import gradio as gr
import pandas as pd
from load_data import *
import os





hf_writer = gr.HuggingFaceDatasetSaver('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', "Pavankalyan/chitti_data")


def chitti(query):
    re_table = search(query)
    answers_re_table = [re_table[i][0] for i in range(0,5)]
    answer_links = [re_table[i][3] for i in range(0,5)]
    sorted_indices = sorted(range(len(answers_re_table)), key=lambda k: len(answers_re_table[k]))
    repeated_answers_indices =list()
    for i in range(4):
        if answers_re_table[sorted_indices[i]] in answers_re_table[sorted_indices[i+1]]:
            repeated_answers_indices.append(sorted_indices[i])
    for idx in repeated_answers_indices:
        answers_re_table.pop(idx)
        answer_links.pop(idx)
        
    #return [res1,answers_re_table[0],res2,answers_re_table[1]]
    return [answers_re_table[0],answers_links[0],answers_re_table[1],answer_links[1]]

demo = gr.Interface(
    fn=chitti,
    inputs=["text"],
    outputs=["text","text","text","text"],
    allow_flagging = "manual",
    flagging_options = ["0","1","None"],
    flagging_callback=hf_writer
)
demo.launch()