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