| # set path | |
| import glob, os, sys | |
| sys.path.append('../utils') | |
| #import needed libraries | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| from utils.adapmit_classifier import load_adapmitClassifier,adapmit_classification | |
| # from utils.keyword_extraction import textrank | |
| import logging | |
| logger = logging.getLogger(__name__) | |
| from utils.config import get_classifier_params | |
| from utils.preprocessing import paraLengthCheck | |
| from io import BytesIO | |
| import xlsxwriter | |
| import plotly.express as px | |
| # Declare all the necessary variables | |
| classifier_identifier = 'adapmit' | |
| params = get_classifier_params(classifier_identifier) | |
| def app(): | |
| ### Main app code ### | |
| with st.container(): | |
| if 'key1' in st.session_state: | |
| df = st.session_state.key1 | |
| classifier = load_adapmitClassifier(classifier_name=params['model_name']) | |
| st.session_state['{}_classifier'.format(classifier_identifier)] = classifier | |
| df = adapmit_classification(haystack_doc=df, | |
| threshold= params['threshold']) | |
| st.session_state.key1 = df | |