| # 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, 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' | |
| param1 = get_classifier_params(tapp_classifier_identifier) | |
| 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 | |
| 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.dataframe(df) | |
| st.session_state.key1 = df |