Update app.py

app.py

@@ -1,95 +1,10 @@
-from __future__ import annotations
-import gradio as gr
-import numpy as np
-import pandas as pd
-import requests
-from huggingface_hub.hf_api import SpaceInfo
-
-SHEET_ID = '1BWKw2ygYQUUPcNdSJhW9OkXILWO5i-dCa5Uahn9dHNo'
-SHEET_NAME = 'Datasets'
-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.table = self.table.astype({'Year':'string'})
-        self._preprocess_table()
-
-        self.table_header = '''
-            <tr>
-                <td width="15%">Name</td>
-                <td width="10%">URL</td>
-                <td width="30%">About</td>
-                <td width="15%">Publisher</td>
-                <td width="10%">Year Updated</td>
-                <td width="10%">Type</td>
-                <td width="10%">Tag</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="_blank">Link</a>' if isinstance(
-                row.URL, str) else ''
-            row = f'''
-                <tr>
-                    <td>{row.Name}</td>
-                    <td>{source}</td>
-                    <td>{row.About}</td>
-                    <td>{row.Publisher}</td>
-                    <td>{row.Year}</td>
-                    <td>{row.Type}</td>
-                    <td>{row.Tags}</td>
-                </tr>'''
-            rows.append(row)
-        self.table['html_table_content'] = rows
-
-    def render(self, search_query: str, 
-            case_sensitive: bool,
-            filter_names: list[str],
-            data_types: list[str]) -> tuple[int, str]:
-        df = self.table
-        if search_query:
-            if case_sensitive:
-                df = df[df.name.str.contains(search_query)]
-            else:
-                df = df[df.name_lowercase.str.contains(search_query.lower())]
-        df = self.filter_table(df, filter_names, data_types)
-        result = self.to_html(df, self.table_header)
-        return result
-
-    @staticmethod
-    def filter_table(df: pd.DataFrame, filter_names: list[str], data_types: list[str]) -> pd.DataFrame:
-        df = df.loc[df.Type.isin(set(filter_names))]
-        df = df.loc[df.Tags.isin(set(data_types))]
-        return df
+import gradio as gr
 
-    @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()
 with gr.Blocks() as demo:
             with gr.Row():
                 gr.Image(value="RAII.svg",scale=1,show_download_button=False,show_share_button=False,show_label=False,height=100,container=False) 
                 gr.Markdown("# Datasets for Healthcare Teams")
-            search_box = gr.Textbox( label='Search Name', placeholder='You can search for titles with regular expressions. e.g. (?<!sur)face',max_lines=1)
-            case_sensitive = gr.Checkbox(label='Case Sensitive')
-            filter_names = gr.CheckboxGroup(choices=['Real Data','Synthetic Data',], value=['Real Data','Synthetic Data',], label='Type')
-            data_type_names = ['Claims','Scientific','Corpus',]
-            data_types = gr.CheckboxGroup(choices=data_type_names, value=data_type_names, label='Tags')
-            search_button = gr.Button('Search')
-            table = gr.HTML(show_label=False)
-            demo.load(fn=data_list.render, inputs=[search_box, case_sensitive, filter_names, data_types,],outputs=[table,])
-            search_box.submit(fn=data_list.render, inputs=[search_box, case_sensitive, filter_names, data_types,], outputs=[table,])
-            search_button.click(fn=data_list.render, inputs=[search_box, case_sensitive, filter_names, data_types,], outputs=[table,])
+
 demo.queue()
 demo.launch(share=False)