util file for the usage of model

util.py

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+import pickle
+import numpy as np
+import spacy,re
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+import string,nltk
+
+prediction=[]
+
+def preprocess_text(text):
+    nltk.download('punkt')
+    nltk.download('stopwords')
+    text = re.sub(r'(\w)\1{2,}', r'\1', text)
+    tokens = word_tokenize(text)
+    tokens = [token for token in tokens if token not in string.punctuation]
+    tokens = [token.lower() for token in tokens]
+    stop_words = set(stopwords.words('english'))
+    tokens = [token for token in tokens ]
+    return " ".join(tokens)
+
+def remove_user_names(text):
+    return re.sub("@\w+|@\W+","",text.lower())
+
+def remove_web_url(text):
+    return re.sub("http:(..([a-zA-Z0-9_]+)+([a-zA-Z0-9_]+)..([a-zA-Z0-9_]+)..(([a-zA-Z0-9_]+))).(([a-zA-Z0-9_]+))","website",text)
+
+def remove_hastag(text):
+    return re.sub("#\w+","",text)
+
+def remove_all_special_char(text):
+    return re.sub("[^\w\s]","",text)
+
+def remove_more_space(text):
+    return re.sub(' +',' ',text)
+
+def remove(text):
+    return remove_more_space(remove_all_special_char(remove_hastag(remove_web_url(remove_user_names(text)))))
+
+nlp=spacy.load('en_core_web_lg')
+
+emotion_emojies={
+    "happiness":["😊","😂","🤣","😁","👌","👍","🙌","😜"],
+    "love":["❤️","💕","😘","😍"],
+    "sadness":["🤷‍♂️","😒","😫","😞"],
+    "empty":["🪹","🫙","📪"],
+    "enthusiasm":["😀","😃","🫡"],
+    "neutral":["😐","😶","🫥","😑"],
+    "worry":["😵‍💫","🤕","🥶","🤢"],
+    "surprise":["😯","😮","🤭","🎊","🤭","😲","🙀"],
+    "fun":["🎉","🎈","⚱️","😁"],
+    "hate":["👎","🤬","😡"],
+    "boredom":["🥱"],
+    "relief":["😌","😮‍💨"],
+    "anger":["🤐","😥","😴","😤","🤯","💢","😠"]
+}
+
+def predict(text):
+    with open('model.pkl','rb') as f:
+        model=pickle.load(f)
+        prediction.append(model.predict(np.stack(nlp(preprocess_text(remove(text))).vector).reshape(1,300)))
+        emoji=[emotion_emojies[emoji] for emoji in emotion_emojies.keys() if prediction[0][0]==emoji]
+    return emoji
+
+if "__main__"==__name__:
+    text=input("Input the Text : ")
+    print(predict(text))