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| """`hcc-meat` command-line interface. | |
| hcc-meat code <note.txt> — run the pipeline on a single note, print JSON | |
| hcc-meat gallery — dump the precomputed demo gallery | |
| hcc-meat serve [--port 7860] — run the FastAPI backend with uvicorn | |
| """ | |
| from __future__ import annotations | |
| import json | |
| import sys | |
| from pathlib import Path | |
| import typer | |
| from rich.console import Console | |
| from rich.table import Table | |
| from app.pipeline import Pipeline | |
| from app.pipeline.types import Note | |
| app = typer.Typer(help=__doc__, no_args_is_help=True) | |
| console = Console() | |
| def code( | |
| note_path: Path = typer.Argument(..., help="Path to a plain-text clinical note."), | |
| json_out: bool = typer.Option(False, "--json", help="Emit full JSON."), | |
| ) -> None: | |
| text = note_path.read_text() | |
| result = Pipeline().run(Note(note_id=note_path.stem, text=text)) | |
| if json_out: | |
| sys.stdout.write(result.model_dump_json(indent=2)) | |
| sys.stdout.write("\n") | |
| return | |
| table = Table(title=f"HCC suggestions for {note_path.name}") | |
| table.add_column("HCC") | |
| table.add_column("Label") | |
| table.add_column("RAF") | |
| table.add_column("Conf") | |
| table.add_column("Trump", justify="center") | |
| table.add_column("Risk") | |
| table.add_column("MEAT (MEAT)") | |
| for c in result.candidates: | |
| cov = c.meat_coverage() | |
| meat_str = "".join( | |
| cat.value if cov[cat] else "·" for cat in list(cov.keys()) | |
| ) | |
| table.add_row( | |
| c.hcc, | |
| c.hcc_label, | |
| f"{c.raf_weight:.3f}", | |
| f"{c.confidence_calibrated:.2f}", | |
| f"↓{c.hierarchy.trumped_by}" if c.hierarchy.trumped else "—", | |
| c.risk.level.upper() if c.risk else "?", | |
| meat_str, | |
| ) | |
| console.print(table) | |
| console.print(f"[dim]verifier drops: {result.verifier_drops} | latency: {result.latency_ms:.1f} ms[/dim]") | |
| def gallery( | |
| fixture: Path = typer.Option( | |
| Path("tests/fixtures/regression_suite.json"), | |
| help="Fixture file to run against.", | |
| ) | |
| ) -> None: | |
| cases = json.loads(fixture.read_text())["cases"] | |
| pipe = Pipeline() | |
| out = [] | |
| for c in cases: | |
| result = pipe.run(Note(note_id=c["case_id"], text=c["note_text"])) | |
| out.append({ | |
| "case_id": c["case_id"], | |
| "result": result.model_dump(), | |
| }) | |
| sys.stdout.write(json.dumps(out, indent=2)) | |
| sys.stdout.write("\n") | |
| def serve( | |
| host: str = typer.Option("0.0.0.0", help="Bind host."), | |
| port: int = typer.Option(7860, help="Bind port (7860 is HF Space default)."), | |
| reload: bool = typer.Option(False, help="uvicorn --reload for dev."), | |
| ) -> None: | |
| import uvicorn | |
| uvicorn.run("app.api.server:app", host=host, port=port, reload=reload) | |
| if __name__ == "__main__": | |
| app() | |