Updated pickel files and app.py.

app.py

@@ -12,12 +12,11 @@ from sklearn.feature_extraction.text import TfidfVectorizer
 from flair.data import Sentence
 from flair.models import SequenceTagger
 
-# file name
-#lr_filename = 'lr_021223.pkl'
-
-# Load model from pickle file
-# model = pickle.load(open(lr_filename, 'rb'))
-
+# Load pickled model and vectorizer
+model = 'lr_021823.pkl'
+model_loaded = pickle.load(open(model, 'rb'))
+vectorizer = 'vectorizer_021823.pkl'
+vectorizer_loaded = pickle.load(open(vectorizer, 'rb'))
 
 # Process input text, including removing stopwords, converting to lowercase, and removing punctuation
 stop = stopwords.words('english')
@@ -30,32 +29,19 @@ def process_text(text):
     text = " ".join(text.split())
     return text
 
-# Vectorize input text
-vec = CountVectorizer()
-'''
+# Vectorize text
 def vectorize_text(text):
-    #text = process_text(text)
-    #text = vectorizer.fit_transform([text])
-    #return text
-'''
+    text = process_text(text)
+    text = vectorizer_loaded.transform([text])
+    return text
 
 # Valid input for the model so number of features match
-def predict(text):
-    # Load the pickled model
-    filename = 'lr_021223.pkl'
-    loaded_model = pickle.load(open(filename, 'rb'))
-    text = vec.transform([text])
+def class_predict(text):
     text = process_text(text)
-    prediction = loaded_model.predict(text)
+    vec = vectorizer_loaded.transform([text])
+    prediction = model_loaded.predict(vec)
     return prediction
 
-'''
-Prediction function
-#def predict(text):
-    #text = vectorize_text(text)
-    #prediction = model.predict(text)
-    #return prediction
-'''
 
 # Specify NER model
 tagger = SequenceTagger.load('best-model.pt') # SequenceTagger.load('best-model.pt')
@@ -71,7 +57,7 @@ def run_ner(input_text):
 
 # Run both models, and return a tuple of their results
 def run_models(input_text):
-    prediction = 0 # This "0" is a placeholder to avoid errors; once the LR model is working, use this instead: prediction = predict(input_text)
+    prediction = class_predict(input_text)
     entities = run_ner(input_text)
     return prediction, entities