Text to Vector , Preprocessing and Loading to mode

 Hi

import nltk

from nltk.corpus import stopwords

from nltk.stem.porter import PorterStemmer

import pandas as pd

import re

nltk.download('stopwords')

port_stem = PorterStemmer()

def stemming(content):

    stemmed_content = re.sub('[^a-zA-Z]',' ',content)

    stemmed_content = stemmed_content.lower()

    stemmed_content = stemmed_content.split()

    stemmed_content = [port_stem.stem(word) for word in stemmed_content if not word in stopwords.words('english')]

    stemmed_content = ' '.join(stemmed_content)

    return stemmed_content

hupstr=input('Enter the tweet')

df1 = pd.DataFrame({'text': [hupstr]})

df1['text']  = df1['text'].apply(stemming)

from keras.models import load_model

hupmodel= load_model("hupmodel17.h5")

loaded_vectorizer = joblib.load('huptokenizer.joblib')

X2 = loaded_vectorizer.texts_to_sequences(df1['text'])

X2 = pad_sequences(X2,maxlen=337)

#X_test_transformed = loaded_vectorizer.transform(hupinput)

#X_test_dense = X_test_transformed.toarray()

hupprediction1 = hupmodel.predict_on_batch(np.stack(X2))

labelencoder = joblib.load('huplabelencoder.pkl')

huplabel1 = labelencoder.inverse_transform(np.argmax(hupprediction1, axis=1))

hupresult1 = ''.join(huplabel1)

print(hupresult1)