import streamlit as st 
import streamlit.components.v1 as components

import pandas as pd
import numpy as np

import utils # personal library utils.py file
import gmplot

import os
from dotenv import load_dotenv

# Load the .env file
load_dotenv()

# Get the Hugging Face token from the environment variables
google_map_token = os.getenv('GOOGLE_MAP_API_KEY')


url_dict = {"Incidents": "http://datamall2.mytransport.sg/ltaodataservice/TrafficIncidents",
            "SpeedBands": "http://datamall2.mytransport.sg/ltaodataservice/v3/TrafficSpeedBands"}


# st.balloons()
st.title("LTA Singapore Traffic Watch")

st.write("Welcome to the app for live traffic reporting, where you may find relevant road conditions ✨ " 
         "This app reports any road incidents, roadworks in Singapore at a given time from "
         "Singapore's Land Transport Authority and also points out location and detailed information about the incident. ")

st.write("The table below displays the traffic report: ")

incidents_df, speedbands_df = utils.get_lta_data(url_dict)

# Add the 'Issue' column to the front of the DataFrame
incidents_df.insert(0, 'Issue', True)

new_df = st.data_editor(
    incidents_df,
    column_config = {
        "Issue":st.column_config.CheckboxColumn(
            "Issue or Non-issue?",
            default = True
        )
    }
)

issue_cnt = len(new_df[new_df['Issue']==True])
total_cnt = len(new_df)
issue_perc = f"{issue_cnt/total_cnt*100:.0f}%"

col1, col2 = st.columns([1,1])

with col1:
    st.metric("Number of issues",issue_cnt)
with col2:
    st.metric("Percent of reported incidents", issue_perc)


st.markdown("Real-Time Accident or Roadworks Monitoring using Streamlit Map")

#plotting a map with the above defined points
new_df = new_df.rename(columns={"Latitude": "latitude", "Longitude": "longitude"})

st.map(new_df[new_df['Issue']==True][['latitude', 'longitude']])

st.divider()

st.write("*First*, I created some filters to slice and dice the traffic report")

col1, col2 = st.columns([1,1])
with col1:
    issue_filter = st.selectbox("Issues or Non-issues", options = new_df.Issue.unique())
with col2:
    category_filter = st.selectbox("Choose a category", options  = new_df[new_df["Issue"]==issue_filter].Type.unique())

st.dataframe(new_df[(new_df['Issue'] == issue_filter) & (new_df['Type'] == category_filter)])

st.write("*Next*, we can visualize our data quickly using `st.metrics` and `st.bar_plot`")

df_plot = new_df[new_df['Type']!=''].Type.value_counts().reset_index()

st.bar_chart(df_plot, x = 'Type', y = 'count')

st.divider()

# code for Google map plotting

# Indicate traffic disruption as 1 else 0
minor_disruption_list = ["Roadwork"]
incidents_df['Color_Indicator'] = incidents_df['Type'].apply(lambda x: 0 if x in minor_disruption_list else 1)

# Pack the coordinates for polygon
speedbands_df['polygon'] = speedbands_df.apply(utils.zip_columns, axis=1)

# Create the map plotter:
center_lat = incidents_df['Latitude'].iloc[incidents_df.shape[0] // 2]
center_lng = incidents_df['Longitude'].iloc[incidents_df.shape[0] // 2]
zoom = 5

x = incidents_df['Longitude'].to_numpy()
y = incidents_df['Latitude'].to_numpy()
z = incidents_df['Color_Indicator'].to_numpy()

polygons = speedbands_df['polygon']
speeds = speedbands_df['SpeedBand']

colors = list(map(utils.get_color_from_value, z))


# gmap = gmplot.GoogleMapPlotter(center_lat, center_lng, zoom, apikey=google_map_token, map_type='roadmap')
# gmap.scatter(y, x, color=colors, size=2, marker=True)

# utils.plot_polygon_with_speed(polygons, speeds, gmap, 'map2.html')

# ---------------------------------------------------------------------------------

st.markdown("Embedded Google Maps HTML from web-hosting site")

st.write("This part shows the Google Map where the incidents and speedbands are plotted. "
        "Unfortunately, I am unable to embed my Google Maps HTML file on Streamlit."
        "Therefore, I have used a web-hosting site to host my html file and then display it here "
        "for the assignment. I have indicated red as an accident and yellow of all other incident "
        "types. For speedbands, blue indicates slow traffic speed whereas magenta indicates "
        "fast. Both incidents and speedbands are plotted on the same map to give users information "
        "on traffic conditions and incidents happening on the road, and if there is connection between them.")

# Read the HTML file
map_url = "https://lta-sg-archived-traffic-monitoring.tiiny.site"  # Adjust the path if necessary

# HTML content with iframe to embed the Google Map
iframe_html = f"""
<!DOCTYPE html>
<html>
<head>
    <title>Embedded Google Map</title>
</head>
<body>
    <iframe src="{map_url}" width="100%" height="800" style="border:none;"></iframe>
</body>
</html>
"""

# Embed the iframe in the Streamlit app
components.html(iframe_html, height=800)

st.write("Here we are at the end of the traffic report, powered by Streamlit! :balloon:")