cleaning spacy

.ipynb_checkpoints/NLselector-checkpoint.py

@@ -2,7 +2,7 @@
 import pandas as pd, spacy, nltk, numpy as np, re
 from spacy.matcher import Matcher
 #!python -m spacy download en_core_web_md #Not sure if we need this so I'm going to keep it just in case
-nlp = spacy.load("Assets/Models/en_core_web_lg")
+nlp = spacy.load("en_core_web_lg")
 import altair as alt
 import streamlit as st
 from annotated_text import annotated_text as ant

.ipynb_checkpoints/WNgen-checkpoint.py

@@ -2,7 +2,7 @@
 import re, nltk, pandas as pd, numpy as np, ssl, streamlit as st
 from nltk.corpus import wordnet
 import spacy
-nlp = spacy.load("Assets/Models/en_core_web_lg")
+nlp = spacy.load("en_core_web_lg")
 
 #Import necessary parts for predicting things.
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline

.ipynb_checkpoints/app-checkpoint.py

@@ -1,7 +1,7 @@
 #Import the libraries we know we'll need for the Generator.
 import pandas as pd, spacy, nltk, numpy as np
 from spacy.matcher import Matcher
-nlp = spacy.load("Assets/Models/en_core_web_lg")
+nlp = spacy.load("en_core_web_lg")
 
 #Import the libraries to support the model and predictions.
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline

NLselector.py

@@ -2,7 +2,7 @@
 import pandas as pd, spacy, nltk, numpy as np, re
 from spacy.matcher import Matcher
 #!python -m spacy download en_core_web_md #Not sure if we need this so I'm going to keep it just in case
-nlp = spacy.load("Assets/Models/en_core_web_lg")
+nlp = spacy.load("en_core_web_lg")
 import altair as alt
 import streamlit as st
 from annotated_text import annotated_text as ant

WNgen.py

@@ -2,7 +2,7 @@
 import re, nltk, pandas as pd, numpy as np, ssl, streamlit as st
 from nltk.corpus import wordnet
 import spacy
-nlp = spacy.load("Assets/Models/en_core_web_lg")
+nlp = spacy.load("en_core_web_lg")
 
 #Import necessary parts for predicting things.
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline

app.py

@@ -1,7 +1,7 @@
 #Import the libraries we know we'll need for the Generator.
 import pandas as pd, spacy, nltk, numpy as np
 from spacy.matcher import Matcher
-nlp = spacy.load("Assets/Models/en_core_web_lg")
+nlp = spacy.load("en_core_web_lg")
 
 #Import the libraries to support the model and predictions.
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline