Update app.py

app.py

@@ -1,4 +1,13 @@
 # let's import the libraries
+from sentence_transformers import util
+from sentence_transformers import CrossEncoder
+from sentence_transformers import SentenceTransformer
+import sentence_transformers
+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
@@ -11,14 +20,6 @@ import io
 import netrc
 from tqdm import tqdm
 tqdm.pandas()
-import torch
-import os
-import sys
-import time
-import sentence_transformers
-from sentence_transformers import SentenceTransformer
-from sentence_transformers import CrossEncoder
-from sentence_transformers import util
 
 # let's load the english stsb dataset
 stsb_dataset = load_dataset('stsb_multi_mt', 'en')
@@ -26,7 +27,10 @@ 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")
+nlp = en_core_web_sm.load()
+
+#nlp = spacy.load("en_core_web_sm")
+
 
 def text_processing(sentence):
     sentence = [token.lemma_.lower()
@@ -34,10 +38,12 @@ def text_processing(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)
 
@@ -52,10 +58,10 @@ 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])
+    sentence_pairs.append([sentence1, sentence2])
 
 data['SBERT CrossEncoder_Score'] = XpathFinder.predict(
-      sentence_pairs, show_progress_bar=True)
+    sentence_pairs, show_progress_bar=True)
 
 loaded_model = XpathFinder
 
@@ -65,18 +71,19 @@ mod_container = st.container()
 
 # let's create the header
 with header_container:
-      st.title("SBERT CrossEncoder")
-      st.markdown("This is a demo of the SBERT CrossEncoder model")
+    st.title("SBERT CrossEncoder")
+    st.markdown("This is a demo of the SBERT CrossEncoder model")
 
 # let's create the model container
 with mod_container:
-   # let's get input from the user
-   prompt = st.text_input("Enter a description below...")
-
-   if prompt:
-      simscore = loaded_model.predict([prompt])
-      # sort the values
-      data['SBERT CrossEncoder_Score'] = simscore
-      most_acc = data.head(5)
-      st.write(most_acc)
-      st.write("The most accurate sentence is: ", most_acc['sentence2'].iloc[0])
+    # let's get input from the user
+    prompt = st.text_input("Enter a description below...")
+
+    if prompt:
+        simscore = loaded_model.predict([prompt])
+        # sort the values
+        data['SBERT CrossEncoder_Score'] = simscore
+        most_acc = data.head(5)
+        st.write(most_acc)
+        st.write("The most accurate sentence is: ",
+                 most_acc['sentence2'].iloc[0])