Upload 5 files

app.py

@@ -0,0 +1,64 @@
+import streamlit as st
+import pickle
+import string
+import nltk
+from nltk.corpus import stopwords
+from nltk.stem.porter import PorterStemmer
+
+# 🧠 Download required NLTK resources only once
+try:
+    nltk.data.find('tokenizers/punkt')
+except LookupError:
+    nltk.download('punkt')
+
+try:
+    nltk.data.find('corpora/stopwords')
+except LookupError:
+    nltk.download('stopwords')
+
+# 🔤 Initialize stemmer
+ps = PorterStemmer()
+
+# 🔍 Preprocessing function
+def transform_text(text):
+    text = text.lower()
+    text = nltk.word_tokenize(text)
+
+    y = []
+    for word in text:
+        if word.isalnum():
+            y.append(word)
+
+    text = y[:]
+    y.clear()
+
+    for word in text:
+        if word not in stopwords.words('english') and word not in string.punctuation:
+            y.append(ps.stem(word))
+
+    return " ".join(y)
+
+# 📦 Load model and vectorizer
+tfidf = pickle.load(open('vectorizer.pkl', 'rb'))
+model = pickle.load(open('model.pkl', 'rb'))
+
+# 💬 Streamlit UI
+st.title("📩 SMS Spam Classifier")
+
+input_sms = st.text_area("Enter the message")
+
+if st.button('Predict'):
+    # 1. Preprocess
+    transformed_sms = transform_text(input_sms)
+
+    # 2. Vectorize
+    vector_input = tfidf.transform([transformed_sms])
+
+    # 3. Predict
+    result = model.predict(vector_input)[0]
+
+    # 4. Show result
+    if result == 1:
+        st.error("🚫 Spam")
+    else:
+        st.success("✅ Not Spam")

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b9e10101ebb499ed8198528e137234642c377827d8ded234c7292dac890cf28
+size 147

nltk.txt

@@ -0,0 +1,2 @@
+
+punkt

requirements.txt

@@ -1,3 +1,3 @@
-altair
-pandas
-streamlit
+streamlit
+nltk
+sklearn

vectorizer.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:05d5bc6b5ccde6839aefc33be9b9974d59020956f930de583e236559d82f2322
+size 170452