optimize computation

.ipynb_checkpoints/app-checkpoint.py

@@ -1,8 +1,6 @@
 #Import the libraries we know we'll need for the Generator.
-import pandas as pd, spacy, nltk, numpy as np, re
+import pandas as pd, spacy, nltk, numpy as np, re, os
 from spacy.matcher import Matcher
-!python -m spacy download en_core_web_lg
-nlp = spacy.load("en_core_web_lg")
 from nltk.corpus import wordnet
 
 #Import the libraries to support the model and predictions.
@@ -29,10 +27,17 @@ 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
+    return tokenizer, model, pipe, nlp
 
 @st.experimental_singleton
 def prepare_lists():
@@ -80,7 +85,7 @@ 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 = prepare_model()
+tokenizer, model, pipe, nlp = prepare_model()
 countries, professions, word_lists = prepare_lists()
 explainer = set_up_explainer()
 text2 = ""
@@ -347,4 +352,4 @@ if layout == 'VizNLC':
                 size=alt.Size('seed:O'),
                 tooltip=('Categories','text','pred')
             ).mark_circle(opacity=.5).properties(width=450, height=450).add_selection(single_nearest)
-            st.altair_chart(full)
+            st.altair_chart(full)

app.py

@@ -3,15 +3,6 @@ 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
@@ -36,10 +27,17 @@ 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
+    return tokenizer, model, pipe, nlp
 
 @st.experimental_singleton
 def prepare_lists():
@@ -87,7 +85,7 @@ 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 = prepare_model()
+tokenizer, model, pipe, nlp = prepare_model()
 countries, professions, word_lists = prepare_lists()
 explainer = set_up_explainer()
 text2 = ""