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# 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 st_aggrid import AgGrid
from utils.tapp_classifier import load_tappClassifier,load_targetClassifier, tapp_classification
import logging
logger = logging.getLogger(__name__)
from utils.config import get_classifier_params
from io import BytesIO
import xlsxwriter
import plotly.express as px
from pandas.api.types import (
is_categorical_dtype,
is_datetime64_any_dtype,
is_numeric_dtype,
is_object_dtype,
is_list_like)
# Declare all the necessary variables
tapp_classifier_identifier = 'tapp'
target_setfit = 'target'
param1 = get_classifier_params(tapp_classifier_identifier)
param2 = get_classifier_params(target_setfit)
def app():
### Main app code ###
with st.container():
if 'key0' in st.session_state:
df = st.session_state.key0
#load Classifiers
classifier = load_tappClassifier(classifier_name=param1['model_name'])
st.session_state['{}_classifier'.format(tapp_classifier_identifier)] = classifier
classifier = load_targetClassifier(classifier_name=param2['model_name'])
st.session_state['{}_classifier'.format(target_setfit)] = classifier
if len(df) > 100:
warning_msg = ": This might take sometime, please sit back and relax."
else:
warning_msg = ""
df = tapp_classification(haystack_doc=df,
threshold= param1['threshold'])
st.session_state.key1 = df
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