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import gradio as gr | |
import pandas as pd | |
SHEET_ID = '1eaqWOSqNAY64E8jce5R8APAe12WFGga7f0xK3Ygt9HY' | |
SHEET_NAME = 'Sheet1' | |
csv_url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}' | |
class DataList: | |
def __init__(self): | |
self.table = pd.read_csv(csv_url) | |
self._preprocess_table() | |
self.table_header = ''' | |
<tr> | |
<td width="12%">Name</td> | |
<td width="52%">Description</td> | |
<td width="12%">Module</td> | |
<td width="12%">Type</td> | |
<td width="12%">Alignment with Key Guidance</td> | |
</tr>''' | |
def _preprocess_table(self) -> None: | |
self.table['name_lowercase'] = self.table['Name'].str.lower() | |
rows = [] | |
for row in self.table.itertuples(): | |
source = f'<a href="{row.URL}" target="_parent">{row.Name}</a>' if isinstance( | |
row.URL, str) else '{row.Name}' | |
row = f''' | |
<tr> | |
<td>{source}</td> | |
<td>{row.Description}</td> | |
<td>{row.Module}</td> | |
<td>{row.Type}</td> | |
<td>{row.Alignment}</td> | |
</tr>''' | |
rows.append(row) | |
self.table['html_table_content'] = rows | |
def render(self, search_query: str, | |
filter_names: list[str], | |
filter_names2: list[str], | |
filter_names3: list[str] | |
) -> tuple[int, str]: | |
self.table = pd.read_csv(csv_url) | |
self._preprocess_table() | |
self.table_header = ''' | |
<tr> | |
<td width="12%">Name</td> | |
<td width="52%">Description</td> | |
<td width="12%">Module</td> | |
<td width="12%">Type</td> | |
<td width="12%">Alignment with Key Guidance</td> | |
</tr>''' | |
df = self.table | |
if search_query: | |
df = df[df.name_lowercase.str.contains(search_query.lower())] | |
df = self.filter_table(df, filter_names,filter_names2,filter_names3) | |
result = self.to_html(df, self.table_header) | |
return result | |
def filter_table(df: pd.DataFrame, filter_names: list[str], filter_names2: list[str], filter_name3: list[str],) -> pd.DataFrame: | |
df = df.loc[df.Module.isin(set(filter_names))] | |
print(filter_name3) | |
vals= filter_names3 | |
df = df.loc[df.Module.isin(vals)] | |
return df | |
def to_html(df: pd.DataFrame, table_header: str) -> str: | |
table_data = ''.join(df.html_table_content) | |
html = f''' | |
<table> | |
{table_header} | |
{table_data} | |
</table>''' | |
return html | |
data_list = DataList() | |
css = """ | |
button.svelte-kqij2n{font-weight: bold !important; | |
background-color: #ebecf0; | |
color: black; | |
margin-left: 5px;} | |
#tlsnlbs{} | |
#mtcs{} | |
#mdls{} | |
#dts{} | |
.svelte-kqij2n .selected { | |
background-color: black; | |
color: white; | |
} | |
.app.svelte-182fdeq.svelte-182fdeq { | |
padding: 0 !important; | |
} | |
span.svelte-s1r2yt{font-weight: bold !important; | |
} | |
""" | |
with gr.Blocks(css=css) as demo: | |
with gr.Row(): | |
search_box = gr.Textbox( label='Search Name', placeholder='You can search for titles with regular expressions. e.g. (?<!sur)face',max_lines=1, scale = 5) | |
with gr.Row(): | |
with gr.Column(scale=1): | |
filter_names = gr.CheckboxGroup(choices=['Compliance and Regulatory Guidance','Generative AI','Training and Education',], value=['Guidebook','Assessment Tool','Training and Education',], label='Type') | |
with gr.Column(scale=1): | |
filter_names2 = gr.CheckboxGroup(choices=['NIST AI RMF MAP','NIST AI RMF GOVERN','ISO 42001 A.8',], value=['NIST AI RMF MAP','NIST AI RMF GOVERN','ISO 42001 A.8',], label='Alignment with Key Guidance') | |
with gr.Row(): | |
filter_names3 = gr.CheckboxGroup(choices=['Compliance and Regulatory Guidance', 'Generative AI', 'Suppliers and Procurement', 'Policy and Governance', 'Assessing AI Systems',], value=['Compliance and Regulatory Guidance', 'Generative AI', 'Suppliers and Procurement', 'Policy and Governance', 'Assessing AI Systems',], label='Modules', interactive = True) | |
with gr.Row(): | |
search_button = gr.Button('Search', size = 'sm', scale =1) | |
with gr.Row(): | |
table = gr.HTML(show_label=False) | |
demo.load(fn=data_list.render, inputs=[search_box, filter_names,filter_names2,filter_names3,],outputs=[table,]) | |
search_box.submit(fn=data_list.render, inputs=[search_box, filter_names,filter_names2,filter_names3,], outputs=[table,]) | |
search_button.click(fn=data_list.render, inputs=[search_box, filter_names,filter_names2,filter_names3,], outputs=[table,]) | |
demo.queue() | |
demo.launch(share=False) | |