Update app.py

app.py

@@ -5,7 +5,7 @@ from keras.layers import Dense,Embedding
 from keras.utils import pad_sequences
 from keras.preprocessing.text import Tokenizer
 st.title("Spam-NonSpam Detector")
-Input=st.text_input("Input the message below",)
+Input=st.text_area("Input the message below")
 if st.button("Check"):
     st.text("Process may take upto a minute. Please be patient. Thank you!")
     df=pd.read_csv("mail_data.csv")
@@ -31,12 +31,15 @@ if st.button("Check"):
     Y=Y.reshape(-1,1)
     model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])
     model.fit(X,Y,epochs=21)
-    InputDataFeatures=cv.transform([Input])
-    prediction=model.predict(InputDataFeatures)
+    seq=tokenizer.texts_to_sequences(input_mail)
+    inp=pad_sequences(seq,padding='post',maxlen=61)
+    a=model.predict(inp)
+    value=a.argmax()
     st.text("Input:")
     st.markdown(Input)
     st.text("Output:")
-    if prediction==0:
-        st.text("Spam")
+    if (value==1):
+      st.text('Non-spam message')
     else:
-        st.text("Not Spam")
+      st.text('Spam message')
+