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import faicons as fa |
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import plotly.express as px |
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from shinywidgets import render_plotly |
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from shiny import reactive, render |
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from shiny.express import input, ui |
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from shared import app_dir, tips |
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bill_rng = (min(tips.سن), max(tips.سن)) |
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ui.page_opts(title="تحلیل احساسات کاربران", fillable=True) |
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with ui.sidebar(open="desktop"): |
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ui.input_slider( |
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"سن", |
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"رنج سنی", |
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min=bill_rng[0], |
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max=bill_rng[1], |
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value=bill_rng, |
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pre="سال", |
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) |
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ui.input_checkbox_group( |
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"شبکه_اجتماعی", |
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"انتخاب شبکه اجتماعی", |
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tips["شبکه اجتماعی"].unique().tolist(), |
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selected=tips["شبکه اجتماعی"].unique().tolist(), |
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inline=True, |
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) |
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ui.input_action_button("reset", "بازنشانی فیلتر") |
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ICONS = { |
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"user": fa.icon_svg("user", "regular"), |
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"wallet": fa.icon_svg("wallet"), |
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"currency-dollar": fa.icon_svg("dollar-sign"), |
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"ellipsis": fa.icon_svg("ellipsis"), |
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} |
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with ui.layout_columns(fill=False): |
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with ui.value_box(showcase=ICONS["user"]): |
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"تعداد کاربران" |
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@render.express |
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def total_tippers(): |
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data = tips_data() |
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print(data.shape[0]) |
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with ui.value_box(showcase=ICONS["wallet"]): |
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"میانگین احساس" |
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@render.express |
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def average_tip(): |
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data = tips_data() |
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if data.shape[0] > 0: |
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print(data["احساس"].mean()) |
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with ui.value_box(showcase=ICONS["currency-dollar"]): |
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"میانگین سن" |
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@render.express |
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def average_bill(): |
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data = tips_data() |
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if data.shape[0] > 0: |
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print(f"{data['سن'].mean():.1f} سال") |
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with ui.layout_columns(col_widths=[6, 6, 12]): |
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with ui.card(full_screen=True): |
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ui.card_header("جدول داده‌ها") |
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@render.data_frame |
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def table(): |
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return tips_data() |
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@render_plotly |
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def scatterplot(): |
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data = tips_data() |
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if data.shape[0] == 0: |
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return {} |
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return px.scatter( |
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data, |
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x="سن", |
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y="احساس", |
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color="جنسیت", |
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trendline="lowess", |
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) |
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with ui.card(full_screen=True): |
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with ui.card_header(class_="d-flex justify-content-between align-items-center"): |
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"توزیع احساس بر اساس ویژگی‌ها" |
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with ui.popover(title="انتخاب ویژگی برای مقایسه"): |
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ICONS["ellipsis"] |
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ui.input_radio_buttons( |
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"tip_perc_y", |
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"تفکیک بر اساس:", |
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["جنسیت", "موضوع", "سطح تأثیر"], |
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selected="جنسیت", |
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inline=True, |
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) |
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@render_plotly |
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def tip_perc(): |
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from ridgeplot import ridgeplot |
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dat = tips_data() |
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if dat.shape[0] == 0: |
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return {} |
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dat["percent"] = dat["احساس"] |
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yvar = input.tip_perc_y() |
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uvals = dat[yvar].unique() |
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samples = [[dat["percent"][dat[yvar] == val]] for val in uvals] |
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plt = ridgeplot( |
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samples=samples, |
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labels=uvals, |
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bandwidth=0.01, |
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colorscale="viridis", |
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colormode="row-index", |
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) |
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plt.update_layout( |
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legend=dict( |
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orientation="h", yanchor="bottom", y=1.02, xanchor="center", x=0.5 |
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) |
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) |
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return plt |
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ui.include_css(app_dir / "styles.css") |
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@reactive.calc |
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def tips_data(): |
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رنج_سنی = input.سن() |
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شبکه‌ها = input.شبکه_اجتماعی() |
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idx1 = tips["سن"].between(رنج_سنی[0], رنج_سنی[1]) |
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idx2 = tips["شبکه اجتماعی"].isin(شبکه‌ها) |
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return tips[idx1 & idx2] |
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@reactive.effect |
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@reactive.event(input.reset) |
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def _(): |
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ui.update_slider("سن", value=bill_rng) |
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ui.update_checkbox_group("شبکه_اجتماعی", selected=tips["شبکه اجتماعی"].unique().tolist()) |
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