Update app.py

app.py

@@ -4,6 +4,7 @@ import nltk
 import string
 # import for loading python objects (scikit-learn models)
 import pickle
+import streamlit as st
 
 def custom_tokenizer_with_Turkish_stemmer(text):
     # my text was unicode so I had to use the unicode-specific translate function. If your documents are strings, you will need to use a different `translate` function here. `Translated` here just does search-replace. See the trans_table: any matching character in the set is replaced with `None`
@@ -35,15 +36,8 @@ def predictSMSdata(test_text):
 trans_table = {ord(c): None for c in string.punctuation + string.digits}
 stemmerTR = TurkishStemmer()
 
-# Extra test data from the training set
-# legitimate - l0430.txt
-predictSMSdata("Ahmet de gelecek mi?")
-
-# legitimate - l0429.txt
-predictSMSdata("Vakifbank WebSubem girisi icin tek kullanimlik sifreniz: 160038 . Sifreniz 3 dk gecerlidir. Tarih: 14.02.2011 Saat: 13:53")
-
-# spam - s0003.txt
-predictSMSdata("Aveadan SUPER bir Muzik Paketi! MAXI yaz, 5555e gonder, Maxi Muzikindir Paketi ile 150 yerli 50 Yabanci sarkiyi ayda sadece 5,99 TLye cebine indir!")
-
-# spam - s0359.txt
-predictSMSdata("1-2 NISAN TARIHLERINDE;DERMALOGICA CILT BAKIMINA DAVETLISINIZ.RANDEVU ALINIZ TEL:2312840")
+text = st.text_area("enter some text!")
+if text:
+    out = predictSMSdata(text)
+    st.json(out)
+