title: TalentFlow
emoji: ✨
colorFrom: purple
colorTo: indigo
sdk: static
app_file: index.html
pinned: false
license: mit
short_description: Workforce planning and staffing simulation.
TalentFlow
Workforce Intelligence Studio
A professional, fully client-side workforce planning laboratory for examining capacity, staffing, skill readiness, employee turnover, and operating cost under synthetic scenarios.
TalentFlow is an original static Hugging Face Spaces application. It gives users an inspectable, editable demonstration environment for understanding staffing tradeoffs, with transparent calculations and no external services.
Short description: Workforce planning and staffing simulation.
Demonstration only. The eight example teams, workloads, labor costs, readiness scores, and attrition rates are fictional. They are not verified workforce data or validated forecasts. The application does not contain a trained machine learning model.
Features
| Workspace | Included capabilities |
|---|---|
| Command center | Executive metrics, monthly capacity versus demand, staffing-shortfall signals, and an at-a-glance comparison of scenario coverage. |
| Workforce forecast | 12-month projections for headcount equivalents, team-level and aggregate capacity, demand, labor cost, and unserved hours, with a CSV export. |
| Scenario studio | Four editable presets: steady state, growth acceleration, retention pressure, and productivity boost. Sliders control demand, annual attrition, quarterly hires, and productivity. Includes a deterministic 300-run sensitivity band and comparison CSV export. |
| Team registry | Create, edit, archive, restore, and search workforce teams. Customize workload, monthly growth, FTE, annual attrition, quarterly hires, per-FTE cost, and skill readiness. Export as CSV. |
| Methodology | In-app explanations of simulation equations, limitations, data handling, and portability. |
Additional features: light/dark mode, responsive desktop and mobile design, browser-local persistence, versioned JSON backup and import, and form validation.
Deploy on Hugging Face Spaces
- Open Create a new Space.
- Select owner
Hugdrigo. - Name the Space
talentflow-workforce-intelligence. - Choose Static SDK, Public visibility, and MIT license.
- Extract the ZIP and upload all three files (
README.md,index.html,LICENSE) directly to the Space repository root. Do not upload the ZIP itself. - Open the Space to check that it loads and test its settings and exports in the public deployment.
This README.md provides the Hugging Face Spaces configuration front matter. No build step, requirements.txt, package installation, API token, backend, GPU, or paid service is required.
Local operation
Open index.html in a current browser. For normal browser-local persistence, a standard HTTPS or localhost origin is preferable. Storage access can vary for local file:// pages. All calculations run in JavaScript without network requests.
Model definition
Each team has eight numeric attributes: current FTE, monthly workload hours, monthly demand growth %, annual attrition %, quarterly hires, cost per FTE per month in USD, and skill readiness %. Archived teams are excluded from simulations while remaining editable and restorable.
1. Effective capacity
The model assumes 140 productive base hours per FTE per month and a simplified readiness adjustment:
readiness_factor = 0.65 + 0.35 * readiness_percent / 100
monthly_capacity_hours = expected_FTE * 140 * readiness_factor
* (1 + productivity_uplift_percent / 100)
Skill readiness is an illustrative planning coefficient, not a directly measured employee performance multiplier.
2. Expected workforce evolution
The first month begins with the team's input FTE. For months 2 to 12:
annual_attrition_effective = clamp(team_attrition + scenario_attrition, 0%, 85%)
next_month_FTE = previous_month_FTE * (1 - annual_attrition_effective / 12)
For months 3, 6, 9, 12 only:
next_month_FTE += max(0, team_hires_per_quarter + scenario_hiring_adjustment)
FTE is a continuously valued expected headcount, so it can be fractional. This does not mean fractional employees are literally hired or dismissed. Quarterly hires are modeled as instantly productive when added.
3. Demand and coverage
month_m_demand = team_starting_demand * (1 + monthly_growth_rate)^(m - 1)
* (1 + scenario_demand_adjustment)
served_hours = min(month_m_demand, monthly_capacity_hours)
unserved_hours_by_team = max(0, month_m_demand - monthly_capacity_hours)
month_coverage = sum(served_hours across teams) / sum(demand across teams)
annual_coverage = sum(served_hours across all months and teams)
/ sum(demand across all months and teams)
If total demand is zero, coverage defaults to 100% by convention. Excess capacity in one team does not offset shortfall in a different team. Therefore, the sum of team-level unserved hours may exceed an aggregate max(total demand − total capacity, 0) calculation.
4. Labor spend and indicative staffing gap
monthly_labor_spend = sum(expected_FTE * assumed_monthly_cost_per_FTE)
first_month_FTE_gap_by_team = max(0, demand - capacity)
/ (140 * readiness_factor * productivity_factor)
Cost totals are illustrative, exclude recruiting and overhead expenses, and assume constant per-FTE wages. Gap estimates presume immediate availability and constant productivity; they are not operational hiring recommendations.
5. Four scenario presets
| Preset | Demand adjustment | Attrition adjustment | Quarterly hires adjustment | Productivity uplift |
|---|---|---|---|---|
| Steady state | 0% | 0 pp | 0 | 0% |
| Growth acceleration | +30% | +2 pp | +1 per team | 0% |
| Retention pressure | +8% | +16 pp | 0 | 0% |
| Productivity boost | +12% | -2 pp | 0 | +22% |
Scenario adjustments are assumption changes, not probability-based forecasts. Slider overrides can be reset to the chosen preset.
6. Sensitivity illustration
The scenario studio performs 300 deterministic seeded calculations using the selected parameter values plus hypothetical, bounded perturbations in demand and attrition. The displayed P10, median and P90 of annual modeled coverage are not calibrated prediction intervals. Repeating the same dataset and parameters produces identical values.
Validation
The packaged application was tested locally using headless Chromium and independent Python calculations:
- 14,221 numerical assertions, including 220 randomized model comparisons of 12 months and five aggregate quantities against an independent Python implementation, annual coverage comparisons, parameter monotonicity, and deterministic sensitivity reproducibility.
- 47 functional checks, covering navigation, model calculations, team creation/editing/archiving/restoration, scenarios, slider overrides, CSV and JSON downloads, JSON backup validation and restoration, HTML escaping, CSV formula escaping, browser-local state writes, reset, theme toggling, and six viewport widths.
- 35 additional responsive checks, covering five views at seven viewport widths ranging from 320 to 1512 pixels, with no document-level horizontal overflow detected.
- No uncaught JavaScript errors detected in the tested workflows; JavaScript syntax check passed.
Scope: These are local software checks, not an external security assessment, a statistical validation of assumptions, a benchmark against human-resource outcomes, or a successful public Spaces deployment.
Data, safety, and limitations
- All app computations run in the browser. No external fonts, packages, data APIs, telemetry, or tracking are used.
- Per-browser persistence uses
localStorage, where supported; clearing browser data may erase unsaved edits. - Version 1 JSON import is limited to 300 KB and 1–40 teams. Values have type and range checks, teams must have unique IDs, and imports require user confirmation before replacement.
- Names and categories are escaped during HTML display. CSV output quotes cells and prefixes formulas or suspicious spreadsheet formula triggers with an apostrophe. This is a limited defensive measure, not a complete security audit.
- The application does not access live workforce systems, salaries, HR records, skills assessments, staffing vendors, payroll APIs, or employee-specific personal data.
- It does not model labor-law constraints, hiring pipelines, recruiting lead times, employee absence, part-time scheduling, holidays, time-zone constraints, service-level routing, staff ramp-up, or performance outcomes.
- For real organizational use, validate model assumptions, anonymize or aggregate data, and review decisions with qualified professionals.
Files
README.md Documentation and Hugging Face Space metadata
index.html Complete offline application, interface and simulation engine
LICENSE MIT License
License: MIT. See LICENSE.
Portfolio: Hugdrigo
Project: 95 · TalentFlow