reverting

.ipynb_checkpoints/app-checkpoint.py

@@ -3,6 +3,14 @@ import pandas as pd, spacy, nltk, numpy as np, re, os
 from spacy.matcher import Matcher
 from nltk.corpus import wordnet
 
+#Attempting to fix the issue with spacy model in a more intuitive way.
+try:
+    nlp = spacy.load("en_core_web_lg")
+except:
+    script = "python -m spacy download en_core_web_lg"
+    os.system("bash -c '%s'" % script)
+nlp = spacy.load("en_core_web_lg")
+
 #Import the libraries to support the model and predictions.
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
 import lime
@@ -27,17 +35,10 @@ def set_up_explainer():
 
 @st.experimental_singleton
 def prepare_model():
-    #Attempting to fix the issue with spacy model in a more intuitive way.
-    try:
-        nlp = spacy.load("en_core_web_lg")
-    except:
-        script = "python -m spacy download en_core_web_lg"
-        os.system("bash -c '%s'" % script)
-    nlp = spacy.load("en_core_web_lg")
     tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
     model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
     pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True) 
-    return tokenizer, model, pipe, nlp
+    return tokenizer, model, pipe
 
 @st.experimental_singleton
 def prepare_lists():
@@ -85,7 +86,7 @@ st.subheader(f'Current Layout: {layout}')
 text = st.text_input('Provide a sentence you want to evaluate.', placeholder = "I like you. I love you.", key="input")
 
 #Prepare the model, data, and Lime. Set starting variables.
-tokenizer, model, pipe, nlp = prepare_model()
+tokenizer, model, pipe = prepare_model()
 countries, professions, word_lists = prepare_lists()
 explainer = set_up_explainer()
 text2 = ""

app.py

@@ -3,6 +3,14 @@ import pandas as pd, spacy, nltk, numpy as np, re, os
 from spacy.matcher import Matcher
 from nltk.corpus import wordnet
 
+#Attempting to fix the issue with spacy model in a more intuitive way.
+try:
+    nlp = spacy.load("en_core_web_lg")
+except:
+    script = "python -m spacy download en_core_web_lg"
+    os.system("bash -c '%s'" % script)
+nlp = spacy.load("en_core_web_lg")
+
 #Import the libraries to support the model and predictions.
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
 import lime
@@ -27,17 +35,10 @@ def set_up_explainer():
 
 @st.experimental_singleton
 def prepare_model():
-    #Attempting to fix the issue with spacy model in a more intuitive way.
-    try:
-        nlp = spacy.load("en_core_web_lg")
-    except:
-        script = "python -m spacy download en_core_web_lg"
-        os.system("bash -c '%s'" % script)
-    nlp = spacy.load("en_core_web_lg")
     tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
     model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
     pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True) 
-    return tokenizer, model, pipe, nlp
+    return tokenizer, model, pipe
 
 @st.experimental_singleton
 def prepare_lists():
@@ -85,7 +86,7 @@ st.subheader(f'Current Layout: {layout}')
 text = st.text_input('Provide a sentence you want to evaluate.', placeholder = "I like you. I love you.", key="input")
 
 #Prepare the model, data, and Lime. Set starting variables.
-tokenizer, model, pipe, nlp = prepare_model()
+tokenizer, model, pipe = prepare_model()
 countries, professions, word_lists = prepare_lists()
 explainer = set_up_explainer()
 text2 = ""