Create app.py

app.py

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+import streamlit as st 
+import pandas as pd 
+import numpy as np 
+import joblib 
+import tensorflow as tf 
+import nltk 
+
+from nltk.corpus import stopwords 
+from nltk.tokenize import word_tokenize 
+from nltk.stem import WordNetLemmatizer 
+
+import re 
+import ast 
+import string 
+from tensorflow.keras.models import load_model 
+
+nltk.download('wordnet')
+nltk.download('stopwords')
+nltk.download('omw-1.4')
+nltk.download('punkt')
+
+# open chatwords.txt
+with open('chatwords.txt') as f:
+    data = f.read()
+chatwords = ast.literal_eval(data)
+
+# open abbreviation.txt
+with open('abbreviation.txt') as abb:
+    ab2 = abb.read()
+abbreviation = ast.literal_eval(ab2)
+
+# define stopwords
+stop_words = stopwords.words('english')
+
+# define lemmatizer
+lem = WordNetLemmatizer()
+
+# load model
+final_gru = tf.keras.models.load_model('model_gru')
+
+# import functions
+def check_chatwords(text):
+    temp=[]
+    for chat in text.split()
+        if chat.upper() in chatwords:
+            temp.append(chatwords[chat.upper()])
+        else:
+            temp.append(chat)
+    return " ".join(temp)
+
+def lower(text):
+    data = text.lower()
+    return data 
+
+def check_abbr(text):
+    temp2=[]
+    for abbr in text.split():
+        if abbr in abbreviation:
+            temp2.append(abbreviation[abbr])
+        else:
+            temp2.append(abbr)
+
+    return " ".join(temp2)
+
+def check_punctuation(text):
+    data = re.sub("[^a-zA-Z]",' ', text)
+    data = re.sub("[[^]]*]", ' ', data)
+    data = re.sub(r"\n", " ", data)
+    data = data.strip()
+    data = ' '.join(data.split())
+    return data
+
+def token_stopwords_lemma(text):
+    tokens = word_tokenize(text)
+    stop_words2 = ' '.join([word for word in tokens if word not in stop_words])
+    data = [lem.lemmatize(word) for word in stop_words2.split()]
+    data = ' '.join(data)
+    return data 
+
+st.title("SPAM Message Detection")
+
+message = st.text_input('Please input your message here:')
+st.write('Message:', message)
+
+df_inf = [message]
+df_inf1 = pd.DataFrame()
+df_inf1['message'] = df_inf
+
+df_inf1['message'] = df_inf1['message'].apply(lambda j: check_chatwords(j))
+df_inf1['message'] = df_inf1['message'].apply(lambda k: lower(k))
+df_inf1['message'] = df_inf1['message'].apply(lambda v: check_abbr(v))
+df_inf1['message'] = df_inf1['message'].apply(lambda r: check_punctuation(r))
+df_inf1['message'] = df_inf1['message'].apply(lambda r: token_stopwords_lemma(m))
+
+y_pred_inf = final_gru.predict(df_inf1['message'])
+y_pred_inf = np.where(y_pred_inf >= 0.5, 1, 0)
+
+# Membuat dataframe dari array 
+pred_df = pd.DataFrame(y_pred_inf, columns=['label'])
+
+# Melakukan prediksi pada new dataframe
+df_inf2 = pd.DataFrame(df_inf, columns=['message'])
+df_combined = pd.concat([df_inf2, pred_df], axis=1)
+
+# Predict
+if st.button('Predict'):
+    y_pred_inf = final_gru.predict(df_inf1['message'])
+    y_pred_inf = np.where(y_pred_inf >= 0.5, 1, 0)
+    spam_status = str(y_pred_inf[0][0])
+
+    if spam_status == "0":
+        st.success("Your message is not spam.")
+    else:
+        st.error("Your message is spam")