Latex Posters

scripts/generate_schematic.py

#!/usr/bin/env python3
"""
Scientific schematic generation using Nano Banana 2.

Generate any scientific diagram by describing it in natural language.
Nano Banana 2 handles everything automatically with smart iterative refinement.

Smart iteration: Only regenerates if quality is below threshold for your document type.
Quality review: Uses Gemini 3.7 Flash for professional scientific evaluation.

Usage:
    # Generate for journal paper (highest quality threshold)
    python generate_schematic.py "CONSORT flowchart" -o flowchart.png --doc-type journal
    
    # Generate for presentation (lower threshold, faster)
    python generate_schematic.py "Transformer architecture" -o transformer.png --doc-type presentation
    
    # Generate for poster
    python generate_schematic.py "MAPK signaling pathway" -o pathway.png --doc-type poster
"""

import argparse
import os
import subprocess
import sys
from pathlib import Path

# Variables forwarded to the generation subprocess. The child needs the
# OpenRouter credential; the rest keep networking, TLS, and locale working.
# Copying the whole parent environment instead would hand the child every
# unrelated secret that happens to be exported in the calling shell.
FORWARDED_ENV_VARS = (
    "PATH", "HOME", "LANG", "LC_ALL", "TMPDIR", "PYTHONPATH",
    "HTTP_PROXY", "HTTPS_PROXY", "NO_PROXY",
    "http_proxy", "https_proxy", "no_proxy",
    "SSL_CERT_FILE", "SSL_CERT_DIR", "REQUESTS_CA_BUNDLE", "CURL_CA_BUNDLE",
    # Windows needs these for sockets, temp files, and interpreter startup.
    "SYSTEMROOT", "WINDIR", "COMSPEC", "PATHEXT",
    "APPDATA", "LOCALAPPDATA", "USERPROFILE", "TEMP", "TMP",
)


def resolve_api_key(explicit=None):
    """Resolve the OpenRouter key from --api-key, the environment, then any .env file.

    The .env scan walks up from the working directory and finally checks the
    script's own directory, so running from anywhere inside a project picks up
    the key at its root. The child process is handed the resolved value through
    build_subprocess_env, so it never has to repeat this search.
    """
    if explicit:
        return explicit

    from_env = os.environ.get("OPENROUTER_API_KEY", "").strip()
    if from_env:
        return from_env

    cwd = Path.cwd()
    for directory in [cwd, *cwd.parents, Path(__file__).resolve().parent]:
        env_file = directory / ".env"
        if not env_file.is_file():
            continue
        try:
            content = env_file.read_text(encoding="utf-8", errors="replace")
        except OSError:
            continue
        for raw in content.splitlines():
            line = raw.strip()
            if line.startswith("#") or "=" not in line:
                continue
            name, _, value = line.partition("=")
            if name.strip() == "OPENROUTER_API_KEY":
                value = value.strip().strip('"').strip("'")
                if value:
                    return value

    return None


def build_subprocess_env(api_key):
    """Return a minimal environment for the AI generation subprocess."""
    env = {name: os.environ[name] for name in FORWARDED_ENV_VARS if name in os.environ}
    if api_key:
        env["OPENROUTER_API_KEY"] = api_key
    return env


def main():
    """Command-line interface."""
    parser = argparse.ArgumentParser(
        description="Generate scientific schematics using AI with smart iterative refinement",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
How it works:
  Simply describe your diagram in natural language
  Nano Banana 2 generates it automatically with:
  - Smart iteration (only regenerates if quality is below threshold)
  - Quality review by Gemini 3.7 Flash
  - Document-type aware quality thresholds
  - PNG draft for visual and scientific verification

Document Types (quality thresholds):
  journal      8.5/10  - Nature, Science, peer-reviewed journals
  conference   8.0/10  - Conference papers
  thesis       8.0/10  - Dissertations, theses
  grant        8.0/10  - Grant proposals
  preprint     7.5/10  - arXiv, bioRxiv, etc.
  report       7.5/10  - Technical reports
  poster       7.0/10  - Academic posters
  presentation 6.5/10  - Slides, talks
  default      7.5/10  - General purpose

Examples:
  # Generate for journal paper (strict quality)
  python generate_schematic.py "CONSORT participant flow" -o flowchart.png --doc-type journal
  
  # Generate for poster (moderate quality)
  python generate_schematic.py "Transformer architecture" -o arch.png --doc-type poster
  
  # Generate for slides (faster, lower threshold)
  python generate_schematic.py "System diagram" -o system.png --doc-type presentation
  
  # Custom max iterations
  python generate_schematic.py "Complex pathway" -o pathway.png --iterations 2
  
  # Verbose output
  python generate_schematic.py "Circuit diagram" -o circuit.png -v

Environment Variables:
  OPENROUTER_API_KEY    Required for AI generation
        """
    )
    
    parser.add_argument("prompt", 
                       help="Description of the diagram to generate")
    parser.add_argument("-o", "--output", required=True,
                       help="Output PNG file path (.png required)")
    parser.add_argument("--doc-type", default="default",
                       choices=["journal", "conference", "poster", "presentation",
                               "report", "grant", "thesis", "preprint", "default"],
                       help="Document type for quality threshold (default: default)")
    parser.add_argument("--iterations", type=int, default=2,
                       help="Maximum refinement iterations (default: 2, max: 2)")
    parser.add_argument("--api-key", 
                       help="OpenRouter API key (or use OPENROUTER_API_KEY env var)")
    parser.add_argument("-v", "--verbose", action="store_true",
                       help="Verbose output")
    
    args = parser.parse_args()

    # Validated here rather than clamped silently, and before the credential
    # lookup so an out-of-range value reports itself rather than an absent key.
    if not 1 <= args.iterations <= 2:
        parser.error("--iterations must be 1 or 2")

    if Path(args.output).suffix.lower() != ".png":
        parser.error("--output must end in .png; format conversion is not supported")

    # Check for API key — resolves --api-key, the environment, then any .env file
    api_key = resolve_api_key(args.api_key)
    if not api_key:
        print("Error: OPENROUTER_API_KEY not found")
        print("\nFor AI generation, you need an OpenRouter API key.")
        print("Get one at: https://openrouter.ai/keys")
        print("\nSet it with:")
        print("  export OPENROUTER_API_KEY='your_api_key'")
        print("\nOr add OPENROUTER_API_KEY=your_api_key to a .env file")
        print("Or use --api-key flag")
        sys.exit(1)
    
    # Find AI generation script
    script_dir = Path(__file__).parent
    ai_script = script_dir / "generate_schematic_ai.py"
    
    if not ai_script.exists():
        print(f"Error: AI generation script not found: {ai_script}")
        sys.exit(1)
    
    # Build command
    cmd = [sys.executable, str(ai_script), args.prompt, "-o", args.output]
    
    if args.doc_type != "default":
        cmd.extend(["--doc-type", args.doc_type])

    cmd.extend(["--iterations", str(args.iterations)])

    if args.verbose:
        cmd.append("-v")
    
    # Execute — pass API key via environment to avoid exposure in process listings
    try:
        result = subprocess.run(cmd, check=False, env=build_subprocess_env(api_key))
        sys.exit(result.returncode)
    except Exception as e:
        print(f"Error executing AI generation: {e}")
        sys.exit(1)


if __name__ == "__main__":
    main()