthly, percentage_rental_tax, percentage_increase_in_rental_yearly, utilisation_rate, 
                yearly_refurbishment_costs, percentage_increase_in_refurbishment_yearly, ending_value_of_house)
        st.markdown('___')
        st.caption("Expected Internal Rate of Return")
        st.text("{:.2%}".format(irr))
        # Print plots
        fig = plt.figure(figsize=(10, 4))
        plt.bar(loan_dataframe['Year'], loan_dataframe['Cumulative Principal Paid'], color='lightcoral')
        plt.bar(loan_dataframe['Year'], loan_dataframe['Cumulative Interest Paid'], bottom=loan_dataframe['Cumulative Principal Paid'], color='lightsalmon')
        plt.plot(loan_dataframe['Year'], loan_dataframe['Remaining Loan Balance'], color='crimson')
        plt.ylabel('Amount')
        plt.title('Loan Balance')
        plt.legend(('Loan Balance Remaining','Cumulative Principal Paid', 'Cumulative Interest Paid'))
        st.pyplot(fig)