Update project to use MLX for Apple Silicon support

Update dependencies for MLX models
Add automatic model selection based on available RAM
Rewrite README with MLX-specific documentation
Update .gitignore for MLX project structure

src/audio_summary/cli.py

@@ -0,0 +1,102 @@
+"""Command-line entrypoint.
+
+Download (optional), transcribe, and summarize audio/video files fully on
+Apple Silicon via MLX. Models are selected automatically from the detected
+unified memory (see ``config`` and ``device``).
+"""
+
+import argparse
+from pathlib import Path
+
+from .device import select_models
+from .download import download_from_youtube
+from .summarization import summarize_text
+from .transcription import transcribe_file
+
+DEFAULT_LANGUAGE = "en"  # Use "auto" on the CLI for automatic detection.
+
+
+def _resolve_language(arg_language: str | None) -> str | None:
+    """Map the CLI ``--language`` value to a code or ``None`` (auto-detect)."""
+    language = arg_language if arg_language else DEFAULT_LANGUAGE
+    if language and language.lower() == "auto":
+        return None
+    return language
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Download, transcribe, and summarize audio or video files "
+        "on Apple Silicon (MLX)."
+    )
+    group = parser.add_mutually_exclusive_group(required=True)
+    group.add_argument("--from-youtube", type=str, help="YouTube URL to download.")
+    group.add_argument("--from-local", type=str, help="Path to the local audio file.")
+    parser.add_argument(
+        "--output",
+        type=str,
+        default="./summary.md",
+        help="Output markdown file path.",
+    )
+    parser.add_argument(
+        "--transcript-only",
+        action="store_true",
+        help="Only transcribe the file, do not summarize.",
+    )
+    parser.add_argument(
+        "--language",
+        type=str,
+        help="Language code for transcription (e.g. 'en', 'fr', 'es', "
+        "or 'auto' for detection).",
+    )
+    args = parser.parse_args()
+
+    language = _resolve_language(args.language)
+
+    # --- Automatic model selection based on detected unified memory ---
+    ram_gb, tier = select_models()
+    print("=" * 60)
+    print(f"Detected unified memory : {ram_gb:.1f} GB")
+    print(f"Selected hardware tier  : >= {tier.min_ram_gb} GB")
+    print(f"STT model               : {tier.stt.repo} ({tier.stt.engine})")
+    print(f"Summarization model     : {tier.summarization_repo}")
+    print("=" * 60)
+
+    # --- Working directory for intermediate files ---
+    data_directory = Path("tmp")
+    if not data_directory.exists():
+        data_directory.mkdir(parents=True)
+        print(f"Created directory: {data_directory}")
+
+    # --- Resolve input file ---
+    if args.from_youtube:
+        print(f"Downloading YouTube video from {args.from_youtube}")
+        file_path = download_from_youtube(args.from_youtube, str(data_directory))
+    else:
+        file_path = Path(args.from_local)
+
+    # --- Transcription ---
+    print(f"Transcribing file: {file_path}")
+    transcript = transcribe_file(
+        str(file_path),
+        str(data_directory / "transcript.txt"),
+        tier.stt,
+        language,
+    )
+
+    if args.transcript_only:
+        print("Transcription complete. Skipping summary generation.")
+        return
+
+    # --- Summarization ---
+    print("Generating summary...")
+    summary = summarize_text(transcript, tier.summarization_repo)
+
+    with open(args.output, "w") as md_file:
+        md_file.write("# Summary\n\n")
+        md_file.write(summary)
+    print(f"Summary written to {args.output}")
+
+
+if __name__ == "__main__":
+    main()