Team tab: real KPI row matching Overview's style, repost/original post markers
Replaces the ad-hoc stat cards with the same kpi() component the Overview tab uses (Starting/Total/New Followers, Total Impressions, Members Reached, Best Post Real Eng., Avg. Eng. Rate) - same visual language across the dashboard, and "New Followers" in the window is the growth KPI that was missing. The daily Impressions/Engagements chart now overlays star/diamond markers on each post's publish day, split by Original vs Repost (mirroring the Page's own mark_posts() star overlay) - the one thing this export doesn't give you anywhere else as a labeled field. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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@@ -2682,20 +2682,60 @@ def _switch_to_insights_page(pathname, selected_month):
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return classes + [content, hero_title, _hero_subtitle_children(active), _hero_actions(active, month_value)]
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def _team_stat_tile(value, label):
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return dbc.Col(dbc.Card(dbc.CardBody([
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html.Div(str(value), style={"fontSize": "1.8rem", "fontWeight": "800", "color": BLUE}),
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html.Div(label, style={"fontSize": "0.78rem", "color": "#6b7a8d"}),
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]), className="shadow-sm text-center"), md=3)
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def _team_kpi_row(tiles):
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"""Same flex-row-of-kpi()-cards pattern as the Overview tab's own KPI row
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(Starting/Total/New Followers, Total Impressions, Best Post Real Eng., Avg.
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Eng. Rate) - same component, same visual language, just personal-profile
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metrics instead of Page-level ones."""
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return html.Div([html.Div(t, style={"flex": "1", "minWidth": "0"}) for t in tiles],
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style={"display": "flex", "gap": "0.75rem", "marginBottom": "1rem"})
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def _team_engagement_figure(engagement_df):
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def _team_engagement_rate_stats(top_posts_df):
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"""(best_pct, avg_pct, post_count) - engagement rate per post is Engagements /
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Impressions, the same definition as the Page's own Avg. Eng. Rate tile. None for
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either figure if there's no post with nonzero impressions to compute a rate from."""
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if top_posts_df.empty:
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return None, None, 0
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rated = top_posts_df[top_posts_df["Impressions"] > 0].copy()
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if rated.empty:
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return None, None, 0
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rated["rate"] = rated["Engagements"] / rated["Impressions"] * 100
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return rated["rate"].max(), rated["rate"].mean(), len(rated)
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def _team_mark_posts(fig, engagement_df, top_posts_df):
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"""Same star-marker-on-publish-day idea as the Page's own mark_posts(), split into
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two traces (Original vs Repost) since that distinction - not available anywhere
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else in this export - is the whole point of tracking it here."""
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if top_posts_df.empty:
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return fig
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day_impressions = engagement_df.set_index("Date")["Impressions"]
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matched = top_posts_df[top_posts_df["Post Publish Date"].isin(engagement_df["Date"])]
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specs = [(False, "Original post", "star", BLUE), (True, "Repost", "diamond", ORANGE)]
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for is_repost, label, symbol, color in specs:
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subset = matched[matched["Is Repost"] == is_repost]
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if subset.empty:
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continue
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fig.add_trace(go.Scatter(
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x=subset["Post Publish Date"],
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y=[day_impressions.get(d, 0) for d in subset["Post Publish Date"]],
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mode="markers",
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marker=dict(symbol=symbol, size=13, color=color, line=dict(width=1, color="white")),
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name=label,
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hovertemplate=f"<b>{label} — " + "%{x|%b %d, %Y}</b><extra></extra>",
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))
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return fig
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def _team_engagement_figure(engagement_df, top_posts_df):
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fig = go.Figure()
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fig.add_trace(go.Scatter(x=engagement_df["Date"], y=engagement_df["Impressions"],
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name="Impressions", mode="lines", line=dict(color=BLUE, width=2)))
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fig.add_trace(go.Scatter(x=engagement_df["Date"], y=engagement_df["Engagements"],
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name="Engagements", mode="lines", line=dict(color=ORANGE, width=2),
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yaxis="y2"))
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_team_mark_posts(fig, engagement_df, top_posts_df)
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fig.update_layout(yaxis=dict(title="Impressions"),
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yaxis2=dict(title="Engagements", overlaying="y", side="right"),
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template=TMPL)
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@@ -2745,21 +2785,32 @@ def _team_demographics_table(demo_df):
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def _build_person_dashboard(name, data):
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discovery = data["discovery"]
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total_engagements = int(data["engagement"]["Engagements"].sum())
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stat_row = dbc.Row([
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_team_stat_tile(discovery.get("Impressions", 0), "Impressions"),
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_team_stat_tile(discovery.get("Members reached", 0), "Members Reached"),
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_team_stat_tile(data["followers_total"], "Total Followers"),
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_team_stat_tile(total_engagements, "Engagements"),
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], className="gx-3 mb-3")
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window_label = discovery.get("date_range", "")
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window_start, window_end = (window_label.split(" - ") + [""])[:2] if window_label else ("", "")
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new_followers_window = int(data["followers_daily"]["New followers"].sum())
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starting_followers = data["followers_total"] - new_followers_window
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best_eng_pct, avg_eng_pct, rated_post_count = _team_engagement_rate_stats(data["top_posts"])
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stat_row = _team_kpi_row([
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kpi("Starting Followers", f"{starting_followers:,}", f"as of {window_start}", "#999"),
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kpi("Total Followers", f"{data['followers_total']:,}", f"as of {window_end}", TEAL),
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kpi("New Followers", f"{new_followers_window:,}", window_label),
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kpi("Total Impressions", f"{discovery.get('Impressions', 0):,}", window_label),
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kpi("Members Reached", f"{discovery.get('Members reached', 0):,}", window_label),
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kpi("Best Post Real Eng.", f"{best_eng_pct:.2f}%" if best_eng_pct is not None else "—",
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f"across {rated_post_count} posts", GREEN),
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kpi("Avg. Eng. Rate", f"{avg_eng_pct:.2f}%" if avg_eng_pct is not None else "—",
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"raw, personal profile (organic)", TEAL),
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])
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source_note = html.Div(
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f"Source: {', '.join(data['source_files'])} — window {discovery.get('date_range', '')}",
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f"Source: {', '.join(data['source_files'])} — window {window_label}",
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style={"fontSize": "0.72rem", "color": "#6b7a8d", "marginBottom": "0.75rem"})
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charts_row = dbc.Row([
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dbc.Col(section("Daily Impressions & Engagements",
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G(_team_engagement_figure(data["engagement"]), style={"height": "260px"})), md=6),
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G(_team_engagement_figure(data["engagement"], data["top_posts"]), style={"height": "260px"})), md=6),
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dbc.Col(section("Daily New Followers",
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G(_team_followers_figure(data["followers_daily"]), style={"height": "260px"})), md=6),
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], className="gx-3 mb-3")
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@@ -2788,12 +2839,15 @@ def _build_empty_person_dashboard(name):
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table, demographics cards) so every team member's tab looks consistent before
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their first export lands, instead of a lone text alert - each box just shows a
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"No data yet" placeholder in place of real content."""
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stat_row = dbc.Row([
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_team_stat_tile("—", "Impressions"),
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_team_stat_tile("—", "Members Reached"),
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_team_stat_tile("—", "Total Followers"),
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_team_stat_tile("—", "Engagements"),
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], className="gx-3 mb-3")
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stat_row = _team_kpi_row([
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kpi("Starting Followers", "—", "", "#999"),
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kpi("Total Followers", "—", "", TEAL),
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kpi("New Followers", "—", ""),
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kpi("Total Impressions", "—", ""),
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kpi("Members Reached", "—", ""),
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kpi("Best Post Real Eng.", "—", "", GREEN),
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kpi("Avg. Eng. Rate", "—", "", TEAL),
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])
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source_note = dbc.Alert(
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f"No personal LinkedIn export data for {name} yet. Drop their "
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