Upload folder using huggingface_hub

convert_to_hf.py

@@ -44,6 +44,32 @@ from typing import Tuple, Set
 from huggingface_hub import HfApi, HfFolder
 from datasets import Dataset
 
+def _normalize_index_like(x) -> List[str]:
+    """
+    Normalize index-like fields (INDEXES, PRIMARY_KEYS) into a list[str].
+    - Single column indexes expressed as ["col"] or [["col"]] -> ["col"]
+    - Composite indexes like ["a","b"] or [["a","b"]] -> ["a,b"]
+    - Scalars pass through -> ["scalar"]
+    """
+    if x is None:
+        return []
+    if not isinstance(x, list):
+        x = [x]
+
+    out: List[str] = []
+    for item in x:
+        if item is None:
+            continue
+        if isinstance(item, (list, tuple, set)):
+            flat = [_strify_scalar(v) for v in item if v is not None]
+            if len(flat) == 1:
+                out.append(flat[0])
+            elif len(flat) > 1:
+                out.append(",".join(flat))
+        else:
+            out.append(_strify_scalar(item))
+    return out
+
 def _strify_scalar(x):
     if x is None:
         return None
@@ -116,7 +142,8 @@ def normalize_column(col_name: str, col_payload: Dict[str, Any]) -> Dict[str, An
 
 
 def normalize_table(table_name: str, table_payload: Dict[str, Any]) -> Dict[str, Any]:
-    tp = {(k.strip().upper() if isinstance(k, str) else k): v for k, v in (table_payload or {}).items()}
+    tp = {(k.strip().upper() if isinstance(k, str) else k): v
+          for k, v in (table_payload or {}).items()}
 
     columns_obj = tp.get("COLUMNS", {}) or {}
     columns_list: List[Dict[str, Any]] = []
@@ -128,7 +155,8 @@ def normalize_table(table_name: str, table_payload: Dict[str, Any]) -> Dict[str,
         for c in columns_obj:
             if isinstance(c, dict):
                 col_name = (
-                    c.get("NAME") or c.get("name") or c.get("COLUMN_NAME") or c.get("column_name") or "unknown"
+                    c.get("NAME") or c.get("name") or
+                    c.get("COLUMN_NAME") or c.get("column_name") or "unknown"
                 )
                 columns_list.append(normalize_column(str(col_name), c or {}))
 
@@ -138,7 +166,7 @@ def normalize_table(table_name: str, table_payload: Dict[str, Any]) -> Dict[str,
     base["CHECKS"] = list(tp.get("CHECKS", []) or [])
     base["INDEXES"] = list(tp.get("INDEXES", []) or [])
 
-    # Normalize FKs to consistent shape (lists of strings)
+    # Normalize FKs
     norm_fks = []
     for fk in base["FOREIGN_KEYS"]:
         if not isinstance(fk, dict):
@@ -152,16 +180,19 @@ def normalize_table(table_name: str, table_payload: Dict[str, Any]) -> Dict[str,
             "ON_UPDATE": _strify_scalar(fk_up.get("ON_UPDATE")),
         })
 
+    # ✅ Use the new normalizer here
+    norm_pks = _normalize_index_like(base["PRIMARY_KEYS"])
+    norm_indexes = _normalize_index_like(base["INDEXES"])
+
     return {
         "TABLE_NAME": table_name,
         "COLUMNS": columns_list,
-        "PRIMARY_KEYS": _strify_list(base["PRIMARY_KEYS"]) or [],
+        "PRIMARY_KEYS": norm_pks,
         "FOREIGN_KEYS": norm_fks,
         "CHECKS": _strify_list(base["CHECKS"]) or [],
-        "INDEXES": _strify_list(base["INDEXES"]) or [],
+        "INDEXES": norm_indexes,
     }
 
-
 def normalize_record(key: str, payload: Dict[str, Any]) -> Dict[str, Any]:
     id_part, filename = split_id_filename(key)
 

data.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:845387efb02d484b479d9a35f04956e252d5596207d75d3c3d1fdd4a94758504
-size 337334299
+oid sha256:a084eed9ffba1a69765f70c4825589d3b5e1bca2d80e1b7315d3b2094aacae87
+size 337091098