Update app.py

app.py

@@ -4,15 +4,21 @@ import joblib
 import re
 import string
 
-# Page configuration
+# Page config
 st.set_page_config(page_title="SMS Spam Detector", layout="centered")
 st.title("📩 SMS Spam Detection App")
 st.markdown("🔍 Enter a message below to check if it's **Spam** or **Not Spam (Ham)**")
 
-# --- Load Model and Vectorizer ---
-# These should be .pkl files, saved using joblib.dump() in your training script
-model = joblib.load("model/spam_model.pkl")              # Correct path to trained model
-vectorizer = joblib.load("model/tfidf_vectorizer.pkl")   # Correct path to vectorizer
+# --- Load CSV for reference or stats ---
+csv_path = "https://huggingface.co/spaces/MLDeveloper/Spam_SMS_Detection/resolve/main/spam_sms_detection.csv"
+try:
+    df = pd.read_csv(csv_path)
+except Exception as e:
+    st.error(f"Error loading dataset: {e}")
+
+# --- Load trained model & vectorizer ---
+model = joblib.load("model/spam_model.pkl")  # ✅ your trained model
+vectorizer = joblib.load("model/tfidf_vectorizer.pkl")  # ✅ your TF-IDF vectorizer
 
 # --- Text Cleaning Function ---
 def clean_text(text):
@@ -31,7 +37,7 @@ def predict_spam(message):
     prediction = model.predict(vector)
     return "Spam" if prediction[0] == 1 else "Not Spam"
 
-# --- Input Section ---
+# --- UI for prediction ---
 user_input = st.text_area("✉️ Enter your SMS message here:")
 
 if st.button("Check Message"):
@@ -44,6 +50,9 @@ if st.button("Check Message"):
         else:
             st.success("✅ This message is classified as **NOT SPAM (HAM)**.")
 
-# Footer
-st.markdown("---")  
+# Optional: Show dataset preview
+with st.expander("📄 View sample dataset (CSV)"):
+    st.dataframe(df.head())
+
+st.markdown("---")
 st.markdown("🔒 **Note**: This is a demo model and not intended for production use without proper testing.")