Upload app.py

app.py

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+# let's import the libraries
+from sentence_transformers import util
+from sentence_transformers import CrossEncoder
+from sentence_transformers import SentenceTransformer
+import time
+import sys
+import os
+import torch
+import en_core_web_sm
+from email import header
+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import spacy
+from sklearn.metrics.pairwise import cosine_similarity
+from datasets import load_dataset
+import io
+import netrc
+from tqdm import tqdm
+tqdm.pandas()
+
+# Load the English STSB dataset
+stsb_dataset = load_dataset('stsb_multi_mt', 'en')
+stsb_train = pd.DataFrame(stsb_dataset['train'])
+stsb_test = pd.DataFrame(stsb_dataset['test'])
+
+# let's create helper functions
+nlp = spacy.load("en_core_web_sm")
+
+
+def text_processing(sentence):
+    sentence = [token.lemma_.lower()
+                for token in nlp(sentence)
+                if token.is_alpha and not token.is_stop]
+    return sentence
+
+
+def cos_sim(sentence1_emb, sentence2_emb):
+    cos_sim = cosine_similarity(sentence1_emb, sentence2_emb)
+    return np.diag(cos_sim)
+
+
+# let's read the csv file
+data = (pd.read_csv("SBERT_data.csv")).drop(['Unnamed: 0'], axis=1)
+
+prompt = "charles"
+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')
+
+XpathFinder = CrossEncoder("cross-encoder/stsb-roberta-base")
+sentence_pairs = []
+for sentence1, sentence2 in zip(data['sentence1'], data['sentence2']):
+    sentence_pairs.append([sentence1, sentence2])
+
+data['SBERT CrossEncoder_Score'] = XpathFinder.predict(
+    sentence_pairs, show_progress_bar=True)
+
+# sorting the values
+data.sort_values(by=['SBERT CrossEncoder_Score'], ascending=False)
+
+loaded_model = XpathFinder
+
+# 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
+        simscore = loaded_model.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"]))