Update app.py

app.py

@@ -1,89 +1,88 @@
-import streamlit as st
-import joblib
-import nltk
-from nltk.corpus import stopwords
-from nltk.tokenize import word_tokenize
-import string
-import re
-
-# Download NLTK data
-nltk.download('punkt')
-nltk.download('stopwords')
-
-def preprocess_text(text):
-    # Convert to lowercase
-    text = text.lower()
-    
-    # Remove punctuation
-    text = ''.join([char for char in text if char not in string.punctuation])
-    
-    # Remove numbers
-    text = re.sub(r'\d+', '', text)
-    
-    # Remove extra whitespace
-    text = ' '.join(text.split())
-    
-    # Tokenization
-    tokens = word_tokenize(text)
-    
-    # Remove stopwords
-    stop_words = set(stopwords.words('english'))
-    tokens = [token for token in tokens if token not in stop_words]
-    
-    # Join tokens back into text
-    return ' '.join(tokens)
-
-# Load the saved model and vectorizer
-model = joblib.load('spam_detector_model.joblib')
-vectorizer = joblib.load('tfidf_vectorizer.joblib')
-
-# Create the Streamlit interface
-st.title("📧 Spam Message Detector")
-
-st.write("""
-This app detects whether a message is spam or not. 
-Enter your message below and click 'Analyze' to check!
-""")
-
-# Create text input
-message = st.text_area("Enter your message:", height=100)
-
-if st.button("Analyze"):
-    if message:
-        # Preprocess the input
-        processed_text = preprocess_text(message)
-        
-        # Vectorize the text
-        text_vectorized = vectorizer.transform([processed_text])
-        
-        # Make prediction
-        prediction = model.predict(text_vectorized)[0]
-        probability = model.predict_proba(text_vectorized)[0]
-        
-        # Display result
-        st.markdown("### Analysis Result")
-        
-        if prediction == 1:
-            st.error("🚨 This message is likely SPAM!")
-            st.write(f"Confidence: {probability[1]:.2%}")
-        else:
-            st.success("✅ This message appears to be legitimate.")
-            st.write(f"Confidence: {probability[0]:.2%}")
-        
-        # Show preprocessing details
-        with st.expander("See preprocessing steps"):
-            st.write("Original message:", message)
-            st.write("Processed message:", processed_text)
-    else:
-        st.warning("Please enter a message to analyze.")
-
-# Add sidebar information
-with st.sidebar:
-    st.header("About the Model")
-    st.write("""
-    This spam detector uses an XGBoost classifier trained on a dataset of spam and legitimate messages.
-    
-    Model Performance:
-    - Training Accuracy: 99.7%
-    - Testing Accuracy: 98.9%
-    """)
+import streamlit as st
+import joblib
+import nltk
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+import string
+import re
+
+
+nltk.download('punkt')
+nltk.download('stopwords')
+
+def preprocess_text(text):
+
+    text = text.lower()
+    
+
+    text = ''.join([char for char in text if char not in string.punctuation])
+    
+
+    text = re.sub(r'\d+', '', text)
+    
+
+    text = ' '.join(text.split())
+    
+
+    tokens = word_tokenize(text)
+    
+
+    stop_words = set(stopwords.words('english'))
+    tokens = [token for token in tokens if token not in stop_words]
+    
+ 
+    return ' '.join(tokens)
+
+
+model = joblib.load('spam_detector_model.joblib')
+vectorizer = joblib.load('tfidf_vectorizer.joblib')
+
+
+st.title("📧 Spam Message Detector")
+
+st.write("""
+This app detects whether a message is spam or not. 
+Enter your message below and click 'Analyze' to check!
+""")
+
+message = st.text_area("Enter your message:", height=100)
+
+if st.button("Analyze"):
+    if message:
+  
+        processed_text = preprocess_text(message)
+        
+
+        text_vectorized = vectorizer.transform([processed_text])
+        
+
+        prediction = model.predict(text_vectorized)[0]
+        probability = model.predict_proba(text_vectorized)[0]
+        
+
+        st.markdown("### Analysis Result")
+        
+        if prediction == 1:
+            st.error("🚨 This message is likely SPAM!")
+            st.write(f"Confidence: {probability[1]:.2%}")
+        else:
+            st.success("✅ This message appears to be legitimate.")
+            st.write(f"Confidence: {probability[0]:.2%}")
+        
+
+        with st.expander("See preprocessing steps"):
+            st.write("Original message:", message)
+            st.write("Processed message:", processed_text)
+    else:
+        st.warning("Please enter a message to analyze.")
+
+
+with st.sidebar:
+    st.header("About the Model")
+    st.write("""
+    This spam detector uses an XGBoost classifier trained on a dataset of spam and legitimate messages.
+    
+    Model Performance:
+    - Training Accuracy: 99.7%
+    - Testing Accuracy: 98.9%
+    """)