feat: add convert-to-cooklang script for Anthropic API recipe conversion
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#!/usr/bin/env python3
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"""Convert unstructured recipe content to Cooklang format using an
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Anthropic-compatible API (Messages API).
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Accepts text input (--text or --stdin) or file input (--file for PDF/images).
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Reads API configuration from a YAML config file (--config).
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Outputs the Cooklang text on stdout.
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Exits 0 on success, non-zero with error details on stderr on failure.
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Usage:
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scripts/convert-to-cooklang --config roux-config.yaml --text "Recipe text..."
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scripts/convert-to-cooklang --config roux-config.yaml --stdin < recipe.txt
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scripts/convert-to-cooklang --config roux-config.yaml --file recipe.pdf
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"""
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import argparse
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import base64
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import json
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import mimetypes
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import os
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import sys
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import yaml
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from anthropic import Anthropic
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# -- Config -------------------------------------------------------------------
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def load_config(path: str) -> dict:
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with open(path) as f:
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cfg = yaml.safe_load(f)
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anthropic_cfg = cfg.get("anthropic", {})
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if not anthropic_cfg.get("api_key"):
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print("Error: anthropic.api_key is required in config", file=sys.stderr)
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sys.exit(1)
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return anthropic_cfg
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# -- Prompt loading -----------------------------------------------------------
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def load_prompts() -> tuple[str, str, str]:
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"""Load system prompt, converter instructions, and user template.
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Resolved relative to the script's parent directory (project root).
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"""
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script_dir = os.path.dirname(os.path.abspath(__file__))
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project_root = os.path.dirname(script_dir) # scripts/ -> project root
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prompts_dir = os.path.join(project_root, "prompts")
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def read(name: str) -> str:
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with open(os.path.join(prompts_dir, name)) as f:
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return f.read()
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return read("system-prompt.md"), read("cooklang-converter.md"), read("user-prompt-template.md")
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# -- Content preparation ------------------------------------------------------
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def build_text_content(text: str) -> list[dict]:
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return [{"type": "text", "text": text}]
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def build_file_content(filepath: str) -> list[dict]:
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"""Prepare a file (PDF or image) for the Anthropic Messages API.
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Returns a list with one document/image content block.
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"""
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mime_type, _ = mimetypes.guess_type(filepath)
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if mime_type is None:
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# Default to octet-stream; let the API decide
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mime_type = "application/octet-stream"
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with open(filepath, "rb") as f:
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data = base64.b64encode(f.read()).decode("utf-8")
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is_pdf = mime_type == "application/pdf"
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block_type = "document" if is_pdf else "image"
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return [
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{
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"type": block_type,
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"source": {
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"type": "base64",
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"media_type": mime_type,
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"data": data,
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},
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}
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]
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# -- API call -----------------------------------------------------------------
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def cooklang_from_anthropic(
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api_key: str,
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base_url: str | None,
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model: str | None,
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system_prompt: str,
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user_message_content: list[dict],
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) -> str:
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"""Send the recipe content to the Anthropic API and return Cooklang text."""
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client = Anthropic(api_key=api_key, base_url=base_url) if base_url else Anthropic(api_key=api_key)
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resolved_model = model or "claude-sonnet-4-20250514"
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response = client.messages.create(
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model=resolved_model,
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max_tokens=4096,
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system=[{"type": "text", "text": system_prompt}],
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messages=[
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{
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"role": "user",
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"content": user_message_content,
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}
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],
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)
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# Extract text from response content blocks
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parts: list[str] = []
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for block in response.content:
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if block.type == "text":
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parts.append(block.text)
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cooklang = "\n".join(parts).strip()
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# Basic validation: must start with front matter marker
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if not cooklang.startswith("---"):
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print(
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"Error: AI response does not appear to be valid Cooklang "
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"(missing front matter). Response preview: "
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+ cooklang[:200],
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file=sys.stderr,
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)
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sys.exit(1)
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return cooklang
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# -- Main ---------------------------------------------------------------------
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def main() -> None:
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parser = argparse.ArgumentParser(
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description="Convert unstructured recipe content to Cooklang using an Anthropic-compatible API."
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)
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parser.add_argument(
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"--config",
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required=True,
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help="Path to YAML config file (must contain anthropic.api_key)",
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)
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input_group = parser.add_mutually_exclusive_group(required=True)
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input_group.add_argument("--text", help="Inline recipe text")
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input_group.add_argument("--stdin", action="store_true", help="Read recipe text from stdin")
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input_group.add_argument("--file", help="Path to recipe PDF or image file")
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args = parser.parse_args()
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# Load config
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config = load_config(args.config)
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api_key = config["api_key"]
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base_url = config.get("base_url")
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model = config.get("model")
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# Load prompts
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system_prompt, converter_instructions, user_template = load_prompts()
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# Prepare user message content
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if args.file:
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# File input (PDF or image): send file content + converter instructions
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file_content = build_file_content(args.file)
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user_message_content = (
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[{"type": "text", "text": converter_instructions}]
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+ file_content
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)
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elif args.stdin:
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raw_text = sys.stdin.read()
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user_message_content = build_text_content(
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user_template.replace("{{FULL_RECIPE_TEXT}}", raw_text)
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.replace("{{TITLE}}", "Untitled")
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.replace("{{URL}}", "")
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)
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else:
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# --text
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user_message_content = build_text_content(
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user_template.replace("{{FULL_RECIPE_TEXT}}", args.text)
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.replace("{{TITLE}}", "Untitled")
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.replace("{{URL}}", "")
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)
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# Call API
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try:
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cooklang = cooklang_from_anthropic(
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api_key=api_key,
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base_url=base_url,
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model=model,
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system_prompt=system_prompt,
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user_message_content=user_message_content,
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)
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except Exception as e:
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print(f"Error calling Anthropic API: {e}", file=sys.stderr)
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sys.exit(1)
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# Output Cooklang text
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print(cooklang)
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if __name__ == "__main__":
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main()
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