import dash
from dash import html, dcc, Output, Input, State
import pickle
import pandas as pd
import numpy as np
import re
import os

# Load preprocessed data
with open('processed_data.pkl', 'rb') as f:
    data = pickle.load(f)

df = data['df']
similarity_matrix = data['similarity_matrix']

# Clean genres (remove empty)
all_genres = sorted({genre for genres in df['genres'] for genre in genres if genre.strip() != ''})
genre_tabs = [{'label': genre, 'value': genre} for genre in all_genres]
genre_tabs.insert(0, {'label': 'Most Popular', 'value': '__popular__'})

# Helper for recommendations
def extract_series(name):
    return re.split(r'[:\-]', name)[0].strip().lower()

def get_recommendations(game_id):
    idx = df[df['id'] == game_id].index[0]
    sim_scores = list(enumerate(similarity_matrix[idx]))
    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)[1:]

    selected = []
    seen_series = set()

    for i, score in sim_scores:
        name = df.iloc[i]['name']
        series = extract_series(name)
        if series not in seen_series:
            selected.append({
                'id': int(df.iloc[i]['id']),
                'name': name,
                'image': df.iloc[i]['background_image'],
                'rating': df.iloc[i]['rating'],
                'platforms': df.iloc[i]['platforms'],
            })
            seen_series.add(series)
        if len(selected) >= 5:
            break

    return selected

app = dash.Dash(__name__)
server = app.server
app.title = "Game Recommender 🎮"

# Layout
app.layout = html.Div(className="app-background", children=[
    html.Div(className="layout-container", children=[

        html.Div(className="sidebar-panel", children=[
            html.H2("Discover Games", className="sidebar-title"),
            dcc.Tabs(
                id='genre-tabs',
                value='__popular__',
                vertical=True,
                children=[
                    dcc.Tab(label=tab['label'], value=tab['value'],
                            className="tab", selected_className="selected-tab")
                    for tab in genre_tabs
                ],
                className="tabs-container"
            )
        ]),

        html.Div(className="main-container", children=[
            html.H1("🎮 Game Recommender", className="fancy-title"),

            dcc.Dropdown(
                id='game-dropdown',
                options=[{'label': row['name'], 'value': row['id']} for _, row in df.iterrows()],
                placeholder="Search for a game",
                className="custom-dropdown"
            ),

            dcc.Store(id='mode-store', data='discovery'),
            dcc.Store(id='cards-data-store'),
            dcc.Store(id='recommendations-data-store'),

            html.Div(id='search-output'),
            html.Div(id='grid-display'),
        ])
    ])
])

# 1️⃣ Render grid and store grid card metadata
@app.callback(
    [Output('grid-display', 'children'),
     Output('cards-data-store', 'data')],
    [Input('genre-tabs', 'value'),
     Input('mode-store', 'data')]
)
def render_grid(selected_tab, mode):
    if mode != 'discovery':
        return "", dash.no_update

    if selected_tab == '__popular__':
        filtered = df.sort_values(by='rating', ascending=False).head(20)
    else:
        filtered = df[df['genres'].apply(lambda genres: selected_tab in genres)].sort_values(by='rating', ascending=False).head(20)

    cards_data = []
    grid_cards = []

    for idx, row in enumerate(filtered.itertuples()):
        card_info = {
            'id': row.id,
            'name': row.name,
            'image': row.background_image,
            'rating': row.rating
        }
        cards_data.append(card_info)

        grid_cards.append(
            html.Div([
                html.Img(src=row.background_image, className="rec-image"),
                html.Div([
                    html.H4(row.name, className="rec-title"),
                    html.P(f"{row.rating} ⭐", className="rec-rating")
                ], className="rec-info")
            ],
            className="rec-card",
            n_clicks=0,
            id={'type': 'grid-card', 'index': idx})
        )

    return [
        html.H3("Games", className="subtitle"),
        html.Div(grid_cards, className="recommendations-container")
    ], cards_data

# 2️⃣ Render search output and store recommendation metadata
@app.callback(
    [Output('search-output', 'children'),
     Output('recommendations-data-store', 'data')],
    [Input('game-dropdown', 'value'),
     Input('mode-store', 'data')]
)
def render_search(selected_id, mode):
    if mode != 'search' or selected_id is None:
        return "", dash.no_update

    row = df[df['id'] == selected_id].iloc[0]
    platforms_display = row['platforms'][:4]
    platforms_hidden = ', '.join(row['platforms'])

    main_game = html.Div([
        html.Div([
            html.Img(src=row['background_image'], className='main-image'),
            html.Div([
                html.H2(row['name'], className='main-title'),
                html.P(f"Rating: {row['rating']} ⭐"),
                html.P(f"Metacritic: {row['metacritic']} 🎯"),
                html.P(f"Genres: {', '.join(row['genres'])}"),
                html.P("Platforms:"),
                html.Div([
                    html.Span(', '.join(platforms_display), title=platforms_hidden, className="platform-text")
                ])
            ], className="main-details")
        ], className="main-card-inner")
    ], className="main-card")

    recs = get_recommendations(selected_id)
    rec_cards = []
    rec_data = []

    for idx, rec in enumerate(recs):
        rec_cards.append(
            html.Div([
                html.Img(src=rec['image'], className="rec-image"),
                html.Div([
                    html.H4(rec['name'], className="rec-title"),
                    html.P(f"{rec['rating']} ⭐", className="rec-rating")
                ], className="rec-info")
            ], className="rec-card",
               n_clicks=0,
               id={'type': 'rec-card', 'index': idx})
        )
        rec_data.append({'id': rec['id'], 'name': rec['name']})

    return html.Div([
        main_game,
        html.H3("You May Also Like:", className="subtitle"),
        html.Div(rec_cards, className="recommendations-container")
    ]), rec_data

# 3️⃣ Unified callback handling all clicks + dropdown + tabs
@app.callback(
    [Output('game-dropdown', 'value'),
     Output('mode-store', 'data')],
    [Input('game-dropdown', 'value'),
     Input('genre-tabs', 'value'),
     Input({'type': 'grid-card', 'index': dash.ALL}, 'n_clicks'),
     Input({'type': 'rec-card', 'index': dash.ALL}, 'n_clicks')],
    [State('cards-data-store', 'data'),
     State('recommendations-data-store', 'data')]
)
def handle_inputs(dropdown_value, tab_value, grid_clicks, rec_clicks, cards_data, rec_data):
    ctx = dash.callback_context

    if not ctx.triggered:
        raise dash.exceptions.PreventUpdate

    triggered = ctx.triggered[0]['prop_id']

    # Handle grid card click
    if "grid-card" in triggered:
        for i, clicks in enumerate(grid_clicks):
            if clicks and clicks > 0:
                clicked_game_id = cards_data[i]['id']
                return clicked_game_id, 'search'

    # Handle recommendation card click
    if "rec-card" in triggered:
        for i, clicks in enumerate(rec_clicks):
            if clicks and clicks > 0:
                clicked_game_id = rec_data[i]['id']
                return clicked_game_id, 'search'

    # Handle search dropdown
    if 'game-dropdown' in triggered and dropdown_value is not None:
        return dropdown_value, 'search'

    # Handle tab change
    if 'genre-tabs' in triggered:
        return dash.no_update, 'discovery'

    return dash.no_update, dash.no_update

if __name__ == '__main__':
    app.run(host="0.0.0.0", port=int(os.environ.get("PORT", 7860)), debug=False)