functions needed

functions.py

@@ -0,0 +1,42 @@
+
+import re
+from contractions import contractions_dict
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+
+def sentiment_analysis_LR(input):
+    # Assuming you have a Logistic Regression model and TfidfVectorizer in the pipeline
+    input = preprocess_text(input)
+
+    vectorizer = model_LR.named_steps['tfidfvectorizer']
+    lr_classifier = model_LR.named_steps['logisticregression']
+
+    # Transform the user input using the TF-IDF vectorizer
+    user_input_tfidf = vectorizer.transform([input])
+
+    # Make predictions
+    user_pred = lr_classifier.predict(user_input_tfidf)
+
+    # Display the prediction
+    if user_pred[0] == 0:
+        return 0
+    else:
+        return 1
+
+def sentiment_analysis_NB(input):
+  input = preprocess_text(input)
+
+  vectorizer = model_NB.named_steps['tfidf']
+  nb_classifier = model_NB.named_steps['nb']
+
+  # Transform the user input using the TF-IDF vectorizer
+  user_input_tfidf = vectorizer.transform([input])
+
+  # Make predictions
+  user_pred = nb_classifier.predict(user_input_tfidf)
+
+  # Display the prediction
+  if user_pred[0] == 0:
+      return 0
+  else:
+      return 1