feat: add meeting transcription app scaffolding

- Add Python backend structure with FastAPI for transcription/summarization
- Add React UI with audio recording, transcript, and summary views
- Configure Tauri to manage Python backend lifecycle
- Set up Windows cross-compilation with cargo-xwin
- Add Gitea CI workflow for lint, test, and multi-platform builds
- Configure ESLint, Prettier, and Vitest for code quality

Note: App scaffolding only - Python env and models not yet set up

scripts/download_models.py

@@ -0,0 +1,90 @@
+"""Download required models for Chronara."""
+
+import os
+import sys
+from pathlib import Path
+from urllib.request import urlretrieve
+
+# Model download URLs
+MODELS = {
+    "llama-3.2-1B": {
+        "url": "https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_K_M.gguf",
+        "filename": "llama-3.2-1B-instruct-Q4_K_M.gguf",
+        "size": "1.2GB",
+    },
+    "llama-3.2-3B": {
+        "url": "https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF/resolve/main/Llama-3.2-3B-Instruct-Q4_K_M.gguf",
+        "filename": "llama-3.2-3B-instruct-Q4_K_M.gguf",
+        "size": "2.5GB",
+    },
+}
+
+
+def download_with_progress(url: str, filepath: Path):
+    """Download file with progress bar."""
+
+    def _progress(block_num, block_size, total_size):
+        downloaded = block_num * block_size
+        percent = min(downloaded * 100 / total_size, 100)
+        progress = int(50 * percent / 100)
+        sys.stdout.write(
+            f"\r[{'=' * progress}{' ' * (50 - progress)}] {percent:.1f}%"
+        )
+        sys.stdout.flush()
+
+    print(f"Downloading {filepath.name}...")
+    urlretrieve(url, filepath, reporthook=_progress)
+    print()  # New line after progress bar
+
+
+def main():
+    """Download all required models."""
+    # Get project root
+    project_root = Path(__file__).parent.parent
+    models_dir = project_root / "models"
+    models_dir.mkdir(exist_ok=True)
+
+    print("🤖 Chronara Model Downloader")
+    print("=" * 50)
+
+    # Ask which model to download
+    print("\nWhich Llama model would you like to use?")
+    print("1. Llama 3.2 1B (1.2GB) - Faster, good for basic summaries")
+    print("2. Llama 3.2 3B (2.5GB) - Better quality summaries")
+    print("3. Both models")
+
+    choice = input("\nEnter your choice (1/2/3): ").strip()
+
+    models_to_download = []
+    if choice == "1":
+        models_to_download = ["llama-3.2-1B"]
+    elif choice == "2":
+        models_to_download = ["llama-3.2-3B"]
+    elif choice == "3":
+        models_to_download = ["llama-3.2-1B", "llama-3.2-3B"]
+    else:
+        print("Invalid choice!")
+        return
+
+    # Download selected models
+    for model_name in models_to_download:
+        model_info = MODELS[model_name]
+        filepath = models_dir / model_info["filename"]
+
+        if filepath.exists():
+            print(f"\n✓ {model_name} already downloaded")
+            continue
+
+        print(f"\n📥 Downloading {model_name} ({model_info['size']})...")
+        try:
+            download_with_progress(model_info["url"], filepath)
+            print(f"✓ Downloaded {model_name} successfully!")
+        except Exception as e:
+            print(f"✗ Failed to download {model_name}: {e}")
+
+    print("\n✨ Model download complete!")
+    print("\nNote: WhisperX models will be downloaded automatically on first run.")
+
+
+if __name__ == "__main__":
+    main()