Delete app.py

app.py

@@ -1,55 +0,0 @@
-import streamlit as st
-import pickle
-import string
-from nltk.corpus import stopwords
-import nltk
-from nltk.stem.porter import PorterStemmer
-
-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("Offensive/Non-Offensive 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("Offensive")
-    else:
-        st.header("Non-Offensive")