Upload app.py

app.py

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+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")
+
+
+
+
+
+
+
+
+
+