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codeflash-agent/reports/unstructured/engagement_report.py
Kevin Turcios a4276d658a Refine engagement report and case study for executive review 
- Hero metrics: -89% cost, -52% peak memory, flat scaling, -12.9% latency
- Add lightspeed canvas animation via assets/lightspeed.js for Plotly Cloud
- Add platform-libs CI/CD migration to timeline (Phase 1b) with PR links
- Update next-engagement card with POC branch and PR references
- Replace RSS with peak memory in user-facing copy
- Add flat memory scaling to case study results table
2026-04-16 17:51:54 -05:00

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"""Unstructured x Codeflash — Engagement Report
Four-tab report served at http://localhost:8050/:
1. Executive Summary — high-level engagement summary for VP Engineering (JPC)
2. Engineering Details — for Crag's team, aggregate view with commit refs
3. Full Detail — per-PR inventory, benchmarks, methodology
4. Timeline — proposed engagement phases with Gantt chart
Standalone routes:
/jpc — shareable executive summary (same content as tab 1)
/timeline — shareable timeline (same content as tab 4)
"""
import json
import os
from pathlib import Path
import plotly.graph_objects as go
from dash import (
Dash,
Input,
Output,
clientside_callback,
dash_table,
dcc,
html,
)
from theme import (
ACCENT,
AMBER,
BG,
BLUE,
CARD,
CARD_BG,
CARD_BORDER,
DARK,
FONT,
GRAY,
GREEN,
GRID_OVERLAY,
LIGHT_GRAY,
LIGHT_GREEN,
LIGHT_RED,
MONO,
PURPLE,
RED,
SLATE,
TABLE_CELL,
TABLE_DATA,
TABLE_DATA_CONDITIONAL,
TABLE_HEADER,
TABLE_WRAP,
WHITE,
)
# ── Data ────────────────────────────────────────────────────────────────────
_DATA = json.loads((Path(__file__).parent / "data.json").read_text())
CORE_PRODUCT_BASE = _DATA["core_product_base"]
UNSTRUCTURED_BASE = _DATA["unstructured_base"]
INFERENCE_BASE = _DATA["inference_base"]
OD_MODELS_BASE = _DATA["od_models_base"]
REPO_BASES: dict[str, str] = {
"core-product": CORE_PRODUCT_BASE,
"unstructured": UNSTRUCTURED_BASE,
"unstructured-inference": INFERENCE_BASE,
"unstructured-od-models": OD_MODELS_BASE,
}
MEM_BEFORE = _DATA["mem_before"]
MEM_AFTER = _DATA["mem_after"]
BENCH_BEFORE = _DATA["bench_before"]
BENCH_AFTER = _DATA["bench_after"]
LATENCY_STANDALONE = _DATA["latency_standalone"]
MERGED_PRS = _DATA["merged_prs"]
OPEN_PRS = _DATA["open_prs"]
# ── Helpers ──────────────────────────────────────────────────────────────────
def hero_metric(value, label, detail, color=GREEN):
return html.Div(
[
html.Div(
value,
style={
"fontSize": "42px",
"fontWeight": "800",
"color": color,
"lineHeight": "1",
"letterSpacing": "-0.02em",
"fontFamily": FONT,
},
),
html.Div(
label,
style={
"fontSize": "15px",
"fontWeight": "600",
"color": SLATE,
"marginTop": "8px",
},
),
html.Div(
detail,
style={"fontSize": "13px", "color": GRAY, "marginTop": "4px"},
),
],
style={
"background": CARD_BG,
"borderRadius": "16px",
"padding": "32px 24px",
"textAlign": "center",
"flex": "1 1 0%",
"minWidth": "0",
"border": f"1px solid {CARD_BORDER}",
},
)
def section(title, subtitle=None):
children = [
html.H2(
title,
style={
"fontSize": "22px",
"fontWeight": "700",
"color": SLATE,
"margin": "0",
"fontFamily": FONT,
"letterSpacing": "-0.01em",
},
)
]
if subtitle:
children.append(
html.P(
subtitle,
style={
"fontSize": "14px",
"color": GRAY,
"margin": "6px 0 0",
"lineHeight": "1.5",
},
)
)
return html.Div(children, style={"margin": "56px 0 24px"})
def card(children, **kw):
style = {**CARD}
for k, v in kw.items():
style[k] = v
return html.Div(children, style=style)
def _next_card(number, title, description, notes=None):
"""Numbered card for the 'Future Engagements' section."""
left = html.Div(
[
html.Div(
number,
style={
"fontSize": "28px",
"fontWeight": "800",
"color": ACCENT,
"lineHeight": "1",
"minWidth": "36px",
},
),
html.Div(
[
html.Div(
title,
style={
"fontWeight": "700",
"color": SLATE,
"fontSize": "16px",
"marginBottom": "8px",
},
),
html.P(
description,
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.6",
"margin": "0",
},
),
],
),
],
style={
"display": "flex",
"gap": "16px",
"alignItems": "flex-start",
"flex": "1",
},
)
if not notes:
return card([left])
right = html.Div(
[
html.Div(
"Notes",
style={
"fontSize": "11px",
"fontWeight": "700",
"color": ACCENT,
"textTransform": "uppercase",
"letterSpacing": "0.05em",
"marginBottom": "8px",
},
),
html.Ul(
[
html.Li(
n,
style={
"fontSize": "13px",
"color": LIGHT_GRAY,
"lineHeight": "1.5",
"marginBottom": "4px",
},
)
for n in notes
],
style={
"paddingLeft": "16px",
"margin": "0",
"listStyleType": "'– '",
},
),
],
style={
"minWidth": "240px",
"maxWidth": "280px",
"borderLeft": f"1px solid {CARD_BORDER}",
"paddingLeft": "20px",
"marginLeft": "20px",
},
)
return card(
[
html.Div(
[left, right],
style={
"display": "flex",
"gap": "0",
"alignItems": "flex-start",
},
),
],
)
def metric_row(
label, before, after, unit="", fmt="{:,.0f}", better="lower", note=None
):
if before and after:
delta = (after - before) / before * 100
improved = delta < 0 if better == "lower" else delta > 0
delta_text = f"{delta:+.0f}%"
delta_color = GREEN if improved else RED
delta_bg = LIGHT_GREEN if improved else LIGHT_RED
else:
delta_text, delta_color, delta_bg = "—", GRAY, "transparent"
def _f(v):
return f"{fmt.format(v)} {unit}".strip() if v is not None else "—"
return html.Div(
[
html.Div(
[
html.Span(
label,
style={
"fontWeight": "600",
"color": SLATE,
"fontSize": "14px",
},
),
html.Span(
f" {note}",
style={"fontSize": "12px", "color": LIGHT_GRAY},
)
if note
else html.Span(),
],
style={"flex": "1"},
),
html.Div(
_f(before),
style={
"width": "140px",
"textAlign": "right",
"color": GRAY,
"fontSize": "14px",
"fontFamily": MONO,
},
),
html.Div(
_f(after),
style={
"width": "140px",
"textAlign": "right",
"color": SLATE,
"fontSize": "14px",
"fontWeight": "600",
"fontFamily": MONO,
},
),
html.Span(
delta_text,
style={
"width": "80px",
"textAlign": "center",
"fontSize": "13px",
"fontWeight": "700",
"color": delta_color,
"background": delta_bg,
"borderRadius": "6px",
"padding": "2px 8px",
},
),
],
style={
"display": "flex",
"alignItems": "center",
"gap": "16px",
"padding": "12px 0",
"borderBottom": f"1px solid {CARD_BORDER}",
},
)
def table_header(cols):
return html.Div(
style={
"display": "flex",
"gap": "16px",
"padding": "10px 0",
"borderBottom": f"2px solid {CARD_BORDER}",
"marginBottom": "4px",
},
children=[
html.Div(
c["label"],
style={
"flex": "1" if c.get("flex") else None,
"width": c.get("width"),
"textAlign": c.get("align", "left"),
"fontWeight": "700",
"fontSize": "13px",
"color": ACCENT,
"textTransform": "uppercase",
"letterSpacing": "0.05em",
},
)
for c in cols
],
)
# ── Charts ───────────────────────────────────────────────────────────────────
def make_memory_chart():
"""Before/after memory: the headline chart (FastAPI endpoint measurement)."""
cats = ["Pre-Partition RSS", "Post-Partition RSS", "Partition Delta"]
before = [
MEM_BEFORE["pre_partition_mb"],
MEM_BEFORE["post_partition_mb"],
MEM_BEFORE["partition_delta_mb"],
]
after = [
MEM_AFTER["pre_partition_mb"],
MEM_AFTER["post_partition_mb"],
MEM_AFTER["partition_delta_mb"],
]
fig = go.Figure()
fig.add_trace(
go.Bar(
name="Before (glibc, 4 OCR workers)",
x=cats,
y=before,
marker_color=LIGHT_GRAY,
marker_cornerradius=6,
text=[f"{v:,.0f} MB" for v in before],
textposition="outside",
textfont={"size": 13, "color": GRAY},
)
)
fig.add_trace(
go.Bar(
name="After (jemalloc opt-in, serial OCR, 1-CPU)",
x=cats,
y=after,
marker_color=ACCENT,
marker_cornerradius=6,
text=[f"{v:,.0f} MB" for v in after],
textposition="outside",
textfont={"size": 13, "color": ACCENT},
)
)
fig.update_layout(
barmode="group",
bargap=0.3,
bargroupgap=0.1,
plot_bgcolor="rgba(0,0,0,0)",
paper_bgcolor="rgba(0,0,0,0)",
font={"family": FONT, "size": 13, "color": SLATE},
yaxis={
"title": "Memory (MB)",
"gridcolor": CARD_BORDER,
"zeroline": False,
},
xaxis={"title": ""},
margin={"t": 20, "b": 60, "l": 60, "r": 20},
legend={
"orientation": "h",
"yanchor": "bottom",
"y": 1.05,
"xanchor": "center",
"x": 0.5,
"font": {"size": 13},
},
height=380,
)
return fig
# ── Shared layout components ─────────────────────────────────────────────────
_TAB_BTN_STYLE = {
"padding": "10px 24px",
"border": "none",
"borderRadius": "8px",
"cursor": "pointer",
"fontSize": "14px",
"fontWeight": "600",
"fontFamily": FONT,
"background": "transparent",
"color": GRAY,
"transition": "all 0.2s",
}
_TAB_BTN_ACTIVE = {**_TAB_BTN_STYLE, "background": ACCENT, "color": DARK}
def _logo_lockup(codeflash_h="24px", unstructured_h="28px", gap="16px", radius="4px"):
"""Codeflash × Unstructured logo pair, reused in headers and footers."""
return html.Div(
style={
"display": "flex",
"alignItems": "center",
"gap": gap,
},
children=[
html.Img(src="/assets/codeflash.svg", style={"height": codeflash_h}),
html.Span(
"\u00d7",
style={
"fontSize": f"{int(codeflash_h.replace('px', '')) - 6}px",
"fontWeight": "300",
"color": LIGHT_GRAY,
},
),
html.Img(
src="/assets/unstructured_logo.jpg",
style={"height": unstructured_h, "borderRadius": radius},
),
],
)
# ── View builders ────────────────────────────────────────────────────────────
def build_team_view():
return html.Div(
id="team-view",
style={"display": "none"},
children=[
# ── Engineering Impact Summary ──
section(
"Engineering Impact Summary",
"What changed and what it means for your infrastructure.",
),
card(
[
html.Div(
style={
"display": "flex",
"gap": "20px",
"flexWrap": "wrap",
},
children=[
html.Div(
[
html.Div(
"32 → 4 GB",
style={
"fontSize": "24px",
"fontWeight": "800",
"color": GREEN,
"lineHeight": "1",
},
),
html.Div(
"K8s memory limit per pod",
style={
"fontSize": "13px",
"color": GRAY,
"marginTop": "6px",
},
),
],
style={"flex": "1", "minWidth": "160px"},
),
html.Div(
[
html.Div(
"5 → 46",
style={
"fontSize": "24px",
"fontWeight": "800",
"color": GREEN,
"lineHeight": "1",
},
),
html.Div(
"pods per D48s_v5 node",
style={
"fontSize": "13px",
"color": GRAY,
"marginTop": "6px",
},
),
],
style={"flex": "1", "minWidth": "160px"},
),
html.Div(
[
html.Div(
"-12.9%",
style={
"fontSize": "24px",
"fontWeight": "800",
"color": ACCENT,
"lineHeight": "1",
},
),
html.Div(
"end-to-end latency (FastAPI)",
style={
"fontSize": "13px",
"color": GRAY,
"marginTop": "6px",
},
),
],
style={"flex": "1", "minWidth": "160px"},
),
html.Div(
[
html.Div(
"41 vCPU",
style={
"fontSize": "24px",
"fontWeight": "800",
"color": ACCENT,
"lineHeight": "1",
},
),
html.Div(
"previously idle, now available",
style={
"fontSize": "13px",
"color": GRAY,
"marginTop": "6px",
},
),
],
style={"flex": "1", "minWidth": "160px"},
),
],
),
html.P(
"The sections below cover how these numbers were measured, "
"what specifically changed in the codebase, and the per-PR "
"benchmark breakdown.",
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.6",
"marginTop": "20px",
"paddingTop": "16px",
"borderTop": f"1px solid {CARD_BORDER}",
},
),
],
),
# ── Methodology ────────────────────────────────────────────
section(
"Methodology",
"How every number in this report was produced.",
),
# Environment card
card(
[
html.H3(
"Benchmark Environment",
style={
"fontSize": "16px",
"fontWeight": "700",
"color": ACCENT,
"margin": "0 0 16px",
},
),
html.Div(
[
_method_row(
"Hardware",
"Azure Standard_E4s_v5 — 4 vCPU, 32 GB RAM, "
"non-burstable (consistent clock speed, no noisy-neighbor variance).",
),
_method_row(
"OS / Runtime",
"Ubuntu 24.04 LTS, Python 3.12, pip-installed "
"unstructured + all extras.",
),
_method_row(
"CPU Pinning",
"taskset -c 0 pins the process to a single core. "
"This simulates the production pod's 1-CPU "
"resource request (CFS quota) and eliminates "
"cross-core scheduling noise.",
),
_method_row(
"Baseline Config",
"main branch, glibc malloc, 4 parallel OCR workers "
"(os.cpu_count). This is the default behaviour "
"when deploying core-product today.",
),
_method_row(
"Current Config",
"All merged optimizations + jemalloc (opt-in), serial OCR "
"via cgroup-aware CPU detection (1 worker on 1-CPU pods).",
),
]
),
],
marginBottom="24px",
),
# Measurement protocol card
card(
[
html.H3(
"Measurement Protocol",
style={
"fontSize": "16px",
"fontWeight": "700",
"color": ACCENT,
"margin": "0 0 16px",
},
),
html.Div(
[
_method_row(
"Workload",
"10-page scanned PDF → hi_res strategy via "
"POST /general/v0/general (FastAPI / uvicorn). "
"Same document and model weights (YOLOX) in every run.",
),
_method_row(
"Latency",
"pytest-benchmark pedantic mode — 5 rounds, "
"1 warmup, median reported. Stddev consistently "
"< 0.4%, confirming low noise.",
),
_method_row(
"Memory",
"psutil process-tree RSS sampled via the FastAPI "
"endpoint. Process-tree (not single-process) captures "
"the OCR worker pool and pdfium subprocesses that "
"drive the memory limit. We measure at four points: "
"pre-import, post-import, post-partition, and "
"per-request delta — separating static from dynamic "
"memory to identify what contributes to baseline "
"overhead vs. per-request cost.",
),
_method_row(
"Profiling",
"cProfile for CPU hotspots; memray --native for "
"per-allocation breakdown (including C extensions). "
"Profiling runs are separate from benchmark runs "
"to avoid observer effect.",
),
_method_row(
"Standalone vs. Cumulative",
"Each optimization is benchmarked both in "
"isolation (one PR vs. main) and cumulatively "
"(full stack). This dual approach catches a common "
"problem: optimizations that look good individually "
"but interfere when stacked (e.g. two changes "
"competing for the same cache lines). Standalone "
"confirms each change's contribution; cumulative "
"confirms they compose without regression.",
),
]
),
],
marginBottom="24px",
),
# Variance control card
card(
[
html.H3(
"Variance Control",
style={
"fontSize": "16px",
"fontWeight": "700",
"color": ACCENT,
"margin": "0 0 16px",
},
),
html.Div(
[
_method_row(
"A/B/A Validation",
"Every latency improvement is validated with an "
"A/B/A pattern: run optimization, then baseline, "
"then optimization again. If A1 ≈ A2, the delta "
"is real and not thermal drift. If A2 degrades "
"toward B, the result is discarded.",
),
_method_row(
"Non-Burstable VM",
"E4s_v5 specifically chosen over B-series. "
"Burstable VMs have variable CPU performance "
"(credit-based throttling) that makes benchmarks "
"unreliable. Non-burstable guarantees consistent "
"clock speed with no noisy-neighbor variance.",
),
_method_row(
"Statistical over Hardware",
"We attempted to disable turbo boost and pin CPU "
"frequency via cpupower, but Azure Hyper-V "
"overrides guest frequency settings — the "
"hypervisor manages the physical CPU. Instead we "
"rely on statistical methods: 5 measured rounds + "
"1 warmup + median reporting, which tolerates up "
"to 2 outliers per measurement.",
),
_method_row(
"Warmup Round",
"The discarded warmup round absorbs three "
"specific first-run costs: ONNX model JIT and "
"session creation, page cache warming for the PDF "
"test file, and OCR/pdfium process pool "
"initialization. Without it, the first measured "
"round is 10-30% slower than steady state.",
),
]
),
],
marginBottom="24px",
),
# ── What Changed ──────────────────────────────────────────
section(
"What Changed: Memory",
"Three root causes fixed, per-request memory creep reduced (24 MB \u2192 17 MB/req), one allocator optimization added.",
),
html.Div(
[
html.Div(
[
html.Div(
"CPU-Aware OCR Worker Count",
style={
"fontWeight": "700",
"color": SLATE,
"fontSize": "16px",
},
),
html.Span(
"Biggest impact",
style={
"marginLeft": "12px",
"padding": "2px 10px",
"borderRadius": "999px",
"fontSize": "12px",
"fontWeight": "600",
"background": ACCENT,
"color": DARK,
},
),
],
style={
"marginBottom": "12px",
"display": "flex",
"alignItems": "center",
},
),
html.P(
[
html.Code(
"os.cpu_count()",
style={
"fontFamily": MONO,
"color": ACCENT,
},
),
" returns the host CPU count (e.g. 48 on a D48s_v5 node), not the pod's CFS quota (1). ",
"The OCR pool was spawning 4 workers on a 1-CPU pod, each loading the full ONNX model set. "
"Replaced with a three-tier detection: ",
html.Code(
"/sys/fs/cgroup/cpu.max",
style={
"fontFamily": MONO,
"color": ACCENT,
},
),
" (cgroup v2) first, then ",
html.Code(
"sched_getaffinity",
style={
"fontFamily": MONO,
"color": ACCENT,
},
),
" (cpuset), then ",
html.Code(
"os.cpu_count()",
style={
"fontFamily": MONO,
"color": ACCENT,
},
),
" — taking the minimum. Result: serial mode on 1-CPU pods, "
"eliminating 3 redundant model copies from memory.",
],
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.6",
"margin": "0",
},
),
html.Div(
html.A(
"PR #1502",
href=f"{CORE_PRODUCT_BASE}/1502",
target="_blank",
style={
"color": BLUE,
"fontSize": "13px",
"textDecoration": "none",
},
),
style={"marginTop": "8px"},
),
],
style={
**CARD,
"marginBottom": "16px",
"borderLeft": f"4px solid {ACCENT}",
},
),
html.Div(
[
html.Div(
"Resize-First Preprocessing",
style={
"fontWeight": "700",
"color": SLATE,
"fontSize": "16px",
"marginBottom": "12px",
},
),
html.P(
"Pages were being converted to full-resolution numpy arrays before any resizing. "
"Now resizes the PIL image first, avoiding a large temporary allocation for every page.",
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.6",
"margin": "0",
},
),
html.Div(
html.A(
"PR #1441",
href=f"{CORE_PRODUCT_BASE}/1441",
target="_blank",
style={
"color": BLUE,
"fontSize": "13px",
"textDecoration": "none",
},
),
style={"marginTop": "8px"},
),
],
style={
**CARD,
"marginBottom": "16px",
"borderLeft": f"4px solid {GREEN}",
},
),
html.Div(
[
html.Div(
"Early Page Image Release",
style={
"fontWeight": "700",
"color": SLATE,
"fontSize": "16px",
"marginBottom": "12px",
},
),
html.P(
"Page images were held in memory through the entire table OCR + transformer inference pipeline. "
"Now freed as soon as OCR is complete, reducing peak concurrent memory.",
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.6",
"margin": "0",
},
),
html.Div(
html.A(
"PR #1448",
href=f"{CORE_PRODUCT_BASE}/1448",
target="_blank",
style={
"color": BLUE,
"fontSize": "13px",
"textDecoration": "none",
},
),
style={"marginTop": "8px"},
),
],
style={
**CARD,
"marginBottom": "16px",
"borderLeft": f"4px solid {GREEN}",
},
),
html.Div(
[
html.Div(
"jemalloc Allocator",
style={
"fontWeight": "700",
"color": SLATE,
"fontSize": "16px",
"marginBottom": "12px",
},
),
html.P(
"Opt-in allocator switch from glibc malloc to jemalloc via MALLOC_IMPL=jemalloc. "
"Reduces memory fragmentation from the alloc/free churn in the serial OCR pipeline "
"(-21% partition delta). Recommended for 1-CPU pods only — on multi-CPU pods with "
"parallel workers, jemalloc's per-arena metadata overhead (~50 MB/process) can erase "
"the savings. Multi-CPU deployments should use the glibc default.",
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.6",
"margin": "0",
},
),
html.Div(
html.A(
"PR #1507",
href=f"{CORE_PRODUCT_BASE}/1507",
target="_blank",
style={
"color": BLUE,
"fontSize": "13px",
"textDecoration": "none",
},
),
style={"marginTop": "8px"},
),
],
style={
**CARD,
"marginBottom": "16px",
"borderLeft": f"4px solid {AMBER}",
},
),
# Memory results chart
section("Memory Results"),
card(
[
dcc.Graph(
figure=make_memory_chart(),
config={"displayModeBar": False},
)
]
),
# Additional memory metrics (not shown in chart above)
card(
[
table_header(
[
{"label": "Metric", "flex": True},
{
"label": "Before",
"width": "140px",
"align": "right",
},
{
"label": "After",
"width": "140px",
"align": "right",
},
{
"label": "Delta",
"width": "80px",
"align": "center",
},
]
),
metric_row(
"RSS per request",
MEM_BEFORE["rss_per_req_mb"],
MEM_AFTER["rss_per_req_mb"],
"MB",
note="stability across sequential requests",
),
metric_row(
"K8s allocation",
MEM_BEFORE["k8s_gb"] * 1024,
MEM_AFTER["k8s_gb"] * 1024,
"MB",
),
],
marginTop="20px",
),
section(
"What Changed: Latency",
"Five optimizations: an O(N\u00b2) algorithmic fix, redundant image format conversions, "
"and unnecessary serialization in the OCR pipeline. Cumulative: 50.8s to 44.3s (-12.9%) via FastAPI.",
),
html.Div(
[
html.Div(
[
html.Div(
"O(N\u00b2) Text Extraction Fix",
style={
"fontWeight": "700",
"color": SLATE,
"fontSize": "16px",
},
),
html.Span(
"Algorithmic",
style={
"marginLeft": "12px",
"padding": "2px 10px",
"borderRadius": "999px",
"fontSize": "12px",
"fontWeight": "600",
"background": RED,
"color": WHITE,
},
),
],
style={
"marginBottom": "12px",
"display": "flex",
"alignItems": "center",
},
),
html.P(
[
html.Code(
"_patch_current_chars_with_render_mode",
style={
"fontFamily": MONO,
"color": ACCENT,
},
),
" was re-scanning the full character list on every patch operation \u2014 "
"O(N\u00b2) scaling that caused processing time to grow quadratically on "
"text-heavy documents. Replaced with a single-pass approach.",
],
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.6",
"margin": "0",
},
),
html.Div(
html.A(
"PR #4266 (merged)",
href=f"{_DATA['unstructured_base']}/4266",
target="_blank",
style={
"color": BLUE,
"fontSize": "13px",
"textDecoration": "none",
},
),
style={"marginTop": "8px"},
),
],
style={
**CARD,
"marginBottom": "16px",
"borderLeft": f"4px solid {RED}",
},
),
html.Div(
[
html.Div(
"BMP Instead of PNG for PDF Rendering",
style={
"fontWeight": "700",
"color": SLATE,
"fontSize": "16px",
"marginBottom": "12px",
},
),
html.P(
"pdfium was rendering pages as PNG (compressed) when the downstream consumer immediately "
"decompresses to a raw bitmap. Switching to BMP skips the compression step entirely.",
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.6",
"margin": "0",
},
),
html.Div(
[
html.Span(
"-89 ms/page",
style={
"color": GREEN,
"fontWeight": "700",
},
),
html.Span(
" | -890 ms for a 10-page scan | standalone: -14.6%",
style={"color": GRAY},
),
],
style={
"marginTop": "8px",
"fontSize": "14px",
},
),
html.Div(
html.A(
"PR #1503 (open)",
href=f"{CORE_PRODUCT_BASE}/1503",
target="_blank",
style={
"color": BLUE,
"fontSize": "13px",
"textDecoration": "none",
},
),
style={"marginTop": "4px"},
),
],
style={
**CARD,
"marginBottom": "16px",
"borderLeft": f"4px solid {GREEN}",
},
),
html.Div(
[
html.Div(
[
html.Div(
"Direct File Path to Tesseract (Parallel Workers)",
style={
"fontWeight": "700",
"color": SLATE,
"fontSize": "16px",
},
),
html.Span(
"Biggest latency impact",
style={
"marginLeft": "12px",
"padding": "2px 10px",
"borderRadius": "999px",
"fontSize": "12px",
"fontWeight": "600",
"background": ACCENT,
"color": DARK,
},
),
],
style={
"marginBottom": "12px",
"display": "flex",
"alignItems": "center",
},
),
html.P(
"The OCR path was: numpy array -> PIL Image -> temp PNG file -> tesseract CLI. "
"The page image already exists on disk from pdfium rendering. "
"Passing the path directly to pytesseract skips three intermediate copies.",
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.6",
"margin": "0",
},
),
html.Div(
[
html.Span(
"-32.6% on 1-page tables",
style={
"color": GREEN,
"fontWeight": "700",
},
),
html.Span(
" | -7.7% on 10-page scan | -7.4% on 16-page mixed",
style={"color": GRAY},
),
],
style={
"marginTop": "8px",
"fontSize": "14px",
},
),
html.Div(
html.A(
"PR #1505 (open)",
href=f"{CORE_PRODUCT_BASE}/1505",
target="_blank",
style={
"color": BLUE,
"fontSize": "13px",
"textDecoration": "none",
},
),
style={"marginTop": "4px"},
),
],
style={
**CARD,
"marginBottom": "16px",
"borderLeft": f"4px solid {ACCENT}",
},
),
html.Div(
[
html.Div(
"Direct File Path to Tesseract (Serial Fallback)",
style={
"fontWeight": "700",
"color": SLATE,
"fontSize": "16px",
"marginBottom": "12px",
},
),
html.P(
"Same optimization applied to the serial OCR fallback path (1-CPU pods). "
"Eliminated 1.97s of PNG re-encoding self-time across 10 pages.",
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.6",
"margin": "0",
},
),
html.Div(
[
html.Span(
"-1.8% wall clock",
style={
"color": GREEN,
"fontWeight": "700",
},
),
html.Span(
" | -98% PNG encode self-time (1.97s to 0.04s)",
style={"color": GRAY},
),
],
style={
"marginTop": "8px",
"fontSize": "14px",
},
),
html.Div(
html.A(
"PR #1506 (merged)",
href=f"{CORE_PRODUCT_BASE}/1506",
target="_blank",
style={
"color": BLUE,
"fontSize": "13px",
"textDecoration": "none",
},
),
style={"marginTop": "4px"},
),
],
style={
**CARD,
"marginBottom": "16px",
"borderLeft": f"4px solid {GREEN}",
},
),
html.Div(
[
html.Div(
"BMP Temp Files for Pytesseract",
style={
"fontWeight": "700",
"color": SLATE,
"fontSize": "16px",
"marginBottom": "12px",
},
),
html.P(
"When pytesseract receives in-memory images (multi-CPU pods, direct API calls), "
"it creates a temp file for the tesseract CLI. Monkey-patching the format from PNG to BMP "
"cuts encoding from ~0.27s to ~0.018s per page (15x faster).",
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.6",
"margin": "0",
},
),
html.Div(
[
html.Span(
"-6.4% standalone",
style={
"color": GREEN,
"fontWeight": "700",
},
),
html.Span(
" | -91% pytesseract save() time | complements path passthrough on multi-CPU",
style={"color": GRAY},
),
],
style={
"marginTop": "8px",
"fontSize": "14px",
},
),
html.Div(
html.A(
"PR #1509 (open)",
href=f"{CORE_PRODUCT_BASE}/1509",
target="_blank",
style={
"color": BLUE,
"fontSize": "13px",
"textDecoration": "none",
},
),
style={"marginTop": "4px"},
),
],
style={
**CARD,
"marginBottom": "16px",
"borderLeft": f"4px solid {AMBER}",
},
),
],
)
def _method_row(label, text):
"""A single labeled row for the methodology cards."""
return html.Div(
[
html.Span(
label,
style={
"fontWeight": "700",
"color": SLATE,
"minWidth": "160px",
"fontSize": "14px",
},
),
html.Span(
text,
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.6",
},
),
],
style={
"display": "flex",
"gap": "16px",
"padding": "10px 0",
"borderBottom": f"1px solid {CARD_BORDER}",
},
)
def _above_fold_content(negative_margin=False):
"""Hero Metrics + Infrastructure Cost Impact + Broader Context.
Used above the tab toggle on the main page and at the top of /jpc.
"""
top_margin = "-40px" if negative_margin else "0"
return [
html.Div(
style={
"display": "flex",
"gap": "20px",
"flexWrap": "wrap",
"marginTop": top_margin,
"position": "relative",
"zIndex": "1",
},
children=[
hero_metric(
"-89%",
"Core-Product Cost",
"$10,000/mo \u2192 ~$1,100/mo",
ACCENT,
),
hero_metric(
"-52%",
"Peak Memory Usage",
"4,651 MB \u2192 2,227 MB per pod",
GREEN,
),
hero_metric(
"Flat",
"Memory Scaling",
"Constant peak memory regardless of document count",
GREEN,
),
hero_metric(
"-12.9%",
"Latency",
"50.8s \u2192 44.3s via production FastAPI path",
ACCENT,
),
],
),
section(
"Infrastructure Cost Impact",
"AKS node packing analysis based on current production topology.",
),
card(
[
html.P(
[
"Production runs on ",
html.Span(
"Standard_D48s_v5",
style={"fontWeight": "700", "color": SLATE},
),
" nodes (48 vCPU, 192 GB RAM) at ",
html.Span(
"$2.304/hr ($1,682/mo)",
style={
"fontWeight": "700",
"color": ACCENT,
"fontFamily": MONO,
},
),
" per node. Each core-product pod requests ",
html.Span(
"1 CPU / 32 GB RAM",
style={"fontWeight": "700", "color": SLATE},
),
" per pod.",
],
style={
"color": GRAY,
"fontSize": "15px",
"lineHeight": "1.7",
"margin": "0 0 20px",
},
),
html.Div(
style={
"display": "flex",
"gap": "20px",
"flexWrap": "wrap",
"marginBottom": "20px",
},
children=[
html.Div(
[
html.Div(
"BEFORE",
style={
"fontSize": "11px",
"fontWeight": "700",
"color": RED,
"letterSpacing": "0.1em",
"marginBottom": "12px",
},
),
html.Div(
"5 pods / node",
style={
"fontSize": "28px",
"fontWeight": "800",
"color": SLATE,
"lineHeight": "1",
},
),
html.Div(
"RAM is the bottleneck",
style={
"fontSize": "13px",
"color": GRAY,
"marginTop": "8px",
},
),
],
style={
"background": CARD_BG,
"borderRadius": "12px",
"padding": "20px 24px",
"border": f"1px solid {CARD_BORDER}",
"borderTop": f"3px solid {RED}",
"flex": "1 1 0%",
"minWidth": "0",
},
),
html.Div(
"\u2192",
style={
"fontSize": "32px",
"color": GRAY,
"alignSelf": "center",
"padding": "0 4px",
},
),
html.Div(
[
html.Div(
"AFTER",
style={
"fontSize": "11px",
"fontWeight": "700",
"color": GREEN,
"letterSpacing": "0.1em",
"marginBottom": "12px",
},
),
html.Div(
"46 pods / node",
style={
"fontSize": "28px",
"fontWeight": "800",
"color": GREEN,
"lineHeight": "1",
},
),
html.Div(
"CPU becomes the bottleneck",
style={
"fontSize": "13px",
"color": GRAY,
"marginTop": "8px",
},
),
],
style={
"background": CARD_BG,
"borderRadius": "12px",
"padding": "20px 24px",
"border": f"1px solid {CARD_BORDER}",
"borderTop": f"3px solid {GREEN}",
"flex": "1 1 0%",
"minWidth": "0",
},
),
],
),
html.Div(
style={
"display": "flex",
"gap": "20px",
"flexWrap": "wrap",
"marginTop": "4px",
},
children=[
html.Div(
[
html.Div(
"Current Spend",
style={
"fontSize": "11px",
"fontWeight": "700",
"color": RED,
"letterSpacing": "0.1em",
"marginBottom": "8px",
},
),
html.Div(
"$10,000/mo",
style={
"fontSize": "32px",
"fontWeight": "800",
"color": SLATE,
"lineHeight": "1",
"fontFamily": MONO,
},
),
],
style={
"background": CARD_BG,
"borderRadius": "12px",
"padding": "20px 24px",
"border": f"1px solid {CARD_BORDER}",
"flex": "1 1 0%",
"minWidth": "0",
},
),
html.Div(
"\u2192",
style={
"fontSize": "32px",
"color": GRAY,
"alignSelf": "center",
"padding": "0 4px",
},
),
html.Div(
[
html.Div(
"Recommended",
style={
"fontSize": "11px",
"fontWeight": "700",
"color": GREEN,
"letterSpacing": "0.1em",
"marginBottom": "8px",
},
),
html.Div(
"~$1,100/mo",
style={
"fontSize": "32px",
"fontWeight": "800",
"color": GREEN,
"lineHeight": "1",
"fontFamily": MONO,
},
),
],
style={
"background": CARD_BG,
"borderRadius": "12px",
"padding": "20px 24px",
"border": f"1px solid {CARD_BORDER}",
"flex": "1 1 0%",
"minWidth": "0",
},
),
html.Div(
[
html.Div(
"Savings",
style={
"fontSize": "11px",
"fontWeight": "700",
"color": ACCENT,
"letterSpacing": "0.1em",
"marginBottom": "8px",
},
),
html.Div(
"~$8,900/mo",
style={
"fontSize": "32px",
"fontWeight": "800",
"color": ACCENT,
"lineHeight": "1",
"fontFamily": MONO,
},
),
html.Div(
"~$107K/year in compute savings",
style={
"fontSize": "13px",
"color": GRAY,
"marginTop": "8px",
},
),
],
style={
"background": CARD_BG,
"borderRadius": "12px",
"padding": "20px 24px",
"border": f"1px solid {CARD_BORDER}",
"flex": "1 1 0%",
"minWidth": "0",
},
),
],
),
html.P(
"Based on Azure retail pricing for Standard_D48s_v5 in US East ($2.304/hr). "
"Assumes ~46 usable vCPU and ~186 GB usable RAM per node after AKS system reservations.",
style={
"color": LIGHT_GRAY,
"fontSize": "12px",
"marginTop": "12px",
},
),
],
),
html.Div(
[
html.Div(
[
html.Span(
"Broader Context",
style={
"fontSize": "13px",
"fontWeight": "700",
"color": ACCENT,
"letterSpacing": "0.03em",
},
),
],
style={"marginBottom": "10px"},
),
html.P(
[
"Core-product compute represents roughly ",
html.Span(
"10% of the total Azure spend",
style={"fontWeight": "700", "color": SLATE},
),
". The approach that achieved 90% savings here "
"can be applied across the broader platform "
"infrastructure \u2014 with dedicated instance savings flowing through "
"automatically.",
],
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.7",
"margin": "0",
},
),
],
style={
**CARD,
"marginTop": "16px",
"borderLeft": f"4px solid {ACCENT}",
},
),
]
def _jpc_content():
"""Inner content for the JPC summary — reused by tab and standalone route."""
return [
# ── The Engagement ──
section(
"The Engagement",
"Codeflash partnered with the core-product team to profile and "
"optimize the document processing pipeline.",
),
card(
[
html.P(
[
"We identified that core-product pods were requesting ",
html.Span(
"32 GB of RAM",
style={"fontWeight": "700", "color": SLATE},
),
" but still occasionally OOM'ing. The root cause: Python's ",
html.Code(
"os.cpu_count()",
style={
"fontFamily": MONO,
"color": ACCENT,
"fontSize": "13px",
},
),
" was returning the host's full CPU count "
"(48 on a D48s_v5 node) instead of the pod's 1-CPU limit, "
"spawning redundant OCR workers that each loaded the full ONNX "
"model set — 4x the memory for zero parallelism benefit.",
],
style={
"color": GRAY,
"fontSize": "15px",
"lineHeight": "1.7",
"margin": "0 0 16px",
},
),
html.P(
"Over 7 weeks, we profiled the pipeline end-to-end — and each optimization "
"peeled back a layer, revealing issues that had been masked by larger problems "
"upstream. Fixing the worker pool exposed per-request memory creep (24 MB/req from "
"PIL image churn). Reducing memory noise surfaced an O(N\u00b2) text extraction "
"bottleneck and unnecessary PNG serialization between processes. These weren't "
"problems anyone had reason to look for — they only became visible as earlier "
"fixes shifted the performance profile. 24 merged PRs across 5 repos, all "
"passing the existing test suite with zero regressions.",
style={
"color": GRAY,
"fontSize": "15px",
"lineHeight": "1.7",
"margin": "0",
},
),
]
),
# ── What This Enables ──
section("What This Enables"),
card(
[
html.Div(
style={
"display": "flex",
"flexDirection": "column",
"gap": "20px",
},
children=[
html.Div(
[
html.Div(
[
html.Span(
"9.2x",
style={
"fontSize": "28px",
"fontWeight": "800",
"color": GREEN,
"marginRight": "12px",
},
),
html.Span(
"pod density improvement",
style={
"fontSize": "16px",
"fontWeight": "700",
"color": SLATE,
},
),
],
style={
"display": "flex",
"alignItems": "baseline",
},
),
html.P(
"Pods that required 32 GB now run in 4 GB. "
"Same nodes, same hardware — 46 pods per node instead of 5. "
"This frees capacity for the platform team to scale without "
"provisioning new infrastructure.",
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.6",
"margin": "8px 0 0",
},
),
],
style={
"paddingBottom": "20px",
"borderBottom": f"1px solid {CARD_BORDER}",
},
),
html.Div(
[
html.Div(
[
html.Span(
"41 idle vCPUs",
style={
"fontSize": "28px",
"fontWeight": "800",
"color": ACCENT,
"marginRight": "12px",
},
),
html.Span(
"now available per node",
style={
"fontSize": "16px",
"fontWeight": "700",
"color": SLATE,
},
),
],
style={
"display": "flex",
"alignItems": "baseline",
},
),
html.P(
"When RAM was the bottleneck, nodes were at 11% CPU utilization — "
"41 of 48 vCPUs sitting idle. With memory constraints removed, "
"that compute capacity becomes available for higher throughput "
"or additional workloads.",
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.6",
"margin": "8px 0 0",
},
),
],
style={
"paddingBottom": "20px",
"borderBottom": f"1px solid {CARD_BORDER}",
},
),
html.Div(
[
html.Div(
[
html.Span(
"-12.9%",
style={
"fontSize": "28px",
"fontWeight": "800",
"color": ACCENT,
"marginRight": "12px",
},
),
html.Span(
"end-to-end latency reduction",
style={
"fontSize": "16px",
"fontWeight": "700",
"color": SLATE,
},
),
],
style={
"display": "flex",
"alignItems": "baseline",
},
),
html.P(
"50.8s to 44.3s on a 10-page scanned document through the "
"production FastAPI path. Faster document processing means "
"faster responses for platform API consumers — directly "
"relevant as the API is positioned for agentic tool use.",
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.6",
"margin": "8px 0 0",
},
),
],
),
],
),
],
),
# ── Delivered ──
section("Delivered"),
card(
[
html.Div(
style={
"display": "flex",
"gap": "40px",
"flexWrap": "wrap",
},
children=[
html.Div(
[
html.Div(
"24",
style={
"fontSize": "36px",
"fontWeight": "800",
"color": ACCENT,
"lineHeight": "1",
},
),
html.Div(
"PRs merged",
style={
"fontSize": "14px",
"color": GRAY,
"marginTop": "4px",
},
),
]
),
html.Div(
[
html.Div(
"5",
style={
"fontSize": "36px",
"fontWeight": "800",
"color": AMBER,
"lineHeight": "1",
},
),
html.Div(
"PRs in progress",
style={
"fontSize": "14px",
"color": GRAY,
"marginTop": "4px",
},
),
]
),
html.Div(
[
html.Div(
"354",
style={
"fontSize": "36px",
"fontWeight": "800",
"color": GREEN,
"lineHeight": "1",
},
),
html.Div(
"tests passing",
style={
"fontSize": "14px",
"color": GRAY,
"marginTop": "4px",
},
),
]
),
],
),
html.P(
"All changes delivered as individual, reviewable PRs across "
"5 repositories: core-product, unstructured, unstructured-inference, "
"unstructured-od-models, and github-workflows. Each PR includes "
"benchmark numbers and passes the existing test suite.",
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.6",
"marginTop": "20px",
"paddingTop": "16px",
"borderTop": f"1px solid {CARD_BORDER}",
},
),
]
),
# ── Proposed Next Engagement ──
html.Hr(
style={
"border": "none",
"borderTop": f"1px solid {CARD_BORDER}",
"margin": "64px 0 0",
},
),
html.Div(
[
html.H2(
"Proposed Next Engagement",
style={
"fontSize": "28px",
"fontWeight": "800",
"color": WHITE,
"margin": "0",
"fontFamily": FONT,
"letterSpacing": "-0.02em",
},
),
html.P(
"Core-product represents ~10% of Unstructured's Azure spend. "
"The techniques that delivered 90% savings here can be applied "
"across the broader platform. Based on our discovery work and "
"discussions with the team, we recommend the following tracks.",
style={
"fontSize": "15px",
"color": GRAY,
"margin": "12px 0 0",
"lineHeight": "1.6",
"maxWidth": "640px",
},
),
],
style={
"margin": "48px 0 32px",
"padding": "32px 0",
"borderLeft": f"4px solid {ACCENT}",
"paddingLeft": "24px",
},
),
html.Div(
style={
"display": "flex",
"flexDirection": "column",
"gap": "16px",
},
children=[
_next_card(
"1",
"Platform API Speed & Stability",
"The platform API is being positioned as an agentic tool "
"endpoint where latency and reliability are critical. Each "
"DAG step spins up a distinct K8s pod on demand — cold start "
"overhead compounds across the pipeline. We can apply the same "
"profiling-driven approach to reduce pod startup time, optimize "
"image warm-up, and improve end-to-end throughput for the "
"transform pipeline.",
notes=[
"Pod cold start reduction via image snapshotting and pre-warming",
"Import time and startup profiling for each pipeline step",
"Throughput optimization: concurrent requests, batch processing",
"Directly supports the agentic API use case",
],
),
_next_card(
"2",
"Developer Experience & CI/CD",
"Collapse the complex GHA workflow permutations into a streamlined "
"uv workspace \u2014 same GitHub Actions, same repo structure, just fewer "
"moving parts. We've already delivered the foundation: uv workspace "
"POC live in the ci-unified-workflows branch and platform-libs#667.",
notes=[
[
"POC live in ",
html.A("ci-unified-workflows", href="https://github.com/Unstructured-IO/github-workflows/tree/ci-unified-workflows", target="_blank", style={"color": BLUE, "textDecoration": "none"}),
" branch and ",
html.A("platform-libs#667", href="https://github.com/Unstructured-IO/platform-libs/pull/667", target="_blank", style={"color": BLUE, "textDecoration": "none"}),
],
"Eliminates per-package workflow permutations \u2014 one matrix, one lockfile",
"No migration off GitHub Actions \u2014 same CI/CD platform, simplified configuration",
"Same approach ready for core-product workspace migration",
],
),
_next_card(
"3",
"Security Hardening",
"During profiling we identified supply chain risks: dependency "
"confusion exposure on internal package names and a lockfile "
"bypass pattern that could allow CVE-affected transitive "
"dependencies. A targeted engagement would harden the build "
"pipeline and complement existing CVE scanning efforts.",
notes=[
"Lockfile bypass via uv pip install allows CVE-affected transitive deps",
"uv workspace migration eliminates bypass vectors by design",
"Complements existing security scanning workflows",
],
),
_next_card(
"4",
"Infrastructure Cost Discovery",
"The full Azure bill is approximately $100K/month for staging, "
"production, and development — with dedicated instance costs on top. "
"A systematic cost audit would identify the highest-impact targets "
"across the platform and for vertical optimization.",
notes=[
"Core-product savings ($8.9K/mo) proves the approach at ~10% of total spend",
"Dedicated instances inherit generic savings automatically",
"Cost discovery surfaces both infrastructure and architecture opportunities",
"Directly impacts gross margins and unit economics",
],
),
],
),
]
def build_jpc_view():
"""Standalone JPC summary at /jpc — full page with header and footer."""
return html.Div(
style={
"background": BG,
"minHeight": "100vh",
"fontFamily": FONT,
},
children=[
html.Div(
style={
"maxWidth": "800px",
"margin": "0 auto",
"padding": "48px 32px 80px",
},
children=[
# ── Header ──
html.Div(
[
html.Div(
_logo_lockup(),
style={"marginBottom": "20px"},
),
html.H1(
"Engagement Summary",
style={
"fontSize": "32px",
"fontWeight": "800",
"color": WHITE,
"letterSpacing": "-0.02em",
"margin": "0 0 8px",
"fontFamily": FONT,
},
),
html.P(
"Performance optimization — core-product document processing pipeline",
style={
"fontSize": "16px",
"color": GRAY,
"margin": "0 0 16px",
},
),
html.Div(
"April 2026 \u00b7 6-week engagement",
style={
"fontSize": "13px",
"color": LIGHT_GRAY,
"fontFamily": MONO,
},
),
],
style={
"marginBottom": "48px",
"paddingBottom": "32px",
"borderBottom": f"1px solid {CARD_BORDER}",
},
),
*_above_fold_content(),
*_jpc_content(),
],
),
],
)
def _build_jpc_tab():
"""JPC summary as a tab view (default active tab)."""
return html.Div(
id="jpc-view",
children=_jpc_content(),
)
def build_detail_view():
return html.Div(
id="detail-view",
style={"display": "none"},
children=[
card(
[
html.P(
"This view contains the raw data behind the Executive Brief and "
"Engineering Details views: every PR, benchmark measurement, and "
"environment detail. All numbers are reproducible on the benchmark VM.",
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.6",
"margin": "0",
},
),
],
marginTop="24px",
borderLeft=f"4px solid {ACCENT}",
),
section(
"Merged PR Inventory",
"All PRs merged across Unstructured repos, ordered by date.",
),
dash_table.DataTable(
columns=[
{"name": "PR", "id": "pr", "presentation": "markdown"},
{"name": "Merged", "id": "date"},
{"name": "Description", "id": "desc"},
{"name": "Category", "id": "cat"},
{"name": "Repo", "id": "repo"},
],
data=sorted(
[
{
"pr": f"[#{r[0]}]({REPO_BASES.get(r[4], CORE_PRODUCT_BASE)}/{r[0]})",
"date": r[1],
"desc": r[2],
"cat": r[3],
"repo": r[4],
}
for r in MERGED_PRS
if r[4] != "github-workflows"
],
key=lambda x: x["date"],
),
markdown_options={"link_target": "_blank"},
style_header=TABLE_HEADER,
style_cell=TABLE_CELL,
style_data=TABLE_DATA,
style_table=TABLE_WRAP,
style_data_conditional=[
{
"if": {"row_index": "odd"},
"backgroundColor": "#1f1f23",
},
*[
{
"if": {
"filter_query": f'{{cat}} = "{cat}"',
"column_id": "cat",
},
"color": color,
"fontWeight": "600",
}
for cat, color in {
"Memory": GREEN,
"Latency": ACCENT,
"Reliability": BLUE,
"Code quality": PURPLE,
}.items()
],
],
),
section("Open / In-Progress PRs"),
dash_table.DataTable(
columns=[
{"name": "PR", "id": "pr", "presentation": "markdown"},
{"name": "Description", "id": "desc"},
{"name": "Category", "id": "cat"},
{"name": "Repo", "id": "repo"},
],
data=[
{
"pr": f"[#{r[0]}]({REPO_BASES.get(r[3], CORE_PRODUCT_BASE)}/{r[0]})",
"desc": r[1],
"cat": r[2],
"repo": r[3],
}
for r in OPEN_PRS
if r[3] != "platform-libs"
],
markdown_options={"link_target": "_blank"},
style_header=TABLE_HEADER,
style_cell=TABLE_CELL,
style_data=TABLE_DATA,
style_table=TABLE_WRAP,
style_data_conditional=[
{
"if": {"row_index": "odd"},
"backgroundColor": "#1f1f23",
},
],
),
section(
"A/B Benchmark Results (memray --native)",
"18 common partition tests, pre-Feb 2026 baseline vs current main. "
"These are the older memray-based numbers; the headline metrics above use the newer "
"FastAPI-based measurements which are more representative of production.",
),
card(
[
table_header(
[
{"label": "Metric", "flex": True},
{
"label": "Baseline",
"width": "140px",
"align": "right",
},
{
"label": "Current",
"width": "140px",
"align": "right",
},
{
"label": "Delta",
"width": "80px",
"align": "center",
},
]
),
metric_row(
"Post-import RSS",
BENCH_BEFORE["post_import_mib"],
BENCH_AFTER["post_import_mib"],
"MiB",
),
metric_row(
"First partition delta",
BENCH_BEFORE["first_partition_delta_mib"],
BENCH_AFTER["first_partition_delta_mib"],
"MiB",
),
metric_row(
"Peak memory",
BENCH_BEFORE["peak_gb"],
BENCH_AFTER["peak_gb"],
"GB",
"{:.3f}",
),
metric_row(
"Total allocated",
BENCH_BEFORE["total_gb"],
BENCH_AFTER["total_gb"],
"GB",
"{:.1f}",
better="lower",
),
metric_row(
"Allocation count",
BENCH_BEFORE["allocs"],
BENCH_AFTER["allocs"],
"",
"{:,.0f}",
better="lower",
),
metric_row(
"Wall time",
BENCH_BEFORE["wall_s"],
BENCH_AFTER["wall_s"],
"s",
"{:.1f}",
),
]
),
html.P(
"Total allocated increased because current uses more frequent smaller allocations - "
"peak (the OOM-risk metric) still decreased. This pattern indicates better memory recycling.",
style={
"color": LIGHT_GRAY,
"fontSize": "12px",
"marginTop": "12px",
},
),
section(
"Latency Optimization Detail",
"Individual PR benchmarks (standalone vs main) and cumulative via FastAPI endpoint.",
),
# ── Workload Profiles ──
card(
[
html.H3(
"Benchmark Workload Profiles",
style={
"fontSize": "16px",
"fontWeight": "700",
"color": ACCENT,
"margin": "0 0 16px",
},
),
html.P(
"Page count is one dimension of workload, but content density "
"and element type are what actually drive compute cost. A 10-page "
"table-heavy PDF can be more expensive than a 100-page native text PDF. "
"These three documents were chosen to isolate different workload shapes, "
"not just different page counts.",
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.6",
"margin": "0 0 16px",
},
),
html.Div(
[
_method_row(
"1p-tables",
"A single page dense with tables. Despite being 1 page, "
"this is the heaviest per-page workload — each table triggers "
"its own OCR + transformer inference pass. Isolates optimizations "
"that target per-element cost.",
),
_method_row(
"10p-scan",
"10-page scanned document, hi_res strategy. Every page goes through "
"the full pipeline: render → layout detection → OCR. Closest to the "
"real production workload on the FastAPI endpoint.",
),
_method_row(
"16p-mixed",
"16 pages of mixed content: native text, scans, and tables. Not every "
"page hits the heavy path — native text skips OCR entirely. Tests that "
"optimizations improve the heavy path without regressing the light one.",
),
]
),
],
marginBottom="24px",
),
dash_table.DataTable(
columns=[
{"name": "Optimization", "id": "opt"},
{"name": "1p-tables", "id": "one_page"},
{"name": "10p-scan", "id": "ten_page"},
{"name": "16p-mixed", "id": "sixteen_page"},
],
data=[
{
"opt": r[0],
"one_page": r[1],
"ten_page": r[2],
"sixteen_page": r[3],
}
for r in LATENCY_STANDALONE
]
+ [
{
"opt": "Cumulative (FastAPI, warmed)",
"one_page": "",
"ten_page": "-12.9%",
"sixteen_page": "",
}
],
style_header=TABLE_HEADER,
style_cell=TABLE_CELL,
style_data=TABLE_DATA,
style_data_conditional=TABLE_DATA_CONDITIONAL,
style_table=TABLE_WRAP,
),
html.P(
"Individual contributions overlap (they optimize adjacent stages of the same pipeline), "
"so they don't sum to the cumulative total. Cumulative measured through the real production path: "
"uvicorn -> FastAPI -> POST /general/v0/general with strategy=hi_res. "
"Note how #1505 has 4x the impact on the 1-page doc vs. the 16-page doc — "
"because that single page is table-dense and OCR-heavy. Conversely, #1503 scales "
"with page count because it optimizes a per-page operation (render format). "
"This is why per-document workload depends on content, not page count.",
style={
"color": LIGHT_GRAY,
"fontSize": "12px",
"marginTop": "12px",
},
),
section("Benchmark Environment"),
dash_table.DataTable(
columns=[
{"name": "Parameter", "id": "param"},
{"name": "Value", "id": "value"},
],
data=[
{
"param": "VM",
"value": "Azure Standard_E4s_v5 (4 vCPU, 32 GB RAM, non-burstable)",
},
{"param": "OS", "value": "Ubuntu 24.04 LTS"},
{"param": "Python", "value": "3.12"},
{
"param": "CPU Pinning",
"value": "taskset -c 0 (simulates production 1-CPU resource request / CFS quota)",
},
{
"param": "Latency",
"value": "pytest-benchmark pedantic (5 rounds, 1 warmup, median reported, <0.4% stddev)",
},
{
"param": "Memory",
"value": "psutil process-tree RSS via FastAPI endpoint (uvicorn -> POST /general/v0/general)",
},
{
"param": "Profiling",
"value": "cProfile + memray --native for per-function breakdown",
},
{
"param": "Baseline",
"value": "main (glibc, 4 OCR workers via os.cpu_count)",
},
{
"param": "Current",
"value": "full stack + jemalloc opt-in (serial OCR via cgroup-aware CPU detection)",
},
{
"param": "Production Target",
"value": "1-CPU resource request / 32 GB limit -> 4 GB recommended",
},
],
style_header=TABLE_HEADER,
style_cell=TABLE_CELL,
style_data=TABLE_DATA,
style_data_conditional=TABLE_DATA_CONDITIONAL,
style_table=TABLE_WRAP,
),
# ── Methodology Notes ──
card(
[
html.H3(
"Methodology Notes",
style={
"fontSize": "16px",
"fontWeight": "700",
"color": ACCENT,
"margin": "0 0 16px",
},
),
html.Div(
[
_method_row(
"Why non-burstable?",
"B-series VMs use credit-based CPU throttling — "
"once credits deplete, CPU performance drops to a "
"baseline fraction. E4s_v5 guarantees consistent "
"clock speed with no noisy-neighbor variance, so "
"benchmark results are reproducible.",
),
_method_row(
"Why CPU pinning?",
"Production pods have a 1-CPU CFS quota. taskset -c 0 "
"pins the benchmark process to a single core, matching "
"the scheduler behaviour pods actually experience. "
"Without pinning, the kernel can migrate the process "
"across cores, introducing L1/L2 cache invalidation "
"noise that doesn't exist in production.",
),
_method_row(
"Why pedantic mode?",
"pytest-benchmark's pedantic mode disables adaptive "
"iteration counts and runs exactly the configured "
"rounds. This gives us deterministic measurement — "
"same rounds, same conditions, every run. Combined "
"with median reporting, up to 2 of 5 rounds can be "
"outliers without affecting the result.",
),
_method_row(
"Why warmup?",
"The discarded warmup round absorbs three first-run "
"costs: ONNX model JIT and session creation, page "
"cache warming for the PDF test file, and OCR/pdfium "
"process pool initialization. Without it, the first "
"measured round is 10-30% slower than steady state.",
),
_method_row(
"Why A/B/A validation?",
"Every latency improvement is validated with an A/B/A "
"pattern: run optimization, then baseline, then "
"optimization again. If A1 and A2 agree, the delta is "
"real and not thermal drift or background load. If A2 "
"degrades toward B, the result is discarded.",
),
_method_row(
"Why process-tree RSS?",
"psutil's process-tree RSS captures memory from the "
"main process and all child processes (OCR workers, "
"pdfium subprocesses). Single-process RSS would miss "
"the worker pool memory that's the root cause of the "
"high memory limit.",
),
_method_row(
"Why separate profiling runs?",
"cProfile and memray instrument every function call "
"and allocation, adding 2-5x overhead. Running them "
"during benchmark rounds would inflate latency and "
"distort memory measurements (observer effect). "
"Profiling runs identify hotspots; benchmark runs "
"measure impact.",
),
]
),
],
marginBottom="24px",
),
],
)
# ── App ──────────────────────────────────────────────────────────────────────
app = Dash(
__name__,
meta_tags=[
{"name": "viewport", "content": "width=device-width, initial-scale=1"},
{
"property": "og:title",
"content": "Unstructured x Codeflash — Engagement Report",
},
{
"property": "og:description",
"content": "Performance optimization across 4 repos: 52% memory reduction, 12.9% latency improvement, 24 PRs merged",
},
],
suppress_callback_exceptions=True,
)
app.title = "Unstructured x Codeflash — Engagement Report"
app.index_string = """<!DOCTYPE html>
<html>
<head>
{%metas%}
<title>{%title%}</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700;800&family=JetBrains+Mono:wght@400;600;700&display=swap" rel="stylesheet">
{%favicon%}
{%css%}
<style>
.dash-table-container .dash-cell a,
.dash-table-container .cell-markdown a,
.dash-table-container a,
.dash-spreadsheet a { color: #60a5fa !important; text-decoration: none !important; }
.dash-table-container a:hover,
.dash-spreadsheet a:hover { text-decoration: underline !important; }
#lightspeed-canvas {
position: fixed;
top: 0; left: 0;
width: 100%; height: 100%;
pointer-events: none;
z-index: 9999;
}
</style>
</head>
<body>
<canvas id="lightspeed-canvas"></canvas>
{%app_entry%}
<footer>
{%config%}
{%scripts%}
{%renderer%}
</footer>
<script>
(function() {
var canvas = document.getElementById('lightspeed-canvas');
var ctx = canvas.getContext('2d');
var dpr = window.devicePixelRatio || 1;
var W, H;
function resize() {
W = window.innerWidth;
H = window.innerHeight;
canvas.width = W * dpr;
canvas.height = H * dpr;
canvas.style.width = W + 'px';
canvas.style.height = H + 'px';
ctx.setTransform(dpr, 0, 0, dpr, 0, 0);
}
resize();
window.addEventListener('resize', resize);
var palette = [
{r:70, g:90, b:130, w:5},
{r:90, g:110, b:155, w:4},
{r:110, g:135, b:180, w:3},
{r:60, g:75, b:110, w:3},
{r:130, g:155, b:200, w:2},
{r:255, g:210, b:39, w:1},
];
var wc = [];
for (var p = 0; p < palette.length; p++)
for (var w = 0; w < palette[p].w; w++) wc.push(palette[p]);
var NUM = 80;
var bars = [];
function makeBar(scatter) {
var c = wc[Math.floor(Math.random() * wc.length)];
var angleDeg = 8 + Math.random() * 14;
var angle = angleDeg * Math.PI / 180;
var len = 120 + Math.random() * 500;
var thickness = 4 + Math.random() * 14;
var speed = 0.15 + Math.random() * 0.6;
var x = -len - Math.random() * 400;
var y = Math.random() * H * 1.4 - H * 0.2;
if (scatter) {
x = Math.random() * (W + len) - len * 0.5;
y = Math.random() * H;
}
return {
x: x, y: y,
angle: angle,
speed: speed,
len: len,
thickness: thickness,
color: c,
alpha: 0.12 + Math.random() * 0.22,
life: 0,
maxLife: 800 + Math.random() * 2000
};
}
for (var i = 0; i < NUM; i++) bars.push(makeBar(true));
function draw() {
ctx.clearRect(0, 0, W, H);
for (var i = 0; i < NUM; i++) {
var b = bars[i];
b.x += Math.cos(b.angle) * b.speed;
b.y += Math.sin(b.angle) * b.speed;
b.life++;
if (b.x > W + b.len * 2 || b.life > b.maxLife) {
bars[i] = makeBar(false);
b = bars[i];
}
var lp = b.life / b.maxLife;
var fade = lp < 0.15 ? lp / 0.15 :
lp > 0.7 ? (1 - lp) / 0.3 : 1;
var a = b.alpha * Math.max(0, fade);
var cos = Math.cos(b.angle);
var sin = Math.sin(b.angle);
var px = -sin * b.thickness * 0.5;
var py = cos * b.thickness * 0.5;
var x0 = b.x;
var y0 = b.y;
var x1 = b.x + cos * b.len;
var y1 = b.y + sin * b.len;
var grad = ctx.createLinearGradient(x0, y0, x1, y1);
var cr = b.color.r, cg = b.color.g, cb = b.color.b;
grad.addColorStop(0, 'rgba(' + cr + ',' + cg + ',' + cb + ',0)');
grad.addColorStop(0.2, 'rgba(' + cr + ',' + cg + ',' + cb + ',' + a + ')');
grad.addColorStop(0.8, 'rgba(' + cr + ',' + cg + ',' + cb + ',' + a + ')');
grad.addColorStop(1, 'rgba(' + cr + ',' + cg + ',' + cb + ',0)');
ctx.beginPath();
ctx.moveTo(x0 + px, y0 + py);
ctx.lineTo(x1 + px, y1 + py);
ctx.lineTo(x1 - px, y1 - py);
ctx.lineTo(x0 - px, y0 - py);
ctx.closePath();
ctx.fillStyle = grad;
ctx.fill();
}
requestAnimationFrame(draw);
}
requestAnimationFrame(draw);
})();
</script>
</body>
</html>"""
def _tl_node(number, title, dates, duration, status, deliverables, color,
dependencies=None, is_last=False, concurrent_with=None):
"""Single node in the vertical timeline."""
status_colors = {"Completed": GREEN, "Ready to Start": AMBER, "Proposed": ACCENT}
sc = status_colors.get(status, ACCENT)
filled = status == "Completed"
dot = html.Div(
style={
"width": "16px",
"height": "16px",
"borderRadius": "50%",
"background": color if filled else "transparent",
"border": f"3px solid {color}",
"position": "relative",
"zIndex": "2",
"flexShrink": "0",
},
)
connector = html.Div(
style={
"width": "2px",
"flexGrow": "1",
"background": f"linear-gradient({color}, {CARD_BORDER})" if not is_last else "transparent",
"margin": "4px auto 0",
"minHeight": "0" if is_last else "20px",
},
)
phase_card = html.Div(
[
html.Div(
style={
"display": "flex",
"justifyContent": "space-between",
"alignItems": "center",
"marginBottom": "8px",
},
children=[
html.Div(
[
html.Span(
f"Phase {number}",
style={
"fontSize": "11px",
"fontWeight": "700",
"color": color,
"fontFamily": MONO,
"letterSpacing": "0.08em",
"textTransform": "uppercase",
},
),
],
),
html.Span(
status,
style={
"padding": "2px 10px",
"borderRadius": "999px",
"fontSize": "10px",
"fontWeight": "700",
"background": sc if filled else "transparent",
"color": DARK if filled else sc,
"border": f"1px solid {sc}",
},
),
],
),
html.Div(
title,
style={
"fontSize": "17px",
"fontWeight": "700",
"color": WHITE,
"marginBottom": "6px",
},
),
html.Div(
style={
"display": "flex",
"gap": "16px",
"marginBottom": "12px",
"flexWrap": "wrap",
},
children=[
html.Span(
dates,
style={
"fontSize": "13px",
"fontWeight": "600",
"color": SLATE,
"fontFamily": MONO,
},
),
html.Span(
f"\u00b7 {duration}",
style={"fontSize": "13px", "color": LIGHT_GRAY},
),
],
),
*(
[html.Div(
[
html.Span("\u21b3 ", style={"color": AMBER}),
html.Span(dependencies, style={
"color": AMBER, "fontSize": "12px",
}),
],
style={"marginBottom": "12px"},
)] if dependencies else []
),
*(
[html.Div(
[
html.Span("\u2194 ", style={"color": LIGHT_GRAY}),
html.Span(f"Runs parallel with Phase {concurrent_with}", style={
"color": LIGHT_GRAY, "fontSize": "12px",
}),
],
style={"marginBottom": "12px"},
)] if concurrent_with else []
),
html.Div(
style={
"paddingTop": "12px",
"borderTop": f"1px solid {CARD_BORDER}",
},
children=[
html.Ul(
[html.Li(d, style={
"fontSize": "13px", "color": GRAY, "lineHeight": "1.7",
"paddingLeft": "4px",
}) for d in deliverables],
style={"paddingLeft": "16px", "margin": "0"},
),
],
),
],
style={
**CARD,
"borderLeft": f"3px solid {color}",
"marginLeft": "20px",
"flex": "1 1 0%",
},
)
return html.Div(
style={
"display": "flex",
"gap": "0",
"alignItems": "stretch",
},
children=[
html.Div(
style={
"display": "flex",
"flexDirection": "column",
"alignItems": "center",
"width": "16px",
"flexShrink": "0",
"paddingTop": "18px",
},
children=[dot, connector],
),
html.Div(
phase_card,
style={"flex": "1 1 0%", "paddingBottom": "0" if is_last else "20px"},
),
],
)
def _tl_gap(label):
"""Visual gap indicator between phases (e.g. '1 week buffer')."""
return html.Div(
style={
"display": "flex",
"gap": "0",
"alignItems": "stretch",
},
children=[
html.Div(
style={
"display": "flex",
"flexDirection": "column",
"alignItems": "center",
"width": "16px",
"flexShrink": "0",
},
children=[
html.Div(style={
"width": "2px",
"height": "100%",
"background": CARD_BORDER,
"margin": "0 auto",
"borderLeft": f"2px dashed {CARD_BORDER}",
"minHeight": "40px",
}),
],
),
html.Div(
html.Span(label, style={
"fontSize": "11px",
"fontWeight": "600",
"color": LIGHT_GRAY,
"fontFamily": MONO,
"letterSpacing": "0.05em",
}),
style={
"marginLeft": "20px",
"display": "flex",
"alignItems": "center",
},
),
],
)
def _timeline_content():
"""Inner content for the timeline view — reused by standalone route and tab."""
return [
section("Vertical Optimization Roadmap"),
html.Div(
style={"position": "relative"},
children=[
_tl_node(
"1", "Core-Product Optimization",
"Feb 27 \u2192 Apr 14", "7 weeks", "Completed",
deliverables=[
"24 PRs merged across 5 repos, 354 tests passing",
"Memory: 32 GB \u2192 4 GB K8s pod allocation (\u221287.5%)",
"Latency: \u221212.9% end-to-end (50.8s \u2192 44.3s)",
"Pod density: 5 \u2192 46 per node (9.2x improvement)",
"Cost: ~$8,900/mo savings on core-product compute",
],
color=GREEN,
),
_tl_node(
"1b", "Platform-Libs CI/CD Migration",
"Apr 9 \u2192 Apr 14", "1 week", "Ready to Start",
deliverables=[
[
"POC live in ",
html.A("ci-unified-workflows", href="https://github.com/Unstructured-IO/github-workflows/tree/ci-unified-workflows", target="_blank", style={"color": BLUE, "textDecoration": "none"}),
" branch and ",
html.A("platform-libs#667", href="https://github.com/Unstructured-IO/platform-libs/pull/667", target="_blank", style={"color": BLUE, "textDecoration": "none"}),
],
"CI runners: ~189 \u2192 ~27 per PR (\u221285% billed minutes)",
"Same GitHub Actions \u2014 fewer workflow permutations, not a platform migration",
],
color=GREEN,
),
_tl_gap("1 week buffer"),
_tl_node(
"2", "Developer Experience & CI/CD",
"Apr 21 \u2192 May 2", "2 weeks", "Proposed",
deliverables=[
"uv workspace migration for core-product (building on platform-libs POC)",
"Single lockfile replacing fragmented dependency install steps",
"CI pipeline modernization: wall time from ~4 min to ~1 min",
"Developer onboarding documentation and migration guide",
],
concurrent_with="4",
color=BLUE,
),
_tl_node(
"3", "Platform API Speed & Stability",
"May 5 \u2192 May 16", "2 weeks", "Proposed",
deliverables=[
"Pod cold start profiling and reduction (image snapshotting, pre-warming)",
"Import time audit for each pipeline step",
"End-to-end throughput optimization (concurrent requests, batch processing)",
"Latency benchmarks for the agentic tool endpoint",
"Reliability improvements: error handling, retry logic, circuit breakers",
],
dependencies="Builds on Phase 2 CI improvements",
color=ACCENT,
),
_tl_node(
"4", "Security Hardening",
"Apr 21 \u2192 May 2", "2 weeks", "Proposed",
deliverables=[
"Lockfile bypass remediation (eliminate uv pip install vectors)",
"Dependency confusion audit on internal package names",
"Supply chain hardening: pinned hashes, namespace reservation",
"Integration with existing CVE scanning workflows",
],
concurrent_with="2",
color=PURPLE,
),
_tl_node(
"5", "Infrastructure Cost Discovery",
"May 19 \u2192 Jun 27", "6 weeks", "Proposed",
deliverables=[
"Full Azure spend audit ($100K/mo staging + production + development)",
"Dedicated instance cost mapping and optimization targets",
"Right-sizing recommendations across all service tiers",
"Optimization roadmap with projected savings by workload",
],
dependencies="After Phases 2\u20134 deliver optimization data",
color=AMBER,
is_last=True,
),
],
),
# ── Investment Summary ──
html.Div(
[
html.Div(
[
html.Span(
"Investment Summary",
style={
"fontSize": "13px",
"fontWeight": "700",
"color": ACCENT,
"letterSpacing": "0.03em",
},
),
],
style={"marginBottom": "16px"},
),
html.Div(
style={
"display": "flex",
"gap": "20px",
"flexWrap": "wrap",
"marginBottom": "16px",
},
children=[
html.Div(
[
html.Div(
"~4 months",
style={
"fontSize": "24px",
"fontWeight": "800",
"color": SLATE,
"lineHeight": "1",
},
),
html.Div(
"total timeline (with overlap)",
style={
"fontSize": "13px",
"color": GRAY,
"marginTop": "6px",
},
),
],
style={"flex": "1 1 0%", "minWidth": "140px"},
),
html.Div(
[
html.Div(
"5 phases",
style={
"fontSize": "24px",
"fontWeight": "800",
"color": ACCENT,
"lineHeight": "1",
},
),
html.Div(
"1 completed, 4 proposed",
style={
"fontSize": "13px",
"color": GRAY,
"marginTop": "6px",
},
),
],
style={"flex": "1 1 0%", "minWidth": "140px"},
),
html.Div(
[
html.Div(
"$107K/yr",
style={
"fontSize": "24px",
"fontWeight": "800",
"color": GREEN,
"lineHeight": "1",
"fontFamily": MONO,
},
),
html.Div(
"already realized (Phase 1)",
style={
"fontSize": "13px",
"color": GRAY,
"marginTop": "6px",
},
),
],
style={"flex": "1 1 0%", "minWidth": "140px"},
),
],
),
html.P(
"Phase 1 has already paid for itself. Phases 2\u20135 extend "
"the same proven approach across the platform \u2014 with speed "
"and stability as the primary focus, and cost savings as a "
"natural byproduct.",
style={
"color": GRAY,
"fontSize": "14px",
"lineHeight": "1.7",
"margin": "0",
"paddingTop": "16px",
"borderTop": f"1px solid {CARD_BORDER}",
},
),
],
style={
**CARD,
"marginTop": "32px",
"borderLeft": f"4px solid {GREEN}",
},
),
]
def build_timeline_view():
"""Standalone timeline at /timeline — full page with header and footer."""
return html.Div(
style={
"background": BG,
"minHeight": "100vh",
"fontFamily": FONT,
},
children=[
html.Div(
style={
"maxWidth": "900px",
"margin": "0 auto",
"padding": "48px 32px 80px",
},
children=[
# ── Header ──
html.Div(
[
html.Div(
_logo_lockup(),
style={"marginBottom": "20px"},
),
html.H1(
"Proposed Engagement Timeline",
style={
"fontSize": "32px",
"fontWeight": "800",
"color": WHITE,
"letterSpacing": "-0.02em",
"margin": "0 0 8px",
"fontFamily": FONT,
},
),
html.P(
"Phased roadmap for continued performance, reliability, "
"and security work across the Unstructured platform.",
style={
"fontSize": "16px",
"color": GRAY,
"margin": "0 0 16px",
"lineHeight": "1.6",
},
),
html.Div(
"April 2026 \u00b7 5 phases \u00b7 ~4 months total",
style={
"fontSize": "13px",
"color": LIGHT_GRAY,
"fontFamily": MONO,
},
),
],
style={
"marginBottom": "48px",
"paddingBottom": "32px",
"borderBottom": f"1px solid {CARD_BORDER}",
},
),
*_timeline_content(),
# ── Footer ──
html.Div(
style={
"textAlign": "center",
"marginTop": "64px",
"paddingTop": "24px",
"borderTop": f"1px solid {CARD_BORDER}",
},
children=[
html.Div(
_logo_lockup("16px", "20px", "10px", "3px"),
style={
"display": "flex",
"justifyContent": "center",
"marginBottom": "4px",
},
),
html.P(
"Proposed Engagement Timeline — April 2026",
style={
"color": LIGHT_GRAY,
"fontSize": "13px",
"margin": "0",
},
),
],
),
],
),
],
)
def _build_timeline_tab():
"""Timeline as a tab view (hidden by default)."""
return html.Div(
id="timeline-view",
style={"display": "none"},
children=_timeline_content(),
)
def _main_layout():
"""The full three-tab report (default at /)."""
return html.Div(
style={
"background": BG,
"minHeight": "100vh",
"fontFamily": FONT,
"position": "relative",
},
children=[
# ── Grid overlay ──
html.Div(style=GRID_OVERLAY),
# ── Hero ──
html.Div(
style={
"background": f"linear-gradient(135deg, {BG} 0%, #1c1917 50%, {BG} 100%)",
"padding": "60px 24px 52px",
"textAlign": "center",
"borderBottom": f"1px solid {CARD_BORDER}",
"position": "relative",
"zIndex": "1",
},
children=[
# ── Logo lockup: Codeflash x Unstructured ──
html.Div(
_logo_lockup("32px", "36px", "20px", "6px"),
style={
"display": "flex",
"justifyContent": "center",
"marginBottom": "24px",
},
),
html.H1(
"Engagement Report",
style={
"color": WHITE,
"fontSize": "36px",
"fontWeight": "800",
"margin": "0",
"letterSpacing": "-0.02em",
"fontFamily": FONT,
},
),
html.P(
"Performance optimization across the Unstructured platform",
style={
"color": GRAY,
"fontSize": "17px",
"margin": "12px auto 0",
"maxWidth": "700px",
},
),
html.Div(
style={
"marginTop": "24px",
"display": "flex",
"justifyContent": "center",
"gap": "24px",
"flexWrap": "wrap",
},
children=[
html.Span(
"March - April 2026",
style={"color": LIGHT_GRAY, "fontSize": "13px"},
),
html.Span("|", style={"color": LIGHT_GRAY}),
html.Span(
"24 PRs merged",
style={"color": LIGHT_GRAY, "fontSize": "13px"},
),
html.Span("|", style={"color": LIGHT_GRAY}),
html.Span(
"5 PRs in progress",
style={"color": LIGHT_GRAY, "fontSize": "13px"},
),
],
),
],
),
# ── Content ──
html.Div(
style={
"maxWidth": "960px",
"margin": "0 auto",
"padding": "0 24px 80px",
"position": "relative",
"zIndex": "1",
},
children=[
*_above_fold_content(negative_margin=True),
# ── View Toggle ──
html.Div(
style={
"display": "flex",
"justifyContent": "center",
"margin": "40px 0 8px",
},
children=[
html.Div(
style={
"display": "inline-flex",
"background": CARD_BG,
"borderRadius": "12px",
"padding": "4px",
"border": f"1px solid {CARD_BORDER}",
},
children=[
html.Button("Executive Summary", id="btn-jpc", n_clicks=1, style=_TAB_BTN_ACTIVE),
html.Button("Engineering Details", id="btn-team", n_clicks=0, style=_TAB_BTN_STYLE),
html.Button("Full Detail", id="btn-detail", n_clicks=0, style=_TAB_BTN_STYLE),
html.Button("Timeline", id="btn-timeline", n_clicks=0, style=_TAB_BTN_STYLE),
],
),
],
),
# ═════════════════════════════════════════════════════════════════════
# VIEW 1: EXECUTIVE SUMMARY (JPC)
# High-level engagement summary for VP Engineering
# ═════════════════════════════════════════════════════════════════════
_build_jpc_tab(),
# ═════════════════════════════════════════════════════════════════════
# VIEW 2: ENGINEERING TEAM
# For Crag's team — what changed, in plain language, with commit refs
# ═════════════════════════════════════════════════════════════════════
build_team_view(),
# ═════════════════════════════════════════════════════════════════════
# VIEW 3: FULL DETAIL
# Per-PR inventory, benchmarks, methodology
# ═════════════════════════════════════════════════════════════════════
build_detail_view(),
# ═════════════════════════════════════════════════════════════════════
# VIEW 4: TIMELINE
# Proposed engagement phases with Gantt chart
# ═════════════════════════════════════════════════════════════════════
_build_timeline_tab(),
# ── Footer (always visible) ──
html.Div(
style={
"textAlign": "center",
"marginTop": "64px",
"paddingTop": "24px",
"borderTop": f"1px solid {CARD_BORDER}",
},
children=[
html.Div(
_logo_lockup("16px", "20px", "10px", "3px"),
style={
"display": "flex",
"justifyContent": "center",
"marginBottom": "4px",
},
),
html.P(
"Engagement Report — April 2026",
style={
"color": LIGHT_GRAY,
"fontSize": "13px",
"margin": "0",
},
),
],
),
],
),
],
)
def _serve_layout():
"""Return fresh layout on each page load (Dash best practice)."""
return html.Div([
dcc.Location(id="url", refresh=False),
html.Div(id="page-content"),
])
app.layout = _serve_layout
@app.callback(Output("page-content", "children"), Input("url", "pathname"))
def _route(pathname):
if pathname == "/jpc":
return build_jpc_view()
if pathname == "/timeline":
return build_timeline_view()
return _main_layout()
# ── Toggle callback ──
clientside_callback(
"""
function(jpc_c, team_c, detail_c, timeline_c) {
jpc_c = jpc_c || 0;
team_c = team_c || 0;
detail_c = detail_c || 0;
timeline_c = timeline_c || 0;
var base = {
"padding": "10px 24px", "border": "none", "borderRadius": "8px",
"cursor": "pointer", "fontSize": "14px", "fontWeight": "600",
"fontFamily": "'Inter', system-ui, -apple-system, sans-serif",
"transition": "all 0.2s"
};
var active = Object.assign({}, base, {"background": "#ffd227", "color": "#09090b"});
var inactive = Object.assign({}, base, {"background": "transparent", "color": "#a1a1aa"});
var show = {"display": "block"};
var hide = {"display": "none"};
var mx = Math.max(jpc_c, team_c, detail_c, timeline_c);
if (timeline_c === mx && timeline_c > 0)
return [hide, hide, hide, show, inactive, inactive, inactive, active];
if (detail_c === mx && detail_c > 0)
return [hide, hide, show, hide, inactive, inactive, active, inactive];
if (team_c === mx && team_c > 0)
return [hide, show, hide, hide, inactive, active, inactive, inactive];
return [show, hide, hide, hide, active, inactive, inactive, inactive];
}
""",
Output("jpc-view", "style"),
Output("team-view", "style"),
Output("detail-view", "style"),
Output("timeline-view", "style"),
Output("btn-jpc", "style"),
Output("btn-team", "style"),
Output("btn-detail", "style"),
Output("btn-timeline", "style"),
Input("btn-jpc", "n_clicks"),
Input("btn-team", "n_clicks"),
Input("btn-detail", "n_clicks"),
Input("btn-timeline", "n_clicks"),
)
server = app.server
if __name__ == "__main__":
app.run(
debug=os.getenv("DASH_DEBUG", "1") == "1",
port=int(os.getenv("PORT", "8050")),
)
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