Remove type hints for state to bypass Gradio schema error

app.py

@@ -39,7 +39,7 @@ from visualizations import (
 )
 
 
-DatasetState = Any
+
 
 
 def _format_overview_text(info: Dict[str, Any], source_name: str) -> str:
@@ -58,12 +58,12 @@ def _empty_dataframe(message: str = "No data available") -> pd.DataFrame:
     return pd.DataFrame({"status": [message]})
 
 
-def _ensure_state(state: Optional[DatasetState]) -> DatasetState:
+def _ensure_state(state) -> Dict[str, Any]:
     """Guarantee a dictionary-based state object."""
     return state or {}
 
 
-def _current_dataframe(state: DatasetState, filtered: bool = True) -> pd.DataFrame:
+def _current_dataframe(state, filtered: bool = True) -> pd.DataFrame:
     """Return the filtered or raw dataframe from state."""
     key = "filtered_df" if filtered else "dataframe"
     df = state.get(key)
@@ -72,7 +72,7 @@ def _current_dataframe(state: DatasetState, filtered: bool = True) -> pd.DataFra
     raise ValueError("Please upload a dataset before performing this action.")
 
 
-def _finalize_dataset_load(bundle: DatasetBundle, state: DatasetState) -> Tuple[DatasetState, str, str, pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, str]:
+def _finalize_dataset_load(bundle: DatasetBundle, state) -> Tuple[Dict[str, Any], str, str, pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, str]:
     """Populate shared outputs after a dataset is loaded."""
     df = bundle.dataframe
     state = {
@@ -97,7 +97,7 @@ def _finalize_dataset_load(bundle: DatasetBundle, state: DatasetState) -> Tuple[
     return state, status, info_text, dtypes_df, head_df, tail_df, filter_preview, row_count
 
 
-def _handle_file_upload(file, state: Optional[DatasetState]) -> Tuple[DatasetState, str, str, pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, str]:
+def _handle_file_upload(file, state) -> Tuple[Dict[str, Any], str, str, pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, str]:
     """Load a dataset from the uploaded file."""
     state = _ensure_state(state)
     try:
@@ -117,7 +117,7 @@ def _handle_file_upload(file, state: Optional[DatasetState]) -> Tuple[DatasetSta
     return _finalize_dataset_load(bundle, state)
 
 
-def _handle_sample_dataset(selection: Optional[str], state: Optional[DatasetState]) -> Tuple[DatasetState, str, str, pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, str]:
+def _handle_sample_dataset(selection: Optional[str], state) -> Tuple[Dict[str, Any], str, str, pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, str]:
     """Load one of the bundled sample datasets."""
     state = _ensure_state(state)
     if not selection:
@@ -135,7 +135,7 @@ def _handle_sample_dataset(selection: Optional[str], state: Optional[DatasetStat
 
 
 def _populate_column_options(
-    state: Optional[DatasetState],
+    state,
 ) -> Tuple[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]:
     """Populate dropdown choices based on the uploaded dataset."""
     state = _ensure_state(state)
@@ -198,7 +198,7 @@ def _populate_column_options(
     )
 
 
-def _update_numeric_inputs(column: Optional[str], state: Optional[DatasetState]) -> Tuple[Any, Any]:
+def _update_numeric_inputs(column: Optional[str], state) -> Tuple[Any, Any]:
     """Update numeric min/max inputs when a column is selected."""
     state = _ensure_state(state)
     hidden = gr.update(visible=False, value=None)
@@ -215,7 +215,7 @@ def _update_numeric_inputs(column: Optional[str], state: Optional[DatasetState])
     )
 
 
-def _update_categorical_values(column: Optional[str], state: Optional[DatasetState]):
+def _update_categorical_values(column: Optional[str], state):
     """Populate categorical value options for filtering."""
     state = _ensure_state(state)
     if not column or "filter_meta" not in state:
@@ -230,7 +230,7 @@ def _update_categorical_values(column: Optional[str], state: Optional[DatasetSta
     )
 
 
-def _update_date_bounds(column: Optional[str], state: Optional[DatasetState]) -> Tuple[Any, Any]:
+def _update_date_bounds(column: Optional[str], state) -> Tuple[Any, Any]:
     """Populate date inputs when a date column is selected."""
     state = _ensure_state(state)
     if not column or "filter_meta" not in state:
@@ -249,7 +249,7 @@ def _update_date_bounds(column: Optional[str], state: Optional[DatasetState]) ->
 
 
 def _apply_filters(
-    state: Optional[DatasetState],
+    state,
     numeric_column: Optional[str],
     numeric_min: Optional[float],
     numeric_max: Optional[float],
@@ -258,7 +258,7 @@ def _apply_filters(
     date_column: Optional[str],
     start_date: Optional[str],
     end_date: Optional[str],
-) -> Tuple[DatasetState, pd.DataFrame, str]:
+) -> Tuple[Dict[str, Any], pd.DataFrame, str]:
     """Filter the dataset according to user selections."""
     state = _ensure_state(state)
     df = _current_dataframe(state, filtered=False)
@@ -288,7 +288,7 @@ def _apply_filters(
     return state, preview, row_count
 
 
-def _generate_statistics(state: Optional[DatasetState]) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, str]:
+def _generate_statistics(state) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, str]:
     """Produce summary statistics for the Statistics tab."""
     state = _ensure_state(state)
     try:
@@ -313,7 +313,7 @@ def _generate_statistics(state: Optional[DatasetState]) -> Tuple[pd.DataFrame, p
 
 
 def _generate_chart(
-    state: Optional[DatasetState],
+    state,
     chart_type: str,
     ts_date: Optional[str],
     ts_value: Optional[str],
@@ -362,7 +362,7 @@ def _generate_chart(
     return fig, fig, "Visualization generated."
 
 
-def _download_filtered(state: Optional[DatasetState]) -> str:
+def _download_filtered(state) -> str:
     """Export the filtered dataset to a temporary CSV file."""
     state = _ensure_state(state)
     df = _current_dataframe(state, filtered=True)
@@ -387,7 +387,7 @@ def _download_chart(fig: Optional[go.Figure]) -> str:
 
 
 def _generate_insights(
-    state: Optional[DatasetState],
+    state,
     numeric_column: Optional[str],
     trend_date_column: Optional[str],
     trend_value_column: Optional[str],