spamClassification text-classification en dataset:Kingsman2125/Spam-MailClassification-Dataset license:bsd-3-clause region:us
---
license: bsd-3-clause
datasets:
- Kingsman2125/Spam-MailClassification-Dataset
language:
- en
metrics:
- accuracy
- precision
pipeline_tag: text-classification
tags:
- spamClassification
---
# email-spam-classifier-new
```py
import streamlit as st
import pickle
import string
import pickle
from nltk.corpus import stopwords
import nltk
from nltk.stem.porter import PorterStemmer
nltk.download('stopwords')   # Downloading stopwords data
nltk.download('punkt')       # Downloading tokenizer data

ps = PorterStemmer()


def transform_text(text):
    text = text.lower()
    text = nltk.word_tokenize(text)

    y = []
    for i in text:
        if i.isalnum():
            y.append(i)

    text = y[:]
    y.clear()

    for i in text:
        if i not in stopwords.words('english') and i not in string.punctuation:
            y.append(i)

    text = y[:]
    y.clear()

    for i in text:
        y.append(ps.stem(i))

    return " ".join(y)

tfidf = pickle.load(open('vectorizer.pkl','rb'))
model = pickle.load(open('model.pkl','rb'))

st.title("Email/SMS Spam Classifier")

input_sms = st.text_area("Enter the message")

if st.button('Predict'):

    # 1. preprocess
    transformed_sms = transform_text(input_sms)
    # 2. vectorize
    vector_input = tfidf.transform([transformed_sms])
    # 3. predict
    result = model.predict(vector_input)[0]
    # 4. Display
    if result == 1:
        st.header("Spam")
    else:
        st.header("Not Spam")

```

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