Create app.py

app.py

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+import streamlit as st
+import pickle
+from keras import Sequential
+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","Write here...")
+if st.button("Check"):
+    st.text("Analyzing may take upto a minute. Please be patient. Thank you!")
+    df=pd.read_csv("mail_data.csv")
+    df.loc[mail_data['Category'] == 'spam', 'Category'] = 0
+    df.loc[mail_data['Category'] == 'ham', 'Category'] = 1
+    X = df['Message']
+    Y = df['Category']
+    from keras.utils import pad_sequences
+    tokenizer = Tokenizer()
+    docs=X.astype("string")
+    tokenizer.fit_on_texts(docs)
+    sequences = tokenizer.texts_to_sequences(docs)
+    sequences = pad_sequences(sequences,padding='post',maxlen=61)
+    voc_size=len(tokenizer.word_index)
+    model = Sequential()
+    model.add(Embedding(voc_size+1,2,input_length=61))
+    model.add(Dense(5,activation="relu"))
+    model.add(Dense(5,activation="relu"))
+    model.add(Dense(1, activation='sigmoid'))
+    X=sequences
+    Y=Y.to_numpy()
+    Y=Y.astype("int")
+    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)
+    st.text("Input:")
+    st.markdown(Input)
+    st.text("Output:")
+    if prediction==0:
+        st.text("Spam")
+    else:
+        st.text("Not Spam")