Spaces:
Sleeping
Sleeping
File size: 12,715 Bytes
d40a9ae |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 |
from haystack import Document
from haystack.document_stores import InMemoryDocumentStore, ElasticsearchDocumentStore, FAISSDocumentStore
from haystack.nodes import BM25Retriever
from haystack.pipelines import DocumentSearchPipeline
import pandas as pd
import panel as pn
import param
pn.extension('tabulator')
pn.extension(sizing_mode="scale_both")
import hvplot.pandas
# load data
infile = "/Users/carolanderson/Dropbox/repos/miscellany/webapps/Agency Inventory AI Usage - Sheet1.tsv"
df = pd.read_csv(infile, sep="\t", lineterminator='\n')
# rearrange column order
col_list = ['Agency', 'Name of Inventory Item',
'Primary Type of AI',
'Purpose of AI', 'Length of Usage',
'Does it directly impact the public?',
'Vendor System',
'Description of Inventory Item',
'Other Notes\r']
df = df[col_list]
# remove trailing \r from 'Other Notes' header
df = df.rename(columns = {'Other Notes\r' : 'Other Notes'})
# remove trailing spaces from agency names (caused duplicate instance of "DOC")
df['Agency'] = df['Agency'].apply(lambda x : x.rstrip())
# columns not useful for filtering
no_filter_cols = ['Name of Inventory Item', 'Description of Inventory Item', "Other Notes"]
# columns to be used for filtering
filter_cols = [c for c in df.columns.unique() if c not in no_filter_cols]
# column selector for main plot
plot_column_selector = pn.widgets.Select(options=filter_cols, name="Plot category: ")
# agency selector for main plot
plot_agency_selector = pn.widgets.MultiSelect(options=["Select all"] + list(df["Agency"].unique()),
value=["Select all"],
name="Optional - filter by agency")
# selectors below are all for interactive dataframe
agency_selector = pn.widgets.MultiSelect(options=["Select all"] + list(df["Agency"].unique()),
value=["Select all"],
name="Agency")
type_selector = pn.widgets.MultiSelect(options=["Select all"] + list(df['Primary Type of AI'].unique()),
value=["Select all"],
name='Primary Type of AI')
purpose_selector = pn.widgets.MultiSelect(options=["Select all"] + list(df["Purpose of AI"].unique()),
value=["Select all"],
name="Purpose of AI")
length_selector = pn.widgets.MultiSelect(options=["Select all"] + list(df['Length of Usage'].unique()),
value=["Select all"],
name="Length of Usage")
impact_selector = pn.widgets.MultiSelect(options=["Select all"] + list(df['Does it directly impact the public?'].unique()),
value=["Select all"],
name='Does it directly impact the public?')
vendor_selector = pn.widgets.MultiSelect(options=["Select all"] + list(df['Vendor System'].unique()),
value=["Select all"],
name='Vendor System')
row_filters = [agency_selector, type_selector, purpose_selector, length_selector, impact_selector,
vendor_selector]
def custom_plot(table, column_selector, agency_selector):
if "Select all" not in agency_selector:
table = table[table['Agency'].isin(agency_selector)]
table = table[column_selector].value_counts().sort_values(ascending=True)
return table.hvplot.barh(width=600, height=400, color="#336BCC")
def custom_table_filter(table,
agency_selector,
type_selector,
purpose_selector,
length_selector,
impact_selector,
vendor_selector):
"""
This repetitive approach was the only way I could get things working with a
'Select all' menu option.
"""
if "Select all" not in agency_selector:
table = table[table["Agency"].isin(agency_selector)]
if "Select all" not in type_selector:
table = table[table['Primary Type of AI'].isin(type_selector)]
if "Select all" not in purpose_selector:
table = table[table["Purpose of AI"].isin(purpose_selector)]
if "Select all" not in length_selector:
table = table[table['Length of Usage'].isin(length_selector)]
if "Select all" not in impact_selector:
table = table[table['Does it directly impact the public?'].isin(impact_selector)]
if "Select all" not in vendor_selector:
table = table[table['Vendor System'].isin(vendor_selector)]
return table
custom_table = pn.widgets.Tabulator(df, pagination="local", page_size=350, layout="fit_data",
width=800, height=550)
custom_table.add_filter(pn.bind(custom_table_filter,
agency_selector=agency_selector,
type_selector=type_selector,
purpose_selector=purpose_selector,
length_selector=length_selector,
impact_selector=impact_selector,
vendor_selector=vendor_selector))
interactive_plot = pn.bind(custom_plot, table=df, column_selector=plot_column_selector,
agency_selector=plot_agency_selector)
overview_stacked = pn.Column(
pn.pane.Markdown("""
Plot shows the total count of entries, aggregated by various categories.
Change the category with the dropdown menu.
The total number of records in the database is 337, but some fields have missing values.
In particular, 'Vendor System' and 'Primary Type of AI' were not always filled out."""),
pn.Column(pn.Row(plot_column_selector,
plot_agency_selector),
pn.Row(interactive_plot, width=500))
)
overview_card = pn.Card(overview_stacked, header="# Overview of the data")
filename, button = custom_table.download_menu(
text_kwargs={'name': 'Enter filename ending in .csv or .json', 'value': 'default.csv'},
button_kwargs={'name': 'Download table'}
)
download_card = pn.Card(pn.pane.Markdown("""
Download current table in .csv or .json format.
File format will be automatically selected based on the file extension.
"""),
filename, button, header="### Download")
table_card = pn.Card(
pn.Row(
pn.Column(
pn.pane.Markdown("""
### Filter with the menus below
"""),pn.WidgetBox(*row_filters),
styles=dict(background='#DDE6FF')
), pn.Column(pn.pane.Markdown("""
### Scroll horizontally and vertically to see all data
"""), custom_table)),
download_card,
header="# Explore the data"
)
# stacked bar plot of impact by agency (static plot)
impact_counts = df.groupby('Agency')['Does it directly impact the public?'].value_counts()
impact_counts = impact_counts.sort_index(level="Agency", ascending=False)
impact_count_df = pd.DataFrame(impact_counts).rename(columns={'Does it directly impact the public?' : "Count"})
impact_plot = impact_count_df.hvplot.barh(stacked=True, width=500, height=400, color=[ "#019C6D", "#336BCC", "#F41903",], legend="bottom_right")
impact_card = pn.Card(
pn.Column(
pn.pane.Markdown("""
Number of systems with no, indirect, or direct impact on the public.
These judgements were made by Anna Blue and are unique to her report."""),
impact_plot), header="# Impact on the public, by agency")
# keyword search
class TableIndices(param.Parameterized):
row_indices = param.List()
col_indices = param.List()
def __call__(self):
return (self.row_indices, self.col_indices)
def run_search(text, pipeline):
if text == "":
return None
res = pipeline.run(query=text, params={"Retriever": {"top_k": 10}})
relevant_results = [r for r in res['documents'] if r.score > 0.5]
result_rows = [doc.meta['index'] for doc in relevant_results]
result_cols = [doc.meta['column_header'] for doc in relevant_results]
table_indices = TableIndices(row_indices=result_rows, col_indices=result_cols)
return table_indices
def produce_table(df, table_indices):
if not table_indices:
return None
result_df = df.iloc[table_indices.row_indices, :]
result_df = result_df.drop_duplicates()
color_df = result_df.copy()
color_df.loc[:,:] = ''
for row, col in zip(table_indices.row_indices, table_indices.col_indices):
color_df.loc[row, col] = 'background-color: yellow'
result_tab = pn.widgets.Tabulator(result_df,pagination="local", page_size=350, layout="fit_data",
width=800, height=300)
# cell coloration is working, but does not update properly unless empty search is run in between;
# otherwise it re-uses the most recent color scheme; maybe related to https://github.com/holoviz/panel/issues/3363
# result_tab.style.apply(lambda x: color_df, axis=None)
# giving up for now
return result_tab
def make_search_pane(result_tab):
if not result_tab:
return None
filename_2, button_2 = result_tab.download_menu(
text_kwargs={'name': 'Enter filename ending in .csv or .json', 'value': 'default.csv'},
button_kwargs={'name': 'Download search results'})
search_download_card = pn.Card(pn.pane.Markdown("""
Download search results in .csv or .json format.
File format will be automatically selected based on the file extension."""),
filename_2, button_2, header="### Download")
search_result = pn.Column(pn.pane.Markdown("""
### Scroll horizontally and vertically (if needed) to see everything.
"""), result_tab, search_download_card)
return search_result
# which columns to search
col_list = ['Name of Inventory Item',
'Primary Type of AI',
'Purpose of AI',
'Description of Inventory Item',
'Other Notes']
# create document store, where each string from any of the relevant columns is a doc
# save the row index as metadata
docs = []
indices = list(df.index.values)
for col in col_list:
values = df[col].tolist()
assert len(indices) == len(values)
for i, val in zip(indices, values):
dictionary = {'content' : val,
'meta' : {"index": i, "column_header" : col}
}
docs.append(Document.from_dict(dictionary))
document_store = InMemoryDocumentStore(use_bm25=True)
document_store.write_documents(docs)
retriever = BM25Retriever(document_store=document_store)
pipeline = DocumentSearchPipeline(retriever)
text_input = pn.widgets.TextInput(name='Search', placeholder='Enter text here...')
result_indices = pn.bind(run_search, text=text_input, pipeline=pipeline)
result_table = pn.bind(produce_table, df=df, table_indices=result_indices)
result_pane = pn.bind(make_search_pane, result_tab=result_table)
search_card = pn.Card(
pn.Column(
pn.Row(
text_input,
pn.pane.Markdown("""
This will search text in the following columns:
* Name of Inventory Item
* Primary Type of AI
* Purpose of AI
* Description of Inventory Item
* Other Notes
This is a keyword search based on the BM25 algorithm as implemented in the Haystack python library.
""")),
pn.Row(result_pane),
),
header="# Search the text"
)
main_text = """
The data visualized here come from a report by Anna Blue, a Social Impact Fellow
at the Responsible AI Institute. The report was released in May 2023. Some agencies have
released updated inventories since then, which are not reflected here.
Anna's report consolidated data released by individual government agencies in compliance with
Executive Order 13960, which requires federal agencies to produce an annual inventory of their AI usage.
See her [blog post](https://www.responsible.ai/post/federal-government-ai-use-cases) for additional details,
including links to the original data sources.
"""
template = pn.template.FastListTemplate(
title='U.S. Government Use of AI',
main=[pn.pane.Markdown(main_text),
pn.Row(overview_card,impact_card),
pn.Row(table_card),
pn.Row(search_card)],
accent_base_color="#FFDAC2",
header_background="#0037A2")
template.servable()
|