# app.py

# This application provides a user interface for HKUST students to browse, search,
# and find accommodations in different neighborhoods of Hong Kong. It features an interactive map
# visualization, listing cards with pricing information, traffic-based discounts, and smart search
# functionality to match user preferences with available properties.

# Key features:
# - Interactive map displaying BNB listings with location markers
# - Neighborhood-based filtering of available accommodations
# - Smart search system that highlights matching terms in descriptions and reviews
# - Traffic-based discount system promoting eco-friendly housing options
# - Detailed view of property reviews with highlighted search terms
# - Responsive pagination for browsing through large sets of listings
# - Loading animations and informative UI elements for better user experience

# The application uses Folium for map visualization, Streamlit for the web interface

# Author: Gordon Li (20317033)
# Company : HKUST Sustainability
# Date: March 2025

import os
import re
import streamlit as st
import streamlit.components.v1 as components
from html import escape
from streamlit_folium import st_folium
import math
from visualiser.hkust_bnb_visualiser import HKUSTBNBVisualiser
from huggingface_hub import login
from constant.hkust_bnb_constant import (
    SIDEBAR_HEADER,
    SIDEBAR_DIVIDER,
    TRAFFIC_EXPLANATION,
    SEARCH_EXPLANATION,
    REVIEW_CARD_TEMPLATE,
    LISTINGS_COUNT_INFO,
    LISTING_CARD_TEMPLATE,
    PRICE_DISPLAY_WITH_DISCOUNT,
    PRICE_DISPLAY_NORMAL,
    RELEVANCE_INFO_LISTING,
    LOTTIE_HTML
)



# Loads CSS styles from a file and applies them to the Streamlit application.
# Parameters:
#    css_file: Path to the CSS file to be loaded


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


# Highlights search terms within text by wrapping them in a span with highlight class.
# Parameters:
#     text: The original text to process
#     search_query: The search terms to highlight within the text
# Returns:
#     Text with highlighted search terms

def highlight_search_terms(text, search_query):
    if not search_query:
        return text

    highlighted_text = text
    search_terms = search_query.lower().split()

    for term in search_terms:
        if term.strip():
            pattern = f'(?i)\\b{term}\\b'
            replacement = f'<span class="highlight">{term}</span>'
            highlighted_text = re.sub(pattern, replacement, highlighted_text)

    return highlighted_text


# Renders a loading animation using Lottie animation in HTML format.

def render_lottie_loading_animation():
    components.html(LOTTIE_HTML, height=750)


# Renders a dialog containing reviews for the currently selected listing.
# Displays reviewer name, review date, and comments with search terms highlighted.

def render_review_dialog():
    with st.container():
        col_title = st.columns([5, 1])
        with col_title[0]:
            st.markdown(f"### Reviews for {st.session_state.current_review_listing_name}")

        reviews = st.session_state.visualizer.get_listing_reviews(st.session_state.current_review_listing)
        if reviews:
            for review in reviews:
                try:
                    review_date, reviewer_name, comments = review

                    highlighted_comments = highlight_search_terms(
                        str(comments),
                        st.session_state.search_query
                    )

                    st.markdown(
                        REVIEW_CARD_TEMPLATE.format(
                            reviewer_name=escape(str(reviewer_name)),
                            review_date=escape(str(review_date)),
                            highlighted_comments=highlighted_comments
                        ),
                        unsafe_allow_html=True
                    )
                except Exception as e:
                    st.error(f"Error displaying review: {str(e)}")
        else:
            st.info("No reviews available for this listing.")


# Initializes the session state with default values for various application parameters.
# Sets up the visualizer and loads required resources for the application.

def initialize_session_state():
    default_states = {
        'center_lat': None,
        'center_lng': None,
        'selected_id': None,
        'current_page': 1,
        'previous_neighborhood': None,
        'items_per_page': 3,
        'search_query': "",
        'tokenizer_loaded': False,
        'show_review_dialog': False,
        'current_review_listing': None,
        'current_review_listing_name': None,
        'show_traffic_explanation': False,
        'show_search_explanation': False,
        'listings_limit': 10,
        'loading_complete': False,
    }

    for key, default_value in default_states.items():
        if key not in st.session_state:
            st.session_state[key] = default_value

    if 'visualizer' not in st.session_state:
        st.session_state.loading_complete = False
        st.session_state.visualizer = HKUSTBNBVisualiser()
        st.session_state.tokenizer_loaded = True
        st.session_state.loading_complete = True


# Main function that sets up the Streamlit application interface.
# Handles page configuration, sidebar setup, map rendering, listing display,
# pagination, and user interactions with the application elements.
def main():
    st.set_page_config(
        layout="wide",
        page_title="HKUST BNB+ | Platform for BNB Matching for HKUST PG Student",
        initial_sidebar_state="expanded"
    )
    load_css('css/style.css')

    if 'loading_complete' not in st.session_state or not st.session_state.loading_complete:
        render_lottie_loading_animation()
        initialize_session_state()
        st.rerun()

    visualizer = st.session_state.visualizer
    if visualizer is None or not hasattr(visualizer, 'neighborhoods'):
        st.error("Error initializing the application. Please refresh the page.")
        return

    if st.session_state.show_traffic_explanation:
        with st.expander("📊 Traffic-Based Discount System", expanded=True):
            st.markdown(TRAFFIC_EXPLANATION)
            if st.button("Close", key="close_traffic_btn"):
                st.session_state.show_traffic_explanation = False
                st.rerun()

    if st.session_state.show_search_explanation:
        with st.expander("🔍 Smart Search System", expanded=True):
            st.markdown(SEARCH_EXPLANATION)
            if st.button("Close", key="close_search_btn"):
                st.session_state.show_search_explanation = False
                st.rerun()

    with st.sidebar:
        st.image("css/img.png", width=50)
        st.markdown(SIDEBAR_HEADER, unsafe_allow_html=True)


        search_query = st.text_input(
            "🔍 Search listings",
            value=st.session_state.search_query,
            placeholder="Try: 'cozy , quiet '"
        )
        if search_query != st.session_state.search_query:
            st.session_state.search_query = search_query
            st.session_state.current_page = 1
            st.session_state.show_review_dialog = False

        st.markdown(SIDEBAR_DIVIDER, unsafe_allow_html=True)

        neighborhood = st.selectbox(
            "Select Neighborhood",
            options=visualizer.neighborhoods,
            index=visualizer.neighborhoods.index("Kowloon City") if "Kowloon City" in visualizer.neighborhoods else 0
        )

        listings_limit = st.selectbox(
            "Number of listings to show",
            options=[10, 20, 30, 40, 50],
            index=0,
            help="Select how many listings to display for this neighborhood"
        )
        if listings_limit != st.session_state.listings_limit:
            st.session_state.listings_limit = listings_limit
            st.session_state.current_page = 1
            st.session_state.show_review_dialog = False

        st.markdown(SIDEBAR_DIVIDER, unsafe_allow_html=True)

        st.markdown("### 💡 Help & Information")
        col1, col2 = st.columns(2)
        with col1:
            if st.button("Green Discount", key="traffic_info_btn"):
                st.session_state.show_traffic_explanation = True
                st.rerun()
        with col2:
            if st.button("Smart Search", key="search_info_btn"):
                st.session_state.show_search_explanation = True
                st.rerun()

        if st.button("Reset All", key="reset_btn"):
            for key in ['center_lat', 'center_lng', 'selected_id', 'search_query',
                        'show_review_dialog', 'show_traffic_explanation', 'show_search_explanation']:
                st.session_state[key] = None if key in ['center_lat', 'center_lng',
                                                        'selected_id'] else False if 'show_' in key else ""
            st.session_state.current_page = 1
            st.session_state.listings_limit = 10
            st.rerun()

    m, df = visualizer.create_map_and_data(
        neighborhood,
        True,
        st.session_state.center_lat,
        st.session_state.center_lng,
        st.session_state.selected_id,
        st.session_state.search_query,
        st.session_state.current_page,
        st.session_state.items_per_page,
        st.session_state.listings_limit
    )

    if st.session_state.previous_neighborhood != neighborhood:
        st.session_state.current_page = 1
        if not df.empty:
            st.session_state.selected_id = df.iloc[0]['id']
            st.session_state.center_lat = df.iloc[0]['latitude']
            st.session_state.center_lng = df.iloc[0]['longitude']
        st.session_state.previous_neighborhood = neighborhood
        st.session_state.show_review_dialog = False
        st.rerun()

    if m is None:
        st.error("No data available for the selected neighborhood")
        return

    col1, col2 = st.columns([7, 3])

    with col1:
        st.markdown('<div class="map-container">', unsafe_allow_html=True)
        st_folium(m, width=None, height=700)
        st.markdown('</div>', unsafe_allow_html=True)

    with col2:
        st.markdown(
            LISTINGS_COUNT_INFO.format(
                listings_limit=st.session_state.listings_limit,
                neighborhood=neighborhood
            ),
            unsafe_allow_html=True
        )

        total_items = len(df)
        total_pages = math.ceil(total_items / st.session_state.items_per_page)
        st.session_state.current_page = min(max(1, st.session_state.current_page), total_pages)
        start_idx = (st.session_state.current_page - 1) * st.session_state.items_per_page
        end_idx = min(start_idx + st.session_state.items_per_page, total_items)

        st.markdown('<div class="scrollable-container">', unsafe_allow_html=True)
        for idx in range(start_idx, end_idx):
            row = df.iloc[idx]
            background_color = "#E3F2FD" if st.session_state.selected_id == row['id'] else "white"

            discounted_price = row['price']
            discount_tag = ""
            listing_lat = row['latitude']
            listing_lng = row['longitude']
            nearest_spot, distance = visualizer.find_nearest_traffic_spot(listing_lat, listing_lng)

            if nearest_spot:
                discount_rate = nearest_spot.get_discount_rate()
                if discount_rate > 0:
                    discounted_price = row['price'] * (1 - discount_rate)
                    discount_percentage = int(discount_rate * 100)
                    discount_tag = f"""<span class="discount-tag">-{discount_percentage}%</span>"""

            if discount_tag:
                price_display = PRICE_DISPLAY_WITH_DISCOUNT.format(
                    original_price=row['price'],
                    discounted_price=discounted_price,
                    discount_tag=discount_tag
                )
            else:
                price_display = PRICE_DISPLAY_NORMAL.format(price=row['price'])

            relevance_info = ""
            if st.session_state.search_query and 'relevance_percentage' in row:
                relevance_info = RELEVANCE_INFO_LISTING.format(relevance_percentage=row['relevance_percentage'])

            st.markdown(
                LISTING_CARD_TEMPLATE.format(
                    background_color=background_color,
                    listing_name=escape(str(row['name'])),
                    price_display=price_display,
                    room_type=escape(str(row['room_type'])),
                    review_count=row['number_of_reviews'],
                    relevance_info=relevance_info
                ),
                unsafe_allow_html=True
            )

            col_details, col_reviews = st.columns(2)
            with col_details:
                if st.button("View Details", key=f"btn_{row['id']}"):
                    st.session_state.selected_id = row['id']
                    st.session_state.center_lat = row['latitude']
                    st.session_state.center_lng = row['longitude']
                    st.rerun()
            with col_reviews:
                if st.button("View Reviews", key=f"review_btn_{row['id']}"):
                    st.session_state.show_review_dialog = True
                    st.session_state.current_review_listing = row['id']
                    st.session_state.current_review_listing_name = row['name']
                    st.session_state.scroll_to_review = True
                    st.rerun()

        st.markdown('</div>', unsafe_allow_html=True)

        # Pagination controls
        col_prev, col_select, col_next = st.columns([1, 1, 1])

        with col_select:
            page_options = list(range(1, total_pages + 1))
            new_page = st.selectbox(
                "Go to page",
                options=page_options,
                index=st.session_state.current_page - 1,
                key="page_selector",
                label_visibility="collapsed"
            )
            if new_page != st.session_state.current_page:
                st.session_state.current_page = new_page
                new_start_idx = (new_page - 1) * st.session_state.items_per_page
                if not df.empty and new_start_idx < len(df):
                    st.session_state.selected_id = df.iloc[new_start_idx]['id']
                    st.session_state.center_lat = df.iloc[new_start_idx]['latitude']
                    st.session_state.center_lng = df.iloc[new_start_idx]['longitude']
                st.session_state.show_review_dialog = False
                st.rerun()

        with col_prev:
            if st.button("← Previous", disabled=st.session_state.current_page <= 1):
                st.session_state.current_page -= 1
                new_start_idx = (st.session_state.current_page - 1) * st.session_state.items_per_page
                if not df.empty:
                    st.session_state.selected_id = df.iloc[new_start_idx]['id']
                    st.session_state.center_lat = df.iloc[new_start_idx]['latitude']
                    st.session_state.center_lng = df.iloc[new_start_idx]['longitude']
                st.session_state.show_review_dialog = False
                st.rerun()

        with col_next:
            if st.button("Next →", disabled=st.session_state.current_page >= total_pages):
                st.session_state.current_page += 1
                new_start_idx = (st.session_state.current_page - 1) * st.session_state.items_per_page
                if not df.empty:
                    st.session_state.selected_id = df.iloc[new_start_idx]['id']
                    st.session_state.center_lat = df.iloc[new_start_idx]['latitude']
                    st.session_state.center_lng = df.iloc[new_start_idx]['longitude']
                st.session_state.show_review_dialog = False
                st.rerun()

    if st.session_state.show_review_dialog:
        render_review_dialog()


# Main entry point for the application. Authenticates with Hugging Face if a token is available,
# then calls the main function to start the application.
if __name__ == "__main__":
    token = os.environ.get("HF_TOKEN")
    if token:
        login(token=token)
    main()