refactor: assemble inference engines through a shared composition root

- add build_engine() to astrai.inference.engine as the single load-place-wire path for InferenceEngine, accepting a checkpoint path or live model/tokenizer plus passthrough engine kwargs
- migrate the server lifespan, generate CLI, humaneval/ifeval evals, and all three demos to build_engine; app._create_engine collapses into a direct call
- export build_engine from astrai and astrai.inference
- parameterize the autoregressive demo with --prompt one-shot continuation plus model path and sampling knobs, exiting cleanly on !exit or EOF
- cover the composition root with unit tests for live-object assembly, kwargs passthrough, and argument validation

tests/inference/test_server.py

@@ -5,8 +5,7 @@ from pathlib import Path
 import pytest
 import torch
 
-from astrai.inference import get_app
-from astrai.inference.network.app import _create_engine
+from astrai.inference import build_engine, get_app
 from astrai.model.transformer import AutoRegressiveLM
 from astrai.serialization import save_model
 from tests.helpers import CHAT_TEMPLATE, build_test_tokenizer, make_tiny_config
@@ -238,7 +237,7 @@ def test_chat_completions_real_engine(tmp_path, client):
     tokenizer = build_test_tokenizer(vocab_size=256, chat_template=CHAT_TEMPLATE)
     tokenizer.save_pretrained(str(tmp_path))
 
-    engine = _create_engine(
+    engine = build_engine(
         Path(tmp_path),
         device="cpu",
         dtype=torch.float32,