feat: Auto-generate scene descriptions from image/video assets (#689)

Scans media directory, generates descriptions via Ollama vision model.
JSONL output in training data format.
Closes #689.

bin/nostr-agent-demo.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 """
 Full Nostr agent-to-agent communication demo - FINAL WORKING
 """

bin/soul_eval_gate.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 """
 Soul Eval Gate — The Conscience of the Training Pipeline
 

scripts/captcha_bypass_handler.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import json
 from hermes_tools import browser_navigate, browser_vision
 

scripts/diagram_meaning_extractor.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import json
 from hermes_tools import browser_navigate, browser_vision
 

scripts/generate_scene_descriptions.py

@@ -0,0 +1,191 @@
+#!/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()

scripts/visual_pr_reviewer.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import json
 from hermes_tools import browser_navigate, browser_vision