README.md

metadata

title: PDC Intelligence
emoji: 🧵
colorFrom: purple
colorTo: blue
sdk: docker
sdk_version: '3.11'
app_port: 7860
pinned: false

PDC Intelligence Prototype (MVP)

This prototype is built under PDC/ and aligned to the PDC meeting notes and process documents.

Scope Covered

• Phase-1 focus: greige construction recommendation from finish-side requirements.
• Dashboard for dataset health, coverage, and core relativity metrics.
• Article search with filters and article-level detail.
• Construction calculator with optimistic/expected/pessimistic scenarios.
• Relativity analytics by weave/blend and numeric correlation matrix.
• Feasibility process flow mapping from meeting/process docs.
• Built-in validation report endpoint for ongoing prediction quality checks.

Folder Layout

• backend/: FastAPI APIs and statistical engine.
• frontend/: Next.js dashboard and calculator UI.
• backend/app/data/: copied data files for runtime.

Backend Run

cd backend
python3 -m venv .venv
.venv/bin/pip install -r requirements.txt
.venv/bin/uvicorn app.main:app --reload --port 8000

Frontend Run

cd frontend
npm install
npm run dev

Frontend rewrites /api/* to backend http://localhost:8000/api/*.

One-command Run

From PDC/:

./run_app.sh

This script will:

• create backend venv if missing
• install backend dependencies
• run backend tests before startup
• install frontend dependencies
• start backend and frontend together

Verification Performed

• Unit tests:
  • formula correctness checks
  • prediction response contract checks
  • holdout MAE threshold checks
  • validation report generation checks

Run tests:

cd backend
.venv/bin/python -m pytest -q

Current run: 3 passed

Validation API:

GET /api/validation/report?sample_size=250&seed=42

Returns MAE, error percentiles, and confidence distribution.

Data source audit API:

GET /api/data-source/audit

Returns source file usage, raw vs used row counts, dataset distribution, and column-level null coverage.

Predictive Validation Snapshot

On a 300-row holdout sample:

• MAE (Greige EPI): 13.92
• MAE (Greige PPI): 8.132
• P90 absolute error (Greige EPI): 33.0
• P90 absolute error (Greige PPI): 20.0

These are baseline MVP metrics and can be improved by adding tighter cluster filters (count band, loom, weave aliases) and weighted neighbors.