Robert Helewka
64fb83257d
- Add 202512_GenesysCX TEI study (config, seed data, notebooks, README) with NPV $10.8M / ROI 266% including AI-token cost line - Add explicit `key` parameter to all chart wrappers in app/components to prevent StreamlitDuplicateElementId errors when the same figure type renders across Summary/Benefits/Costs tabs - Render benefits bar and cost pie charts on their respective tabs - Add benefits_vs_costs_by_year chart wrapper
132 lines
4.0 KiB
Python
132 lines
4.0 KiB
Python
"""
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Excel / CSV / JSON export.
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Excel uses openpyxl via pandas — multi-sheet workbooks readable in
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Excel 2019+. JSON round-trips the full input state (sites, takeouts,
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feature scopes) so a scenario can be saved and reloaded.
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"""
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from __future__ import annotations
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import dataclasses
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import json
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from pathlib import Path
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import pandas as pd
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from .inputs import CostTakeout, FeatureScope, SiteInput
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from .meters import Confidence, TokenMeter
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from .rollout import RolloutPlan
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def meters_dataframe(meters: dict[str, TokenMeter]) -> pd.DataFrame:
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"""Meter catalogue as a display/export-ready DataFrame."""
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return pd.DataFrame(
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[
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{
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"feature": m.feature,
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"meter_type": m.meter_type.value,
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"units_per_token": m.units_per_token or None,
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"tokens_per_unit": m.tokens_per_unit,
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"confidence": f"{m.confidence.icon} {m.confidence.value}",
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"notes": m.notes,
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"source": m.source_url or "",
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}
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for m in meters.values()
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]
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)
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def sites_dataframe(sites: list[SiteInput]) -> pd.DataFrame:
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rows = []
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for s in sites:
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d = dataclasses.asdict(s)
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d["languages"] = ", ".join(d["languages"])
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rows.append(d)
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return pd.DataFrame(rows)
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def export_excel(
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sheets: dict[str, pd.DataFrame],
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path: str | Path,
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) -> Path:
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"""Write a multi-sheet Excel workbook. Sheet names are truncated to
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Excel's 31-character limit."""
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path = Path(path)
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path.parent.mkdir(parents=True, exist_ok=True)
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with pd.ExcelWriter(path, engine="openpyxl") as writer:
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for name, df in sheets.items():
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df.to_excel(writer, sheet_name=name[:31], index=False)
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return path
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def export_csv(df: pd.DataFrame, path: str | Path) -> Path:
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path = Path(path)
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path.parent.mkdir(parents=True, exist_ok=True)
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df.to_csv(path, index=False)
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return path
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# ── JSON scenario save / load ────────────────────────────────────────
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def scenario_state_to_json(
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sites: list[SiteInput],
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takeouts: list[CostTakeout],
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feature_scopes: list[FeatureScope],
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path: str | Path | None = None,
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rollout: RolloutPlan | None = None,
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) -> str:
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"""Serialize the full input state; optionally write to ``path``."""
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state = {
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"sites": [dataclasses.asdict(s) for s in sites],
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"takeouts": [
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{**dataclasses.asdict(t), "confidence": t.confidence.value}
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for t in takeouts
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],
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"feature_scopes": [
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{
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**dataclasses.asdict(f),
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"adoption_curve": {str(k): v for k, v in f.adoption_curve.items()},
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}
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for f in feature_scopes
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],
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}
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if rollout is not None:
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state["rollout"] = dataclasses.asdict(rollout)
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text = json.dumps(state, indent=2)
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if path is not None:
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Path(path).write_text(text)
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return text
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def scenario_state_from_json(
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source: str | Path,
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) -> tuple[list[SiteInput], list[CostTakeout], list[FeatureScope], RolloutPlan | None]:
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"""Inverse of :func:`scenario_state_to_json`. ``source`` is a JSON
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string or a file path. The fourth element is None for legacy files
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saved without a rollout plan."""
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raw = (
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Path(source).read_text()
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if isinstance(source, Path) or (isinstance(source, str) and source.strip().endswith(".json"))
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else str(source)
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)
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state = json.loads(raw)
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sites = [SiteInput(**s) for s in state["sites"]]
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takeouts = [
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CostTakeout(**{**t, "confidence": Confidence(t["confidence"])})
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for t in state["takeouts"]
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]
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scopes = [
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FeatureScope(
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**{
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**f,
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"adoption_curve": {int(k): v for k, v in f["adoption_curve"].items()},
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}
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)
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for f in state["feature_scopes"]
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]
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rollout = (
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RolloutPlan(**state["rollout"]) if "rollout" in state else None
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)
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return sites, takeouts, scopes, rollout
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