Updated stop word removal in app.py.

app.py

@@ -8,13 +8,18 @@ import nltk
 from nltk.corpus import stopwords
 nltk.download('stopwords')
 from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.feature_extraction.text import TfidfVectorizer
 
 # file name
 lr_filename = 'logistic_regression.pkl'
 
+# Load model from pickle file
+model = pickle.load(open(lr_filename, 'rb'))
+
 # Process input text, including removing stopwords, converting to lowercase, and removing punctuation
+stop = stopwords.words('english')
 def process_text(text):
-    text = stopwords.words('english')
+    text = [word for word in text.split() if word not in stop]
     text = str(text).lower()
     text = re.sub(
         f"[{re.escape(string.punctuation)}]", " ", text
@@ -29,10 +34,6 @@ def vectorize_text(text):
     text = vectorizer.fit_transform([text])
     return text
 
-# Load model from pickle file
-model = pickle.load(open(lr_filename, 'rb'))
-
-
 def predict(text):
     text = vectorize_text(text)
     prediction = model.predict(text)