app.py

from os import write
from typing import Sequence
import streamlit as st
from hf_model import classifier_zero,load_model
from utils import plot_result
import json
classifier=load_model()
with open("examples.json") as f:
    data=json.load(f)

if __name__ == '__main__':
    st.header("Zero Shot Classification")
    st.write("This app allows you to classify any text into any categories you are interested in.")

    with st.form(key='my_form'):
        text_input = st.text_area("Input Text",data['text'])
        labels = st.text_input('Possible topics (separated by `,`)',data["labels"], max_chars=1000)
        labels = list(set([x.strip() for x in labels.strip().split(',') if len(x.strip()) > 0]))
        radio = st.radio("Select Multiclass",('Only one topic can be corect at a time','Multiple topics can be correct at a time'),)
        multi_class= True if radio=="Multiple topics can be correct at a time" else False
        submit_button = st.form_submit_button(label='Submit')
    if submit_button:
        if len(labels) == 0:
            st.write('Enter some text and at least one possible topic to see predictions.')
        top_topics, scores = classifier_zero(classifier,sequence=text_input,labels=labels,multi_class=multi_class)
        plot_result(top_topics[::-1][-10:], scores[::-1][-10:])