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2d52135 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 | """Command-line entry points for mini_transformer."""
from __future__ import annotations
import argparse
import os
import subprocess
import sys
from collections.abc import Sequence
from pathlib import Path
import uvicorn
from omegaconf import DictConfig
from .apps import server as server_module
from .inference import run_inference
from .model_loader import (
CONFIG_DIR_ENV,
CONFIG_NAME_ENV,
MODEL_NAME_ENV,
MODELS_ENV,
compose_config_from_dir,
compose_model_config,
ensure_models_root,
list_model_names,
snapshot_to_local,
)
DEFAULT_CONFIG_NAME = "config_inference"
__all__ = ["infer_main", "serve_main", "ui_main", "fetch_main"]
def _select_model(model: str | None) -> str:
ensure_models_root()
names = list_model_names()
if not names:
raise SystemExit(
"No models found in `trained_models/`. Place a model folder with a configs/config_inference.yaml file there first."
)
if model and model in names:
return model
if model:
raise SystemExit(f"Model '{model}' not found. Available models: {', '.join(names)}.")
return names[0]
def infer_main(argv: Sequence[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Run mini_transformer inference")
parser.add_argument("-t", "--input-text", help="Prompt to feed the model")
parser.add_argument("-m", "--model", help="Model folder under trained_models/")
parser.add_argument("--model-root", help="Override path to trained_models root")
parser.add_argument(
"-c", "--config-dir", help="Use Hydra configs from this directory instead of a model folder"
)
parser.add_argument(
"--config-name",
default=os.environ.get(CONFIG_NAME_ENV, DEFAULT_CONFIG_NAME),
help="Hydra config name to compose (default: config_inference)",
)
parser.add_argument(
"-o",
"--override",
action="append",
default=[],
help="Hydra override in dotlist form (e.g. generation.temperature=0.7)",
)
args = parser.parse_args(argv)
overrides = args.override or []
if args.model_root:
os.environ[MODELS_ENV] = args.model_root
if args.config_dir or os.environ.get(CONFIG_DIR_ENV):
config_dir = args.config_dir or os.environ.get(CONFIG_DIR_ENV)
if config_dir is None:
raise SystemExit("Config directory not provided")
cfg: DictConfig = compose_config_from_dir(
config_dir,
config_name=args.config_name,
overrides=overrides,
)
else:
model_name = _select_model(args.model)
cfg = compose_model_config(
model_name,
config_name=args.config_name,
overrides=overrides,
)
if args.input_text:
cfg.input_text = args.input_text
outputs = run_inference(cfg)
print("\n".join(outputs))
return 0
def serve_main(argv: Sequence[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Launch the FastAPI inference server")
parser.add_argument("--host", default="0.0.0.0", help="Bind address (default: 0.0.0.0)")
parser.add_argument("--port", type=int, default=8000, help="Port for uvicorn (default: 8000)")
parser.add_argument("--reload", action="store_true", help="Enable auto-reload")
parser.add_argument("-m", "--model", help="Model folder under trained_models/")
parser.add_argument("--model-root", help="Override path to trained_models root")
parser.add_argument(
"-c", "--config-dir", help="Use configs from this directory instead of a model folder"
)
parser.add_argument(
"--config-name",
default=os.environ.get(CONFIG_NAME_ENV, DEFAULT_CONFIG_NAME),
help="Hydra config name to compose",
)
args = parser.parse_args(argv)
if args.model_root:
os.environ[MODELS_ENV] = args.model_root
if args.config_dir:
os.environ[CONFIG_DIR_ENV] = args.config_dir
os.environ.pop(MODEL_NAME_ENV, None)
else:
model_name = _select_model(args.model)
os.environ[MODEL_NAME_ENV] = model_name
os.environ.pop(CONFIG_DIR_ENV, None)
os.environ[CONFIG_NAME_ENV] = args.config_name
uvicorn.run(
"mini_transformer.apps.server:app",
host=args.host,
port=args.port,
reload=args.reload,
)
return 0
def ui_main(argv: Sequence[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Launch the Chainlit chat UI")
parser.add_argument("--model-root", help="Override path to trained_models root")
parser.add_argument("-m", "--model", help="Pre-select a model when the UI starts")
parser.add_argument(
"-c", "--config-dir", help="Use configs from this directory instead of trained_models"
)
parser.add_argument(
"--host",
default=os.environ.get("MINI_TRANSFORMER_UI_HOST", "127.0.0.1"),
help="Host to bind Chainlit to (default: 127.0.0.1)",
)
parser.add_argument(
"--port",
type=int,
default=int(os.environ.get("MINI_TRANSFORMER_UI_PORT", 8000)),
help="Port for Chainlit (default: 8000)",
)
parser.add_argument(
"--config-name",
default=os.environ.get(CONFIG_NAME_ENV, DEFAULT_CONFIG_NAME),
help="Hydra config name to compose",
)
args = parser.parse_args(argv)
env = os.environ.copy()
if args.model_root:
env[MODELS_ENV] = args.model_root
if args.config_dir:
env[CONFIG_DIR_ENV] = args.config_dir
if args.model:
env[MODEL_NAME_ENV] = args.model
env[CONFIG_NAME_ENV] = args.config_name
env.setdefault("MINI_TRANSFORMER_UI_HOST", args.host)
env.setdefault("MINI_TRANSFORMER_UI_PORT", str(args.port))
app_path = Path(server_module.__file__).with_name("chainlit_app.py")
cmd = [
"chainlit",
"run",
str(app_path),
"--host",
args.host,
"--port",
str(args.port),
]
try:
proc = subprocess.run(cmd, env=env)
except FileNotFoundError as exc: # pragma: no cover
raise SystemExit(
"The 'chainlit' executable is missing. Install mini-transformer[server] to run the UI."
) from exc
return proc.returncode
def fetch_main(argv: Sequence[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Download a model from the Hugging Face Hub")
parser.add_argument(
"model_id", help="Repo ID on Hugging Face Hub (e.g. AlaBoussoffara/transformer_small)"
)
parser.add_argument(
"-n",
"--name",
help="Local directory name under trained_models/ (default derived from model id)",
)
parser.add_argument("--revision", help="Optional revision or branch to download")
parser.add_argument("--repo-type", default="model", help="Hub repo type (default: model)")
parser.add_argument("--token", help="Authentication token for private repos")
parser.add_argument("--cache-dir", help="Custom Hugging Face cache directory")
parser.add_argument("--model-root", help="Override path to trained_models root")
parser.add_argument("--force", action="store_true", help="Overwrite existing local directory")
args = parser.parse_args(argv)
if args.model_root:
os.environ[MODELS_ENV] = args.model_root
try:
destination = snapshot_to_local(
args.model_id,
local_name=args.name,
revision=args.revision,
repo_type=args.repo_type,
token=args.token,
cache_dir=args.cache_dir,
force=args.force,
)
except FileExistsError as exc: # pragma: no cover
raise SystemExit(str(exc)) from exc
print(f"Downloaded {args.model_id} → {destination}")
config_path = destination / "configs" / f"{DEFAULT_CONFIG_NAME}.yaml"
if not config_path.exists():
print(
f"[warning] Expected config file {config_path} not found. The inference CLI requires this file.",
file=sys.stderr,
)
return 0
if __name__ == "__main__": # pragma: no cover
sys.exit(infer_main())
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