add immutable var class

reflex/experimental/__init__.py

@@ -42,6 +42,7 @@ def toast(self):
     asset=asset,
     client_state=ClientStateVar.create,
     hooks=hooks,
+    vars=vars,
     layout=layout,
     progress=progress,
     PropsBase=PropsBase,

reflex/experimental/vars/base.py

@@ -0,0 +1,73 @@
+import dataclasses
+import sys
+from typing import Any, Literal, Optional, Type, Union, get_args, get_origin
+from reflex.utils import types
+from reflex.vars import Var, VarData
+
+
+@dataclasses.dataclass(
+    eq=False,
+    frozen=True,
+    **{"slots": True} if sys.version_info >= (3, 10) else {},
+)
+class ImmutableVar(Var):
+    # The name of the var.
+    _var_name: str = dataclasses.field()
+
+    # The type of the var.
+    _var_type: Type = dataclasses.field(default=Any)
+
+    # Whether this is a local javascript variable.
+    _var_is_local: bool = dataclasses.field(default=False)
+
+    # Whether the var is a string literal.
+    _var_is_string: bool = dataclasses.field(default=False)
+
+    # _var_full_name should be prefixed with _var_state
+    _var_full_name_needs_state_prefix: bool = dataclasses.field(default=False)
+
+    # Extra metadata associated with the Var
+    _var_data: Optional[VarData] = dataclasses.field(default=None)
+
+    def get_default_value(self) -> Any:
+        """Get the default value of the var.
+
+        Returns:
+            The default value of the var.
+
+        Raises:
+            ImportError: If the var is a dataframe and pandas is not installed.
+        """
+        if types.is_optional(self._var_type):
+            return None
+
+        type_ = (
+            get_origin(self._var_type)
+            if types.is_generic_alias(self._var_type)
+            else self._var_type
+        )
+        if type_ is Literal:
+            args = get_args(self._var_type)
+            return args[0] if args else None
+        if issubclass(type_, str):
+            return ""
+        if issubclass(type_, types.get_args(Union[int, float])):
+            return 0
+        if issubclass(type_, bool):
+            return False
+        if issubclass(type_, list):
+            return []
+        if issubclass(type_, dict):
+            return {}
+        if issubclass(type_, tuple):
+            return ()
+        if types.is_dataframe(type_):
+            try:
+                import pandas as pd
+
+                return pd.DataFrame()
+            except ImportError as e:
+                raise ImportError(
+                    "Please install pandas to use dataframes in your app."
+                ) from e
+        return set() if issubclass(type_, set) else None