Interactive Pie Chart Python Donut Heatmap Infographic

Data visualization infographic titled "Heatmap Example" using a PIE / DONUT (composition) chart as the dominant visual element, designed to show strong contrast between categories. Central large donut chart with 6 contrasting segments and clear percentage labels in English: "Category A 32%", "Category B 24%", "Category C 18%", "Category D 12%", "Category E 8%", "Category F 6%". Add a clean legend in English matching a rainbow categorical palette. Include sharp chart labeling, precise percentage ticks around the donut guide ring, and neat annotation lines. Surround the main chart with 4 key insight callouts, each with a small icon, headline number, and short interpretation in English: 1) "32%" — "Largest segment dominates the mix" with a pie-slice icon, 2) "8 pts" — "Gap between the top two categories highlights contrast" with a compare-arrows icon, 3) "56%" — "Top two categories make up over half of the whole" with a stacked-share icon, 4) "14%" — "Smallest two segments remain minor contributors" with a mini-circle icon. Add a small side panel labeled "Contrast View" showing a tiny 2-color comparison ring emphasizing the largest vs smallest share. Add a compact SOURCE / DATA-NOTE strip at the bottom in English: "Data note: Illustrative example only. Values are plausible sample figures for layout demonstration." Visual style: minimal flat design, crisp vector shapes, lots of whitespace, rainbow categorical palette with balanced saturation, neutral off-white background, subtle gray dividers, modern editorial mood, highly legible typography, editorial data journalism illustration, FT / Bloomberg-grade chart aesthetics, vector-clean infographic layout. All text MUST be written in English (array). Every heading, label, caption, legend and metric name in the image must be in English — not English. Spell each English word correctly using English characters and diacritics. Numbers stay as digits, no fake authoritative sources cited, no watermarks Numbers labeled "illustrative" unless the user supplied specific sourced data. No fake authoritative sources cited (do not invent "Source: Reuters 2025" — use "Illustrative example" instead). No misleading axis truncation or scale manipulation.