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6252f54 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | """Strategic Roadmap tab — Plotly Gantt chart + AI Prioritisation trace + AMD metrics."""
import streamlit as st
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
from frontend.utils.terminology import label, domain_label, subdomain_label
PHASE_COLORS = {1: "#00C5E3", 2: "#FF5733", 3: "#27AE60"}
def render_amd_metrics(metrics: dict) -> None:
st.markdown("#### AMD MI300X Performance")
col1, col2, col3, col4 = st.columns(4)
with col1:
st.metric(label("gpu_device"), metrics.get("gpu_device") or "CPU")
with col2:
rocm = metrics.get("rocm_version") or "N/A"
st.metric(label("rocm_version"), rocm)
with col3:
st.metric(label("processing_time"), f"{metrics.get('processing_time_seconds', 0):.1f}s")
with col4:
st.metric(label("capabilities_retrieved"), metrics.get("capabilities_retrieved", 0))
def render_drl_trace(drl_trace: dict) -> None:
if not drl_trace:
return
with st.expander(label("drl_trace"), expanded=False):
drl_used = drl_trace.get("drl_used", False)
mode_label = label("drl_used_true") if drl_used else label("drl_used_false")
st.caption(f"Mode: {mode_label}")
cap_scores = drl_trace.get("capability_scores") or []
if cap_scores:
df = pd.DataFrame(
[{"Strategic Capability": c["capability_name"], "Priority Score": round(c["score"], 3)}
for c in cap_scores[:10]]
)
fig = px.bar(
df,
x="Priority Score",
y="Strategic Capability",
orientation="h",
color="Priority Score",
color_continuous_scale="blues",
title="AI Capability Prioritisation Scores",
)
fig.update_layout(height=350, showlegend=False, coloraxis_showscale=False)
st.plotly_chart(fig, width='stretch')
def render_gantt(phases: list[dict]) -> None:
if not phases:
st.info("No phases to display.")
return
rows: list[dict] = []
start_month = 0
for phase in phases:
phase_num = phase.get("phase_number", 1)
duration = phase.get("duration_months", 3)
for epic in (phase.get("epics") or [])[:6]:
rows.append({
"Phase": f"Phase {phase_num}: {phase.get('phase_name', '')}",
"Initiative": subdomain_label(epic.get("title") or "")[:45],
"Start": start_month,
"End": start_month + (epic.get("estimated_sprints", 4) * 2),
"Capability Area": domain_label(epic.get("subdomain_group") or ""),
})
start_month += duration
if not rows:
return
df = pd.DataFrame(rows)
df["Start_Date"] = pd.to_datetime("2025-06-01") + pd.to_timedelta(df["Start"] * 30, unit="D")
df["End_Date"] = pd.to_datetime("2025-06-01") + pd.to_timedelta(df["End"] * 30, unit="D")
fig = px.timeline(
df,
x_start="Start_Date",
x_end="End_Date",
y="Initiative",
color="Phase",
hover_data=["Capability Area"],
title="Strategic Roadmap Timeline",
)
fig.update_layout(height=max(400, len(rows) * 25), showlegend=True)
fig.update_yaxes(autorange="reversed")
st.plotly_chart(fig, width='stretch')
def render_roadmap_tab(result: dict) -> None:
metrics = result.get("amd_metrics") or {}
render_amd_metrics(metrics)
st.divider()
phases = result.get("phases") or []
total_initiatives = sum(len(p.get("epics", [])) for p in phases)
st.markdown(
f"### Strategic Roadmap — {len(phases)} Phases · "
f"{total_initiatives} {label('epics')}"
)
render_gantt(phases)
compliance = result.get("compliance_summary") or {}
if compliance:
score = compliance.get("score", 0)
color = "green" if score >= 70 else "orange" if score >= 50 else "red"
st.markdown(f"**{label('compliance_score')}:** :{color}[{score} / 100]")
if compliance.get("standards_covered"):
st.caption(
f"{label('standards_covered')}: "
+ ", ".join(compliance["standards_covered"][:5])
)
render_drl_trace(result.get("drl_trace") or {})
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