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README.md

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+# Spam-message-detection
+Building model to detect spam messages specifically for India most probably by using NNs and ML

spam_app.py

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+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%
+    """)

spam_detector_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1b493180f86a79d71c0f8bb56f09c25627feb48b89e9bd20df1740295abd902d
+size 339087

tfidf_vectorizer.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ae27e1b70184ff42809e70f62e4f58bd9342ed9a2cf438508dd9991b08e6969c
+size 94260