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

    @staticmethod
    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

    @staticmethod
    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)