fix: add python3 shebang to bin/glitch_patterns.py (#681)

bin/glitch_patterns.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 """
 Glitch pattern definitions for 3D world anomaly detection.
 

bin/nostr-agent-demo.py

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

bin/soul_eval_gate.py

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

scripts/captcha_bypass_handler.py

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

scripts/diagram_meaning_extractor.py

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

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()

scripts/visual_pr_reviewer.py

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