first commit

clean-text-to-keywords/infer_json_usage.py

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+import argparse
+import json
+import os
+import re
+from typing import Sequence
+
+from keyword_extractor import KeywordExtractor
+from json_inference import fill_template_from_keywords
+
+
+def _build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="Extract keywords and infer values into a JSON template.",
+    )
+    parser.add_argument(
+        "text",
+        nargs="*",
+        help="Input description text.",
+    )
+    parser.add_argument(
+        "--template",
+        default="",
+        help="Path to JSON template file with keys only. If omitted, full inferred JSON is returned.",
+    )
+    parser.add_argument(
+        "--model",
+        default="en_core_web_sm",
+        help="spaCy model name (default: en_core_web_sm).",
+    )
+    parser.add_argument(
+        "--keywords",
+        nargs="+",
+        default=None,
+        help="Provide keywords directly instead of raw text.",
+    )
+    parser.add_argument(
+        "--json-only",
+        action="store_true",
+        help="Print only inferred JSON (skip keyword list output).",
+    )
+    return parser
+
+
+def _load_template(path: str):
+    if not path:
+        return {}
+
+    if not os.path.exists(path):
+        raise FileNotFoundError(f"Template file not found: {path}")
+
+    with open(path, "r", encoding="utf-8") as file_handle:
+        raw = file_handle.read().strip()
+        if not raw:
+            return {}
+        return json.loads(raw)
+
+
+def _parse_keywords_fragment(raw: str):
+    if not raw.strip():
+        return []
+
+    try:
+        parsed = json.loads(raw)
+        if isinstance(parsed, list):
+            return [str(item).strip().lower() for item in parsed if str(item).strip()]
+    except json.JSONDecodeError:
+        pass
+
+    tokens = re.findall(r"[a-zA-Z0-9_-]+", raw.lower())
+    return [token for token in tokens if token]
+
+
+def _extract_keywords(args):
+    if args.keywords:
+        return [word.strip().lower() for word in args.keywords if word.strip()]
+
+    if args.template and not os.path.exists(args.template) and args.template.lstrip().startswith("["):
+        raw = " ".join([args.template] + args.text)
+        return _parse_keywords_fragment(raw)
+
+    if not args.text:
+        raise ValueError("Provide input text or use --keywords.")
+
+    text = " ".join(args.text)
+    extractor = KeywordExtractor.from_default_model(model_name=args.model)
+    return extractor.extract(text)
+
+
+def main(argv: Sequence[str] | None = None) -> None:
+    parser = _build_parser()
+    args = parser.parse_args(argv)
+
+    keywords = _extract_keywords(args)
+
+    template_path = args.template
+    if args.template and not os.path.exists(args.template) and args.template.lstrip().startswith("["):
+        template_path = ""
+
+    template = _load_template(template_path)
+    inferred_json = fill_template_from_keywords(template, keywords)
+
+    if args.json_only:
+        print(json.dumps(inferred_json, indent=2))
+        return
+
+    print(json.dumps(keywords))
+    print(json.dumps(inferred_json, indent=2))
+
+
+if __name__ == "__main__":
+    main()