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 = '1L7AHpWMVU_kZVLcsk8H2FTizgzeVxWPDoBxw7K8KHXw'
SHEET_NAME = 'model'
csv_url = f'https://docs.google.com/spreadsheets/d/{SHEET_ID}/gviz/tq?tqx=out:csv&sheet={SHEET_NAME}'

class ModelList:
    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%">Year Published</td>
                <td width="10%">Source</td>
                <td width="30%">About</td>
                <td width="10%">Task</td>
                <td width="15%">Training Data Type</td>
                <td width="10%">Publication</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.Source}" target="_blank">Link</a>' if isinstance(
                row.Source, str) else ''
            paper = f'<a href="{row.Paper}" target="_blank">Link</a>' if isinstance(
                row.Source, str) else ''
            row = f'''
                <tr>
                    <td>{row.Name}</td>
                    <td>{row.Year}</td>
                    <td>{source}</td>
                    <td>{row.About}</td>
                    <td>{row.task}</td>
                    <td>{row.data}</td>
                    <td>{paper}</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.task.isin(set(filter_names))]
        df = df.loc[df.data.isin(set(data_types))]
        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
model_list = ModelList()
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("# Models 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_names1 = gr.CheckboxGroup(choices=['NLP','Computer Vision', 'Multi-Model'], value=['NLP','Computer Vision', 'Multi-Model'], label='Task')
            data_type_names1 = ['Biomedical Corpus','Scientific Corpus','Clinical Corpus','Image','Mixed']
            data_types1 = gr.CheckboxGroup(choices=data_type_names1, value=data_type_names1, label='Training Data Type')
            search_button = gr.Button('Search')
            table = gr.HTML(show_label=False)
            demo.load(fn=model_list.render, inputs=[search_box, case_sensitive, filter_names1, data_types1,],outputs=[table,])
            search_box.submit(fn=model_list.render, inputs=[search_box, case_sensitive, filter_names1, data_types1,], outputs=[table,])
            search_button.click(fn=model_list.render, inputs=[search_box, case_sensitive, filter_names1, data_types1,], outputs=[table,])
demo.queue()
demo.launch(share=False)