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from core.data_stats import (
load_dataset,
dataset_summary,
variable_types,
infer_column_types,
apply_category_filters,
reclassify_as_categorical,
reclassify_as_numeric,
)
def wire_callbacks(
*,
file_input,
status_output,
# RAW DATA
preview_checkbox,
overview_checkbox,
csv_preview,
desc_output,
dtypes_output,
# RECLASSIFICATION
num_to_cat,
cat_to_num,
fix_to_categorical_button,
fix_to_numeric_button,
fix_dtype_status,
# FILTERS
cat_filter_cols,
cat_val_1,
cat_val_2,
cat_val_3,
apply_filter_button,
filter_status,
# FILTERED DATA
preview_checkbox_filter,
overview_checkbox_filter,
csv_preview_filter,
desc_output_filter,
dtypes_output_filter,
state,
):
# ==================================================
# File upload
# ==================================================
def on_file_upload(file):
df, status = load_dataset(file)
if df is None:
return (
status,
None, None, None,
gr.update(choices=[], value=None),
gr.update(choices=[], value=None),
gr.update(choices=[], value=[]),
)
numeric_cols, categorical_cols = infer_column_types(df)
state.df = df
state.filtered_df = df
state.numeric_cols = numeric_cols
state.categorical_cols = categorical_cols
state.active_filters = {}
state.overrides = {"num_to_cat": [], "cat_to_num": []}
return (
status,
df,
dataset_summary(df),
variable_types(df),
# Reclassification dropdowns
gr.update(choices=numeric_cols, value=None),
gr.update(choices=categorical_cols, value=None),
# Filter columns (categorical only)
gr.update(choices=categorical_cols, value=[]),
)
file_input.change(
on_file_upload,
inputs=file_input,
outputs=[
status_output,
csv_preview,
desc_output,
dtypes_output,
num_to_cat,
cat_to_num,
cat_filter_cols,
],
)
# ==================================================
# Category value dropdowns (Filter 1–3)
# ==================================================
def update_category_filters(selected_columns):
df = state.df
if df is None or not selected_columns:
return (
gr.update(visible=False, choices=[], value=[]),
gr.update(visible=False, choices=[], value=[]),
gr.update(visible=False, choices=[], value=[]),
)
updates = []
for i in range(3):
if i < len(selected_columns):
col = selected_columns[i]
values = sorted(df[col].dropna().unique().tolist())
updates.append(
gr.update(
visible=True,
choices=values,
value=[],
)
)
else:
updates.append(
gr.update(visible=False, choices=[], value=[])
)
return tuple(updates)
cat_filter_cols.change(
update_category_filters,
inputs=cat_filter_cols,
outputs=[cat_val_1, cat_val_2, cat_val_3],
)
# ==================================================
# Apply filters
# ==================================================
def on_apply_filter(cat_cols, v1, v2, v3):
filtered_df, status = apply_category_filters(
state.df,
cat_cols,
v1, v2, v3,
)
state.filtered_df = filtered_df
state.active_filters = {
col: vals
for col, vals in zip(cat_cols[:3], [v1, v2, v3])
if vals
}
return status
apply_filter_button.click(
on_apply_filter,
inputs=[cat_filter_cols, cat_val_1, cat_val_2, cat_val_3],
outputs=filter_status,
)
# ==================================================
# RAW preview / summary
# ==================================================
preview_checkbox.change(
lambda x: gr.update(visible=x),
inputs=preview_checkbox,
outputs=csv_preview,
)
overview_checkbox.change(
lambda x: (
gr.update(visible=x),
gr.update(visible=x),
),
inputs=overview_checkbox,
outputs=[desc_output, dtypes_output],
)
# ==================================================
# FILTERED preview / summary
# ==================================================
preview_checkbox_filter.change(
lambda x: (
gr.update(visible=x),
state.filtered_df if x else None,
),
inputs=preview_checkbox_filter,
outputs=[csv_preview_filter, csv_preview_filter],
)
overview_checkbox_filter.change(
lambda x: (
gr.update(visible=x),
gr.update(visible=x),
dataset_summary(state.filtered_df) if x else None,
variable_types(state.filtered_df) if x else None,
),
inputs=overview_checkbox_filter,
outputs=[
desc_output_filter,
dtypes_output_filter,
desc_output_filter,
dtypes_output_filter,
],
)
# ==================================================
# Reclassification
# ==================================================
def on_fix_to_categorical(column):
_, msg = reclassify_as_categorical(state, column)
return (
gr.update(choices=state.categorical_cols, value=[]),
gr.update(choices=state.numeric_cols, value=None),
gr.update(choices=state.categorical_cols, value=None),
msg,
gr.update(visible=False),
gr.update(visible=False),
gr.update(visible=False),
)
def on_fix_to_numeric(column):
_, msg = reclassify_as_numeric(state, column)
return (
gr.update(choices=state.categorical_cols, value=[]),
gr.update(choices=state.numeric_cols, value=None),
gr.update(choices=state.categorical_cols, value=None),
msg,
gr.update(visible=False),
gr.update(visible=False),
gr.update(visible=False),
)
fix_to_categorical_button.click(
on_fix_to_categorical,
inputs=num_to_cat,
outputs=[
cat_filter_cols,
num_to_cat,
cat_to_num,
fix_dtype_status,
cat_val_1,
cat_val_2,
cat_val_3,
],
)
fix_to_numeric_button.click(
on_fix_to_numeric,
inputs=cat_to_num,
outputs=[
cat_filter_cols,
num_to_cat,
cat_to_num,
fix_dtype_status,
cat_val_1,
cat_val_2,
cat_val_3,
],
)
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