import dash
import numpy
import pandas
from dash import html, dash, Output, Input, dcc
from dash.exceptions import PreventUpdate
from Project_data_processor_ML import get_genre_plot
import dash_plot_generation.data_store as ds
from dash_plot_generation.styles_and_handles import RATING_MIN_REVIEWS, RATING_SLIDER, RATING_TABLE, \
DEV_AVERAGE_RATING_LABEL, DENSITY_LAYOUT_STYLE, WHITE_STEAM, TAB_COLOR, TAB_EDGE, \
TAB_HEADER_COLOR, DEVELOPER_DROPDOWN, DEV_TOP_GENRES_LABEL, DEV_CCU_LABEL, DEV_GAME_COUNT_LABEL, \
DEV_REV_PER_GAME_LABEL, DEV_REVENUE_LABEL, DEV_TOP_GAMES, RATING_TABS, RATING_TABS_OUTPUT_AREA, \
GENRE_PREDICTION_GRAPH, GENRE_DROPDOWN, DEFAULT_PLOT_STYLE_DICT, GAMES_BY_DEV_GRAPH, MARKET_PERFORMANCE_SCATTER, \
MP_COMPANY_TYPE_DROPDOWN, create_market_scatter_plot_style, REVENUE_COMPANY_GAME_COUNT, PUB_REVENUE_LABEL, \
PUB_TOP_GENRES_LABEL, PUB_CCU_LABEL, PUB_GAME_COUNT_LABEL, PUB_REV_PER_GAME_LABEL, PUB_TOP_GAMES, \
PUB_AVERAGE_RATING_LABEL, PUBLISHER_DROPDOWN, GAMES_BY_PUB_GRAPH, TOP_COMPANY_TABLE_AREA, TOP_REVENUE_COMPANIES
from dash_plot_generation.utils import get_average_user_rating_label, get_game_count_label, get_top_revenue_game_labels, \
get_total_revenue_label, get_top_genre_labels, get_ccu_label, get_average_game_rev_label, get_ccu_str, \
get_top_revenue_game_names, convert_to_numeric_str
from visual_presentation.Annual_release_games import get_game_release_figure
from visual_presentation.Distribution_of_review_rating import get_rating_density_plot
from visual_presentation.Market_performance_function import plot_market_performance
APP = dash.Dash(
name=__name__,
use_pages=True,
external_stylesheets=['/assets/styles.css',
'https://codepen.io/chriddyp/pen/bWLwgP.css']
)
server = APP.server
APP.layout = html.Div([
html.Nav(className="navbar", children=[
html.A("SteamSavvy - Steam game data insights", href="/",
style={"margin-left": "60px", "display": "inline-block"},
className="nav-item-1"),
html.A('About', className="nav-item nav-link btn", href='/about',
style={"margin-left": "150px"}, ),
html.A('Dashboard', className="nav-item nav-link btn", href='/dashboard',
style={"margin-left": "150px"}),
html.A('Technical report', className="nav-item nav-link active btn",
href="", download='dark city.jpg', style={"margin-left": "150px"})
]),
dash.page_container
], className="body")
def update_company_info(filtered_dataframe: pandas.DataFrame):
# Top games
company_top_games_label = get_top_revenue_game_labels(filtered_dataframe)
# Dev total revenue
company_revenue = get_total_revenue_label(filtered_dataframe)
# Dev revenue per game
company_game_revenue_per_game = get_average_game_rev_label(filtered_dataframe)
# Top genres
company_top_genre_labels = get_top_genre_labels(filtered_dataframe)
# CCU
company_ccu = get_ccu_label(filtered_dataframe)
# Game count
company_game_count = get_game_count_label(filtered_dataframe)
user_rating_value = get_average_user_rating_label(filtered_dataframe)
return company_revenue, company_top_genre_labels, company_ccu, company_game_count, company_game_revenue_per_game, \
company_top_games_label, user_rating_value
@APP.callback([Output(DEV_REVENUE_LABEL, "children"),
Output(DEV_TOP_GENRES_LABEL, "children"),
Output(DEV_CCU_LABEL, "children"),
Output(DEV_GAME_COUNT_LABEL, "children"),
Output(DEV_REV_PER_GAME_LABEL, "children"),
Output(DEV_TOP_GAMES, "children"),
Output(DEV_AVERAGE_RATING_LABEL, "children")],
inputs=[Input(DEVELOPER_DROPDOWN, "value")])
def update_dev_info(dev_name):
if not (dev_name and isinstance(ds.FULL_DATA, pandas.DataFrame)):
raise PreventUpdate
# Remove empty rows
mask = ds.FULL_DATA.developer.apply(lambda x: dev_name in x if isinstance(x, str) else False)
dev_data = ds.FULL_DATA[mask]
return update_company_info(dev_data)
@APP.callback([Output(PUB_REVENUE_LABEL, "children"),
Output(PUB_TOP_GENRES_LABEL, "children"),
Output(PUB_CCU_LABEL, "children"),
Output(PUB_GAME_COUNT_LABEL, "children"),
Output(PUB_REV_PER_GAME_LABEL, "children"),
Output(PUB_TOP_GAMES, "children"),
Output(PUB_AVERAGE_RATING_LABEL, "children")],
inputs=[Input(PUBLISHER_DROPDOWN, "value")])
def update_pub_info(pub_name):
if not (pub_name and isinstance(ds.FULL_DATA, pandas.DataFrame)):
raise PreventUpdate
# Remove empty rows
mask = ds.FULL_DATA.publisher.apply(lambda x: pub_name in x if isinstance(x, str) else False)
pub_data = ds.FULL_DATA[mask]
return update_company_info(pub_data)
@APP.callback(Output(GAMES_BY_PUB_GRAPH, "figure"),
Input(PUBLISHER_DROPDOWN, "value"))
def get_games_by_pub_table(pub_name):
if not (pub_name and isinstance(ds.FULL_DATA, pandas.DataFrame)):
raise PreventUpdate
layout_arguments = DEFAULT_PLOT_STYLE_DICT | dict(margin=dict(l=20, r=20, t=50, b=20))
return get_game_release_figure(ds.FULL_DATA, pub_name, "publisher", **layout_arguments)
@APP.callback(Output(GAMES_BY_DEV_GRAPH, "figure"),
Input(DEVELOPER_DROPDOWN, "value"))
def get_games_by_dev_table(dev_name):
if not (dev_name and isinstance(ds.FULL_DATA, pandas.DataFrame)):
raise PreventUpdate
layout_arguments = DEFAULT_PLOT_STYLE_DICT | dict(margin=dict(l=20, r=20, t=50, b=20))
return get_game_release_figure(ds.FULL_DATA, dev_name, "developer", **layout_arguments)
@APP.callback(Output(RATING_TABS_OUTPUT_AREA, 'children'),
Input(RATING_SLIDER, "value"),
Input(RATING_MIN_REVIEWS, "value"),
Input(RATING_TABS, "value"))
def update_density_filter_plot(rating_range, min_reviews, active_tab):
allowed_indexes = [str_val for (val, str_val) in ds.OWNER_RANGE_PARTS_SORTED[rating_range[0]:rating_range[1] + 1]]
allowed_ratings = [" .. ".join([val, allowed_indexes[i + 1]]) for (i, val) in enumerate(allowed_indexes)
if i < len(allowed_indexes) - 1]
data = get_rating_density_plot(ds.FULL_DATA, allowed_ratings, min_reviews, layout=DENSITY_LAYOUT_STYLE)
table_data_key = None
output_table = False
output = None
match active_tab:
case "free":
table_data_key = "free"
output_table = True
case "plot":
output = html.Div(dcc.Graph(figure=data['fig']))
case "non-free":
table_data_key = "non_free"
output_table = True
case _:
raise KeyError("Invalid tab name")
if output_table:
output = html.Div(dash.dash_table.DataTable(data['top_games'][table_data_key].to_dict('records'),
id=RATING_TABLE,
style_data={'backgroundColor': TAB_COLOR,
'color': WHITE_STEAM,
'border': '1px solid ' + TAB_EDGE},
style_header={'backgroundColor': TAB_HEADER_COLOR,
'color': WHITE_STEAM,
'border': '1px solid ' + TAB_EDGE}))
return [output]
@APP.callback(Output(GENRE_PREDICTION_GRAPH, "figure"),
Input(GENRE_DROPDOWN, "value")
)
def get_genre_prediction_table(genre, **kwargs):
if "layout" not in kwargs.keys():
kwargs["layout"] = DEFAULT_PLOT_STYLE_DICT | dict(
title="Genre future prediction",
margin=dict(l=20, r=20,
t=50, b=20)
)
fig = get_genre_plot(ds.LABEL_ENCODED_DATASET, genre, **kwargs)
return fig
@APP.callback(Output(MARKET_PERFORMANCE_SCATTER, "figure"),
Input(MP_COMPANY_TYPE_DROPDOWN, "value"),
Input(REVENUE_COMPANY_GAME_COUNT, "value"))
def get_market_performance_scatter(company_type, company_game_onwer_range):
style = create_market_scatter_plot_style(company_type)
return plot_market_performance(df=ds.FULL_DATA, company_type=company_type,
game_number_min=company_game_onwer_range[0],
game_number_max=company_game_onwer_range[1], **style)
def top_revenue_company_infromation_for_company(data, company_name):
ccu_str = get_ccu_str(data)
game_count_str = data.shape[0]
top_games = get_top_revenue_game_names(data)
total_revenue = "".join(["$", convert_to_numeric_str(int(numpy.nansum(data["game_revenue"])))])
return {"Company": company_name, "Revenue": total_revenue, "Concurrent users": ccu_str,
"Number of games": game_count_str, "Top games": top_games}
def get_company_information_for_company_list(company_list, company_type):
company_information_list = []
for company, value in company_list.iterrows():
mask = ds.FULL_DATA[company_type].apply(lambda x: company in x if isinstance(x, str) else False)
company_data = ds.FULL_DATA[mask]
company_information = top_revenue_company_infromation_for_company(company_data, company)
company_information_list.append(company_information)
table_data = pandas.DataFrame(company_information_list)
return table_data
@APP.callback(Output(TOP_COMPANY_TABLE_AREA, 'children'),
Input(TOP_REVENUE_COMPANIES, "value"))
def get_top_companies_table(company_type, get_largest_to=50):
data = ds.FULL_DATA[[company_type, "game_revenue"]].groupby(company_type).sum()
top_n_companies = data.nlargest(get_largest_to, 'game_revenue')
data = get_company_information_for_company_list(top_n_companies, company_type)
output = html.Div(dash.dash_table.DataTable(data.to_dict('records'),
id=RATING_TABLE,
style_data={'backgroundColor': TAB_COLOR,
'color': WHITE_STEAM,
'border': '1px solid ' + TAB_EDGE},
style_header={'backgroundColor': TAB_HEADER_COLOR,
'color': WHITE_STEAM,
'border': '1px solid ' + TAB_EDGE}))
return [output]
def start_server():
APP.run_server(debug=True, host="0.0.0.0")
if __name__ == "__main__":
print("Here!")
ds.initialize_data()
start_server()