Make st.write call st.json to display Streamlit secrets object (#8659)

## Describe your changes

When you call `st.write(st.secrets)`, the output is displayed as inline
code with `st.markdown`. This PR makes `st.write` call `st.json` on
objects of type `streamlit.runtime.secrets.Secrets`

## GitHub Issue Link (if applicable)

Closes #2905.

## Testing Plan

- Added Python unit test

---

**Contribution License Agreement**

By submitting this pull request you agree that all contributions to this
project are made under the Apache 2.0 license.

lib/streamlit/elements/write.py

@@ -483,6 +483,9 @@ def flush_buffer():
             ):
                 # We either explicitly allow HTML or infer it's not HTML
                 self.dg.markdown(repr_html, unsafe_allow_html=unsafe_allow_html)
+            elif type_util.is_streamlit_secrets_class(arg):
+                flush_buffer()
+                self.dg.json(arg.to_dict())
             else:
                 stringified_arg = str(arg)
 

lib/streamlit/runtime/secrets.py

@@ -25,6 +25,7 @@
     KeysView,
     Mapping,
     NoReturn,
+    Union,
     ValuesView,
 )
 
@@ -44,6 +45,14 @@
 ]
 
 
+def _convert_to_dict(obj: Union[Mapping[str, Any], AttrDict]) -> dict[str, Any]:
+    """Recursively convert Mapping or AttrDict objects to dictionaries."""
+    if isinstance(obj, AttrDict):
+        return obj.to_dict()
+    elif isinstance(obj, Mapping):
+        return {k: _convert_to_dict(v) for k, v in obj.items()}
+
+
 def _missing_attr_error_message(attr_name: str) -> str:
     return (
         f'st.secrets has no attribute "{attr_name}". '
@@ -227,6 +236,11 @@ def _parse(self, print_exceptions: bool) -> Mapping[str, Any]:
 
             return self._secrets
 
+    def to_dict(self) -> dict[str, Any]:
+        """Converts the secrets store into a nested dictionary, where nested AttrDict objects are also converted into dictionaries."""
+        secrets = self._parse(True)
+        return _convert_to_dict(secrets)
+
     @staticmethod
     def _maybe_set_environment_variable(k: Any, v: Any) -> None:
         """Add the given key/value pair to os.environ if the value

lib/streamlit/type_util.py

@@ -59,6 +59,8 @@
     from plotly.graph_objs import Figure
     from pydeck import Deck
 
+    from streamlit.runtime.secrets import Secrets
+
 
 # Maximum number of rows to request from an unevaluated (out-of-core) dataframe
 MAX_UNEVALUATED_DF_ROWS = 10000
@@ -535,6 +537,11 @@ def is_iterable(obj: object) -> TypeGuard[Iterable[Any]]:
     return True
 
 
+def is_streamlit_secrets_class(obj: object) -> TypeGuard[Secrets]:
+    """True if obj is a Streamlit Secrets object."""
+    return is_type(obj, "streamlit.runtime.secrets.Secrets")
+
+
 def is_sequence(seq: Any) -> bool:
     """True if input looks like a sequence."""
     if isinstance(seq, str):

lib/tests/streamlit/write_test.py

@@ -19,7 +19,7 @@
 import unittest
 from collections import namedtuple
 from typing import Any
-from unittest.mock import MagicMock, Mock, PropertyMock, call, patch
+from unittest.mock import MagicMock, Mock, PropertyMock, call, mock_open, patch
 
 import numpy as np
 import pandas as pd
@@ -35,6 +35,7 @@
 from tests.streamlit.modin_mocks import DataFrame as ModinDataFrame
 from tests.streamlit.modin_mocks import Series as ModinSeries
 from tests.streamlit.pyspark_mocks import DataFrame as PysparkDataFrame
+from tests.streamlit.runtime.secrets_test import MOCK_TOML
 from tests.streamlit.snowpandas_mocks import DataFrame as SnowpandasDataFrame
 from tests.streamlit.snowpandas_mocks import Series as SnowpandasSeries
 from tests.streamlit.snowpark_mocks import DataFrame as SnowparkDataFrame
@@ -250,6 +251,14 @@ def test_query_params(self):
 
             p.assert_called_once()
 
+    @patch("builtins.open", new_callable=mock_open, read_data=MOCK_TOML)
+    def test_streamlit_secrets(self, *mocks):
+        """Test st.write with st.secrets."""
+        with patch("streamlit.delta_generator.DeltaGenerator.json") as p:
+            st.write(st.secrets)
+
+            p.assert_called_once()
+
     @parameterized.expand(
         [
             (pd.DataFrame([[20, 30, 50]], columns=["a", "b", "c"]),),