Deploy Naive Bayes spam classifier

app.py

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+import gradio as gr
+from joblib import load
+import string
+import nltk
+from nltk.corpus import stopwords
+
+nltk.download('stopwords')
+
+model = load("spam_classifier.joblib")
+vectorizer = load("tfidf_vectorizer.joblib")
+
+def process(text):
+    nopunc = ''.join([char for char in text if char not in string.punctuation])
+    clean = [word for word in nopunc.split() if word.lower() not in stopwords.words('english')]
+    return ' '.join(clean)
+
+def predict_email(text):
+    clean_text = process(text)
+    vect_text = vectorizer.transform([clean_text])
+    pred = model.predict(vect_text)[0]
+    return "Spam" if pred == 1 else "Not Spam"
+
+iface = gr.Interface(fn=predict_email,
+                     inputs=gr.Textbox(lines=5, placeholder='Enter email text here...'),
+                     outputs='text',
+                     title='Email Spam Classifier',
+                     description='Predict Spam or Not Spam')
+
+if __name__ == '__main__':
+    iface.launch()

requirements.txt

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+gradio>=3.0
+scikit-learn>=1.2
+joblib>=1.3
+nltk>=3.8
+numpy>=1.27
+pandas>=2.1

spam_classifier.joblib

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+oid sha256:ab8d15ad10dc0b902433c14f4bc901da8af5545bc78e4b66faf3b226f0dc66ef
+size 1190775

tfidf_vectorizer.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a2f0b2306c207777b5bc9b4d59044321d093cda9c9f49c045c93daca4a21b9ef
+size 781557