Presentar: Sovereign AI Visualization & App Framework

Version: 0.1.0 | Status: Specification Complete

Presentar is a PURE WASM visualization and rapid application framework built entirely on Sovereign AI Stack primitives. It replaces Streamlit, Gradio, and Panel with 60fps GPU-accelerated rendering, compile-time type safety, and deterministic reproducibility.

Position in the Stack

┌─────────────────────────────────────────────────────────────────┐
│  Presentar (Visualization & Apps)           ← YOU ARE HERE     │
├─────────────────────────────────────────────────────────────────┤
│  Trueno-Viz (GPU Rendering Primitives)                         │
├─────────────────────────────────────────────────────────────────┤
│  Trueno (SIMD/GPU Compute) v0.7.3                               │
├─────────────────────────────────────────────────────────────────┤
│  Aprender (ML) | Realizar (Inference) | Alimentar (Data)       │
└─────────────────────────────────────────────────────────────────┘

Core Principles

Auto-Display: Convention Over Configuration

Presentar auto-generates UIs from Sovereign AI Stack file formats:

# Point at a directory, get an app
presentar --serve ./fraud-detector/

# Bundle for deployment
presentar --bundle ./fraud-detector/ -o app.wasm

YAML App Configuration

presentar: "0.1"
name: "fraud-detection-dashboard"
version: "1.0.0"

# Data sources (Alimentar .ald files)
data:
  transactions:
    source: "pacha://datasets/transactions:latest"
    format: "ald"
    refresh: "5m"

# Model references (Aprender .apr files)
models:
  fraud_detector:
    source: "pacha://models/fraud-detector:1.2.0"
    format: "apr"

# Layout definition (12-column responsive grid)
layout:
  type: "dashboard"
  columns: 12
  sections:
    - id: "metrics"
      span: [1, 4]
      widgets:
        - type: "metric"
          label: "Fraud Rate"
          value: "{{ data.predictions | filter(fraud=true) | percentage }}"

    - id: "main-chart"
      span: [5, 12]
      widgets:
        - type: "chart"
          chart_type: "line"
          data: "{{ data.transactions }}"
          x: "timestamp"
          y: "amount"

Quality Scoring

Every Presentar app receives a TDG score (0-100, F-A):

Integration with Batuta Workflow

Presentar apps integrate with Batuta’s 5-phase workflow:

Phase 1: Analysis    → presentar analyze app.yaml
Phase 2: Transpile   → (N/A - pure Rust)
Phase 3: Optimize    → presentar optimize --wasm-opt
Phase 4: Validate    → presentar test (zero-dep harness)
Phase 5: Deploy      → presentar --bundle → pacha publish

presentar-test: Zero-Dependency E2E Testing

Critical constraint: No playwright, selenium, npm, or C bindings.

#![allow(unused)]
fn main() {
use presentar_test::*;

#[presentar_test]
fn inference_flow() {
    let mut h = Harness::new(include_bytes!("fixtures/app.tar"));
    h.type_text("[data-testid='input-amount']", "1500")
     .click("[data-testid='predict-btn']");
    h.assert_text_contains("[data-testid='result']", "Fraud Score:");
}

#[presentar_test]
fn visual_regression() {
    let mut h = Harness::new(include_bytes!("fixtures/app.tar"));
    Snapshot::assert_match("app-default", h.screenshot("[data-testid='app-root']"), 0.001);
}
}

Determinism guarantees:

• Fixed DPI: 1.0
• Font antialiasing: Grayscale only
• Fixed viewport: 1280x720
• Embedded test font (Inter)

Trueno-Viz GPU Primitives

Presentar renders via Trueno-Viz draw commands:

#![allow(unused)]
fn main() {
pub enum DrawCommand {
    Path { points: Vec<Point>, closed: bool, style: StrokeStyle },
    Fill { path: PathRef, color: Color, rule: FillRule },
    Rect { bounds: Rect, radius: CornerRadius, style: BoxStyle },
    Text { content: String, position: Point, style: TextStyle },
    Image { tensor: TensorRef, bounds: Rect, sampling: Sampling },
}
}

Anti-aliasing strategy:

• Hardware MSAA (4x) for fills
• Analytical AA for lines/curves
• SDF for text rendering

Pacha Registry Integration

# Fetch models and datasets from Pacha
models:
  classifier:
    source: "pacha://models/mnist-cnn:1.0.0"

data:
  training:
    source: "pacha://datasets/mnist:latest"

Lineage tracking follows W3C PROV-DM for full provenance.

Performance Targets

Ruchy Script Integration (Future)

Embedded scripting for dynamic behavior:

scripts:
  on_load: |
    let data = load_dataset("transactions")
    let filtered = data.filter(|row| row.amount > 100)
    set_state("filtered_data", filtered)

Security: Resource limits (1M instructions, 16MB memory, 10ms slice) prevent DoS.

Comparison with Alternatives

presentar-terminal: Native TUI Backend

For terminal-based applications, presentar-terminal provides efficient character-cell rendering with the same Brick Architecture as the WASM stack.

Architecture

┌─────────────────────────────────────────────────────────────────┐
│  presentar-terminal (TUI)                                       │
├─────────────────────────────────────────────────────────────────┤
│  CellBuffer + DiffRenderer (efficient updates)                  │
├─────────────────────────────────────────────────────────────────┤
│  crossterm 0.28 (terminal control)                              │
└─────────────────────────────────────────────────────────────────┘

Key Components

Example Usage

#![allow(unused)]
fn main() {
use presentar_terminal::{CellBuffer, Color, DiffRenderer, Modifiers};

// Create buffer
let mut buffer = CellBuffer::new(80, 24);

// Write colored text
buffer.update(0, 0, "H", Color::GREEN, Color::TRANSPARENT, Modifiers::NONE);
buffer.update(1, 0, "i", Color::GREEN, Color::TRANSPARENT, Modifiers::NONE);

// Render to terminal with diff optimization
let mut renderer = DiffRenderer::new();
renderer.flush(&mut buffer, &mut std::io::stdout())?;
}

Widgets Available

• Table: Data tables with sorting and selection
• Gauge: Progress bars and meters
• Sparkline: Inline mini-charts
• ForceGraph: Force-directed network visualization
• Treemap: Hierarchical data visualization
• Heatmap: 2D density visualization
• BoxPlot/ViolinPlot: Statistical distributions

Stack Dashboards

Batuta uses presentar-terminal for its TUI dashboards:

# Stack health dashboard
cargo run --example stack_graph_tui --features native

# Oracle RAG dashboard
cargo run --example rag_oracle_demo --features native

Why Not ratatui?

presentar-terminal replaces ratatui for stack consistency:

Academic Foundation

Key references (see full spec for 30+ citations):

• Czaplicki (2012): Elm Architecture
• Haas et al. (2017): WebAssembly performance model
• Mitchell et al. (2019): Model Cards
• Ohno (1988): Toyota Production System (Jidoka)

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