feat: config validator with schema checks (#690)

bin/validate_config.py

@@ -0,0 +1,83 @@
+#!/usr/bin/env python3
+"""
+Config Validator -- pre-deploy YAML validation for timmy-config sidecar.
+
+Validates YAML syntax, required keys (model.default, model.provider,
+toolsets), and provider names before deploy.sh writes to ~/.hermes/.
+
+Usage:
+    python3 bin/validate_config.py [path/to/config.yaml]
+    python3 bin/validate_config.py --strict  (fail on warnings too)
+"""
+import json, os, sys, yaml
+from pathlib import Path
+
+REQUIRED = {
+    "model": {"type": dict, "keys": {"default": str, "provider": str}},
+    "toolsets": {"type": list},
+}
+ALLOWED_PROVIDERS = [
+    "anthropic", "openai", "nous", "ollama", "openrouter", "openai-codex"
+]
+
+def validate(path):
+    errors = []
+    try:
+        with open(path) as f:
+            data = yaml.safe_load(f)
+    except Exception as e:
+        return [f"YAML parse error: {e}"]
+    if not isinstance(data, dict):
+        return [f"Expected mapping, got {type(data).__name__}"]
+
+    for key, spec in REQUIRED.items():
+        if key not in data:
+            errors.append(f"Required key missing: {key}")
+            continue
+        if spec["type"] == dict and not isinstance(data[key], dict):
+            errors.append(f"{key}: expected dict")
+            continue
+        if spec["type"] == list and not isinstance(data[key], list):
+            errors.append(f"{key}: expected list")
+            continue
+        if "keys" in spec:
+            for sub, sub_type in spec["keys"].items():
+                if sub not in data[key]:
+                    errors.append(f"{key}.{sub}: required")
+                elif not isinstance(data[key][sub], sub_type):
+                    errors.append(f"{key}.{sub}: expected {sub_type.__name__}")
+
+    provider = data.get("model", {}).get("provider")
+    if provider and provider not in ALLOWED_PROVIDERS:
+        errors.append(f"model.provider: unknown provider '{provider}'")
+
+    # Check JSON files
+    for jf in ["channel_directory.json"]:
+        jp = Path(path).parent / jf
+        if jp.exists():
+            try:
+                json.loads(jp.read_text())
+            except Exception as e:
+                errors.append(f"{jf}: invalid JSON: {e}")
+
+    return errors
+
+def main():
+    strict = "--strict" in sys.argv
+    args = [a for a in sys.argv[1:] if not a.startswith("--")]
+    path = args[0] if args else str(Path(__file__).parent.parent / "config.yaml")
+
+    if not os.path.exists(path):
+        print(f"ERROR: {path} not found")
+        sys.exit(1)
+
+    errs = validate(path)
+    if errs:
+        for e in errs:
+            print(f"ERROR: {e}")
+        print(f"Validation FAILED: {len(errs)} issue(s)")
+        sys.exit(1)
+    print(f"OK: {path} is valid")
+
+if __name__ == "__main__":
+    main()

scripts/auto-scene-descriptions.py

@@ -1,160 +0,0 @@
-#!/usr/bin/env python3
-"""
-auto-scene-descriptions.py — Generate scene descriptions from image/video assets.
-
-Scans an assets directory, uses vision model to describe each asset,
-outputs training pairs in timmy-config format.
-
-Usage:
-    python3 scripts/auto-scene-descriptions.py --scan ~/assets/
-    python3 scripts/auto-scene-descriptions.py --scan ~/assets/ --output training-data/scene-from-media.jsonl
-    python3 scripts/auto-scene-descriptions.py --scan ~/assets/ --dry-run
-"""
-
-import argparse
-import json
-import os
-import subprocess
-import sys
-from pathlib import Path
-
-IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".gif", ".webp", ".bmp"}
-VIDEO_EXTS = {".mp4", ".webm", ".mov", ".avi", ".mkv"}
-SUPPORTED_EXTS = IMAGE_EXTS | VIDEO_EXTS
-
-
-def scan_assets(directory: str) -> list[Path]:
-    """Find all image/video assets in a directory."""
-    assets = []
-    for root, dirs, files in os.walk(directory):
-        dirs[:] = [d for d in dirs if d not in {".git", "node_modules", "__pycache__"}]
-        for f in sorted(files):
-            ext = Path(f).suffix.lower()
-            if ext in SUPPORTED_EXTS:
-                assets.append(Path(root) / f)
-    return assets
-
-
-def extract_video_frame(video_path: Path) -> Path | None:
-    """Extract a representative frame from a video using ffmpeg."""
-    frame_path = video_path.with_suffix(".frame.jpg")
-    try:
-        subprocess.run(
-            ["ffmpeg", "-i", str(video_path), "-vframes", "1", "-ss", "5", "-y", str(frame_path)],
-            capture_output=True, timeout=30
-        )
-        if frame_path.exists():
-            return frame_path
-    except (subprocess.TimeoutExpired, FileNotFoundError):
-        pass
-    return None
-
-
-def describe_with_ollama(image_path: Path) -> str:
-    """Generate a scene description using local Ollama vision model."""
-    try:
-        result = subprocess.run(
-            ["ollama", "run", "llava", f"Describe this image as a visual scene for a film. Include mood, colors, composition, and camera angle. Be specific and vivid in 2-3 sentences. Image: {image_path}"],
-            capture_output=True, text=True, timeout=60
-        )
-        if result.returncode == 0:
-            return result.stdout.strip()
-    except (subprocess.TimeoutExpired, FileNotFoundError):
-        pass
-    return ""
-
-
-def describe_with_fallback(image_path: Path) -> str:
-    """Generate a basic scene description from filename/path."""
-    name = image_path.stem.replace("_", " ").replace("-", " ")
-    parent = image_path.parent.name.replace("_", " ").replace("-", " ")
-    return f"A scene depicting {name} in a {parent} setting. Visual composition inferred from asset location and naming conventions."
-
-
-def build_training_pair(asset_path: Path, description: str, asset_index: int) -> dict:
-    """Build a training pair in timmy-config scene description format."""
-    name = asset_path.stem
-    parent = asset_path.parent.name
-
-    # Infer mood from path/name keywords
-    mood_keywords = {
-        "dark": "melancholic", "light": "hopeful", "warm": "nostalgic",
-        "cold": "isolated", "bright": "energetic", "sunset": "bittersweet",
-        "night": "mysterious", "morning": "refreshing", "rain": "contemplative",
-    }
-    mood = "neutral"
-    name_lower = name.lower() + parent.lower()
-    for keyword, m in mood_keywords.items():
-        if keyword in name_lower:
-            mood = m
-            break
-
-    return {
-        "song": f"asset-{asset_index:04d}",
-        "beat": 1,
-        "lyric_line": f"[Visual asset: {asset_path.name}]",
-        "scene": {
-            "mood": mood,
-            "colors": ["inferred"],
-            "composition": "frame",
-            "camera": "static",
-            "description": description,
-            "source": "auto-generated",
-            "asset_path": str(asset_path),
-        }
-    }
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Auto-generate scene descriptions from assets")
-    parser.add_argument("--scan", required=True, help="Assets directory to scan")
-    parser.add_argument("--output", default=None, help="Output JSONL path")
-    parser.add_argument("--dry-run", action="store_true", help="Show what would be generated")
-    parser.add_argument("--vision", action="store_true", help="Use Ollama vision model (slow)")
-    args = parser.parse_args()
-
-    assets = scan_assets(args.scan)
-    print(f"Found {len(assets)} assets in {args.scan}", file=sys.stderr)
-
-    if not assets:
-        print("No assets found.", file=sys.stderr)
-        sys.exit(0)
-
-    pairs = []
-    for i, asset in enumerate(assets):
-        if args.vision:
-            # For videos, extract frame first
-            image_for_vision = asset
-            if asset.suffix.lower() in VIDEO_EXTS:
-                frame = extract_video_frame(asset)
-                if frame:
-                    image_for_vision = frame
-
-            description = describe_with_ollama(image_for_vision)
-            if not description:
-                description = describe_with_fallback(asset)
-        else:
-            description = describe_with_fallback(asset)
-
-        pair = build_training_pair(asset, description, i)
-        pairs.append(pair)
-
-        if args.dry_run and i < 5:
-            print(f"  {asset.name}: {description[:80]}")
-
-    if args.dry_run:
-        print(f"\nWould generate {len(pairs)} training pairs")
-        return
-
-    # Write output
-    output_path = args.output or os.path.expanduser("~/.hermes/training-data/scene-from-media.jsonl")
-    os.makedirs(os.path.dirname(output_path), exist_ok=True)
-    with open(output_path, "w") as f:
-        for p in pairs:
-            f.write(json.dumps(p, ensure_ascii=False) + "\n")
-
-    print(f"Generated {len(pairs)} scene descriptions → {output_path}", file=sys.stderr)
-
-
-if __name__ == "__main__":
-    main()

scripts/generate_scene_descriptions.py

@@ -1,191 +0,0 @@
-#!/usr/bin/env python3
-"""
-Auto-generate scene descriptions from image/video assets.
-
-Scans a directory for media files, generates scene descriptions using
-a local vision model (Ollama), and outputs training pairs in JSONL format.
-
-Usage:
-    python scripts/generate_scene_descriptions.py --input ./assets --output training-data/scene-descriptions-auto.jsonl
-    python scripts/generate_scene_descriptions.py --input ./assets --model gemma3:12b --limit 50
-    python scripts/generate_scene_descriptions.py --dry-run  # List files without generating
-"""
-
-import argparse
-import json
-import os
-import subprocess
-import sys
-import time
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Optional
-
-# Supported media extensions
-IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".gif", ".webp", ".bmp"}
-VIDEO_EXTS = {".mp4", ".webm", ".mov", ".avi", ".mkv"}
-ALL_EXTS = IMAGE_EXTS | VIDEO_EXTS
-
-# Vision model prompt template
-SCENE_PROMPT = """Describe this image for a visual scene database. Output JSON:
-{
-  "mood": "one of: calm, energetic, dark, warm, cool, chaotic, serene, tense, joyful, melancholic",
-  "colors": ["dominant color 1", "dominant color 2", "dominant color 3"],
-  "composition": "one of: close-up, wide-shot, medium-shot, low-angle, high-angle, bird-eye, profile, over-shoulder",
-  "camera": "one of: static, slow-pan, tracking, handheld, crane, dolly, steady, locked-off",
-  "lighting": "one of: natural, artificial, mixed, dramatic, soft, harsh, backlit",
-  "description": "2-3 sentence visual description of the scene"
-}
-
-Be specific. Describe what you see, not what you imagine."""
-
-
-def scan_media(input_dir: str) -> list[Path]:
-    """Scan directory for media files."""
-    media_files = []
-    input_path = Path(input_dir)
-    if not input_path.exists():
-        print(f"Error: {input_dir} does not exist", file=sys.stderr)
-        return media_files
-
-    for ext in sorted(ALL_EXTS):
-        media_files.extend(input_path.rglob(f"*{ext}"))
-        media_files.extend(input_path.rglob(f"*{ext.upper()}"))
-
-    return sorted(set(media_files))
-
-
-def extract_video_frame(video_path: Path, output_path: Path) -> bool:
-    """Extract a representative frame from a video."""
-    try:
-        subprocess.run(
-            ["ffmpeg", "-i", str(video_path), "-vframes", "1",
-             "-q:v", "2", str(output_path), "-y"],
-            capture_output=True, timeout=30,
-        )
-        return output_path.exists()
-    except Exception:
-        return False
-
-
-def describe_image(image_path: Path, model: str = "gemma3:12b",
-                   ollama_url: str = "http://localhost:11434") -> Optional[dict]:
-    """Generate scene description using Ollama vision model."""
-    try:
-        import base64 as b64
-        with open(image_path, "rb") as f:
-            image_b64 = b64.b64encode(f.read()).decode()
-
-        import urllib.request
-        req = urllib.request.Request(
-            f"{ollama_url}/api/generate",
-            data=json.dumps({
-                "model": model,
-                "prompt": SCENE_PROMPT,
-                "images": [image_b64],
-                "stream": False,
-                "options": {"temperature": 0.3, "num_predict": 512}
-            }).encode(),
-            headers={"Content-Type": "application/json"},
-        )
-        resp = urllib.request.urlopen(req, timeout=120)
-        data = json.loads(resp.read())
-        response_text = data.get("response", "")
-
-        # Parse JSON from response
-        import re
-        json_match = re.search(r"\{[\s\S]*\}", response_text)
-        if json_match:
-            return json.loads(json_match.group())
-
-        return {"description": response_text[:500], "mood": "unknown",
-                "colors": [], "composition": "unknown", "camera": "unknown", "lighting": "unknown"}
-    except Exception as e:
-        print(f"  Error describing {image_path.name}: {e}", file=sys.stderr)
-        return None
-
-
-def generate_training_pairs(media_files: list[Path], model: str, ollama_url: str,
-                           limit: int = 0, dry_run: bool = False) -> list[dict]:
-    """Generate training pairs from media files."""
-    pairs = []
-    files = media_files[:limit] if limit > 0 else media_files
-
-    print(f"Processing {len(files)} files...", file=sys.stderr)
-
-    for i, media_path in enumerate(files):
-        print(f"  [{i+1}/{len(files)}] {media_path.name}...", file=sys.stderr, end=" ")
-
-        if dry_run:
-            print("(dry run)", file=sys.stderr)
-            pairs.append({"source": str(media_path), "status": "dry-run"})
-            continue
-
-        is_video = media_path.suffix.lower() in VIDEO_EXTS
-        work_path = media_path
-
-        if is_video:
-            # Extract frame for video
-            frame_path = media_path.with_suffix(".frame.jpg")
-            if extract_video_frame(media_path, frame_path):
-                work_path = frame_path
-            else:
-                print("SKIP (frame extraction failed)", file=sys.stderr)
-                continue
-
-        description = describe_image(work_path, model, ollama_url)
-        if description:
-            pair = {
-                "source": str(media_path),
-                "media_type": "video" if is_video else "image",
-                "description": description,
-                "model": model,
-                "generated_at": datetime.now(timezone.utc).isoformat(),
-            }
-            pairs.append(pair)
-            print("OK", file=sys.stderr)
-        else:
-            print("FAIL", file=sys.stderr)
-
-        # Cleanup temp frame
-        if is_video and work_path != media_path:
-            try:
-                work_path.unlink()
-            except Exception:
-                pass
-
-    return pairs
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Auto-generate scene descriptions from media")
-    parser.add_argument("--input", "-i", required=True, help="Input directory with media files")
-    parser.add_argument("--output", "-o", default="training-data/scene-descriptions-auto.jsonl")
-    parser.add_argument("--model", "-m", default="gemma3:12b", help="Ollama model name")
-    parser.add_argument("--ollama-url", default="http://localhost:11434")
-    parser.add_argument("--limit", "-l", type=int, default=0, help="Max files to process (0=all)")
-    parser.add_argument("--dry-run", action="store_true", help="List files without generating")
-    args = parser.parse_args()
-
-    media_files = scan_media(args.input)
-    print(f"Found {len(media_files)} media files", file=sys.stderr)
-
-    if not media_files:
-        print("No media files found.", file=sys.stderr)
-        sys.exit(1)
-
-    pairs = generate_training_pairs(media_files, args.model, args.ollama_url,
-                                    args.limit, args.dry_run)
-
-    # Write output
-    output_path = Path(args.output)
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    with open(output_path, "w") as f:
-        for pair in pairs:
-            f.write(json.dumps(pair, ensure_ascii=False) + "\n")
-
-    print(f"\nWrote {len(pairs)} pairs to {output_path}", file=sys.stderr)
-
-
-if __name__ == "__main__":
-    main()