Delete app.py

app.py

@@ -1,77 +0,0 @@
-import streamlit as st
-import pickle
-import string
-from nltk.corpus import stopwords
-import nltk
-nltk.download('punkt')
-nltk.download('stopwords')
-
-
-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("EMAIL/SMS SPAM CLASSIFIER")
-
-#follow documentation for syntax and fn
-
-input_sms=st.text_input("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("make some friends loner")
-
-    else:
-        st.header("not spam uwu")
-
-
-
-
-
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-