Update app.py

app.py

@@ -1,128 +1,130 @@
-import streamlit as st
-import pickle
-import string
-from nltk.corpus import stopwords
-import nltk
-from nltk.stem.porter import PorterStemmer
-
-ps = PorterStemmer()
-
-tfidf = pickle.load(open("vectorizer.pkl", "rb"))
-model = pickle.load(open("model.pkl", "rb"))
-
-#st.title("EMail/SMS SPAM CLASSIFIER")
-#input_sms = st.text_area("Enter the Message", height=200)
-
-st.markdown(
-    f"""
-    <style>
-    body {{
-        background-image: url('D:\machine learning projects\email sms spam classifier\background.jpg') !important;
-        background-size: cover !important;
-        background-position: center !important;
-        background-repeat: no-repeat !important;
-        background-attachment: fixed !important;
-    }}
-    </style>
-    """,
-    unsafe_allow_html=True
-)
-
-st.markdown(
-    """
-    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/bootstrap-icons/1.10.5/font/bootstrap-icons.min.css">
-    <h1 style="color: #3498db; font-weight: bold; text-align: center; font-size: 36px;">
-        <i class="bi bi-envelope-fill" style="vertical-align: middle; font-size: 36px; margin-right: 10px;"></i>
-        EMail/SMS SPAM DETECTOR
-
-    <h1 style="color: #008000; font-weight: normal; text-align: center; font-size: 15px;">
-    Protect Your Inbox from Spam
-    
-    </h1>
-    """,
-    unsafe_allow_html=True
-)
-
-if 'input_sms' not in st.session_state:
-    st.session_state.input_sms = ""
-
-# Create a text area with the current text in session state
-st.session_state.input_sms = st.text_area("Enter your text here:", key="textarea", placeholder="Type here...",
-                                           value=st.session_state.input_sms, height=200)
-
-st.markdown(
-    """
-    <style>
-    textarea {
-        background-color: #2b2b2b !important;
-        color: white !important;
-        border-radius: 10px !important;
-        border: 2px solid #4CAF50 !important;
-        font-size: 16px !important;
-        padding: 10px !important;
-    }
-    </style>
-    """,
-    unsafe_allow_html=True,
-)
-
-# Create a "Clear Text" button
-col1, col2, col3, col4 = st.columns(4)
-
-with col3:
-    st.write("Double click")
-    if st.button("Clear Text"):
-        st.session_state.input_sms = ""  # Clear the text
-
-# Display the current text (optional)
-#st.write("Current text:", st.session_state.input_sms)
-
-with col4:
-    st.write(f"You wrote {len(st.session_state.input_sms)} characters.")
-
-# 1. Preprocess the text
-
-def transformed_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)
-
-transformed_sms = transformed_text(st.session_state.input_sms)
-
-# 2. Vectorize the text
-
-vectorize_input = tfidf.transform([transformed_sms])
-
-# 3. Predict
-
-result = model.predict(vectorize_input)[0]
-
-# 4. Check the output
-with col2:
-    st.write("Check result")
-    if st.button("Predict"):
-        if result == 1:
-            st.subheader(":red[SPAM]")
-
-        else:
-            st.subheader(":green[NOT SPAM]")
+import streamlit as st
+import pickle
+import string
+from nltk.corpus import stopwords
+import nltk
+from nltk.stem.porter import PorterStemmer
+
+ps = PorterStemmer()
+
+nltk.download('punkt')
+
+tfidf = pickle.load(open("vectorizer.pkl", "rb"))
+model = pickle.load(open("model.pkl", "rb"))
+
+#st.title("EMail/SMS SPAM CLASSIFIER")
+#input_sms = st.text_area("Enter the Message", height=200)
+
+st.markdown(
+    f"""
+    <style>
+    body {{
+        background-image: url('D:\machine learning projects\email sms spam classifier\background.jpg') !important;
+        background-size: cover !important;
+        background-position: center !important;
+        background-repeat: no-repeat !important;
+        background-attachment: fixed !important;
+    }}
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+st.markdown(
+    """
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/bootstrap-icons/1.10.5/font/bootstrap-icons.min.css">
+    <h1 style="color: #3498db; font-weight: bold; text-align: center; font-size: 36px;">
+        <i class="bi bi-envelope-fill" style="vertical-align: middle; font-size: 36px; margin-right: 10px;"></i>
+        EMail/SMS SPAM DETECTOR
+
+    <h1 style="color: #008000; font-weight: normal; text-align: center; font-size: 15px;">
+    Protect Your Inbox from Spam
+    
+    </h1>
+    """,
+    unsafe_allow_html=True
+)
+
+if 'input_sms' not in st.session_state:
+    st.session_state.input_sms = ""
+
+# Create a text area with the current text in session state
+st.session_state.input_sms = st.text_area("Enter your text here:", key="textarea", placeholder="Type here...",
+                                           value=st.session_state.input_sms, height=200)
+
+st.markdown(
+    """
+    <style>
+    textarea {
+        background-color: #2b2b2b !important;
+        color: white !important;
+        border-radius: 10px !important;
+        border: 2px solid #4CAF50 !important;
+        font-size: 16px !important;
+        padding: 10px !important;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+# Create a "Clear Text" button
+col1, col2, col3, col4 = st.columns(4)
+
+with col3:
+    st.write("Double click")
+    if st.button("Clear Text"):
+        st.session_state.input_sms = ""  # Clear the text
+
+# Display the current text (optional)
+#st.write("Current text:", st.session_state.input_sms)
+
+with col4:
+    st.write(f"You wrote {len(st.session_state.input_sms)} characters.")
+
+# 1. Preprocess the text
+
+def transformed_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)
+
+transformed_sms = transformed_text(st.session_state.input_sms)
+
+# 2. Vectorize the text
+
+vectorize_input = tfidf.transform([transformed_sms])
+
+# 3. Predict
+
+result = model.predict(vectorize_input)[0]
+
+# 4. Check the output
+with col2:
+    st.write("Check result")
+    if st.button("Predict"):
+        if result == 1:
+            st.subheader(":red[SPAM]")
+
+        else:
+            st.subheader(":green[NOT SPAM]")