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	import io
	import netrc
	import pickle
	import sys
	import pandas as pd
	import numpy as np
	import streamlit as st
	# from sentence_transformers import SentenceTransformer
	# import sentence_transformers
	# import torch
	#######################################

	st.markdown(
	f"""
	<style>
	.reportview-container .main .block-container{{
	max-width: 90%;
	padding-top: 5rem;
	padding-right: 5rem;
	padding-left: 5rem;
	padding-bottom: 5rem;
	}}
	img{{
	max-width:40%;
	margin-bottom:40px;
	}}
	</style>
	""",
	unsafe_allow_html=True,
	)

	# # let's load the saved model
	# loaded_model = pickle.load(open('XpathFinder1.sav', 'rb'))
	# loaded_model = pickle.load('XpathFinder1.sav', map_location='cpu')


	# class CPU_Unpickler(pickle.Unpickler):
	# def find_class(self, module, name):
	# if module == 'torch.storage' and name == '_load_from_bytes':
	# return lambda b: torch.load(io.BytesIO(b), map_location='cpu')
	# else:
	# return super().find_class(module, name)
	#

	#loaded_model = CPU_Unpickler(open('XpathFinder1.sav', 'rb')).load()


	# Containers
	header_container = st.container()
	mod_container = st.container()

	# Header
	with header_container:

	# different levels of text you can include in your app
	st.title("Xpath Finder App")


	# model container
	with mod_container:
	# collecting input from user
	prompt = st.text_input("Enter your description below ...")

	# Loading e data
	data = (pd.read_csv("SBERT_data.csv")).drop(['Unnamed: 0'], axis=1)

	data['prompt'] = prompt
	data.rename(columns={'target_text': 'sentence2',
	'prompt': 'sentence1'}, inplace=True)
	data['sentence2'] = data['sentence2'].astype('str')
	data['sentence1'] = data['sentence1'].astype('str')

	# let's pass the input to the loaded_model with torch compiled with cuda
	if prompt:
	# let's get the result
	from sentence_transformers.cross_encoder import CrossEncoder
	XpathFinder = CrossEncoder("cross-encoder/stsb-roberta-base")
	sentence_pairs = []
	for sentence1, sentence2 in zip(data['sentence1'], data['sentence2']):
	sentence_pairs.append([sentence1, sentence2])
	simscore = XpathFinder.predict([prompt])

	# sorting the df to get highest scoring xpath_container
	data['SBERT CrossEncoder_Score'] = XpathFinder.predict(sentence_pairs)
	most_acc = data.head(5)
	# predictions
	st.write("Highest Similarity score: ", simscore)
	st.text("Is this one of these the Xpath you're looking for?")
	st.write(st.write(most_acc["input_text"]))