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import streamlit as st
from importlib.machinery import PathFinder
import io
import netrc
import pickle
import sys
import pandas as pd
import numpy as np
import streamlit as st
# let's import sentence transformer
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('https://drive.google.com/file/d/1CUGbhyT8M4wU_y6FDYS5LBngcgORjGej/view?usp=sharing', 'rb'))
# 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("/content/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
simscore = PathFinder.predict([prompt])
from sentence_transformers import CrossEncoder
loaded_model = CrossEncoder("cross-encoder/stsb-roberta-base")
sentence_pairs = []
for sentence1, sentence2 in zip(data['sentence1'], data['sentence2']):
sentence_pairs.append([sentence1, sentence2])
# sorting the df to get highest scoring xpath_container
data['SBERT CrossEncoder_Score'] = loaded_model.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"]))
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