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app.py

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+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.naive_bayes import MultinomialNB
+
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, ConfusionMatrixDisplay
+from sklearn.preprocessing import LabelEncoder
+from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, TfidfTransformer
+
+import joblib
+import gradio as gr 
+
+
+
+
+
+# Load the saved model and preprocessing objects
+model = joblib.load('models/spam_classifier_model.joblib')
+cv = joblib.load('models/count_vectorizer.joblib')
+tfidf = joblib.load('models/tfidf_transformer.joblib')
+le = joblib.load('models/label_encoder.joblib')
+
+def predict_spam(message):
+    X_new_counts = cv.transform([message])
+    X_new_tfidf = tfidf.transform(X_new_counts)
+    pred = model.predict(X_new_tfidf)[0]
+    label = le.inverse_transform([pred])[0]
+    return f"Prediction: {label}"
+
+# Create Gradio interface
+iface = gr.Interface(
+    title=gr.Markdown('# 📱💬 SMS Spam Classifier'),
+    theme=gr.themes.Soft(),
+    fn=predict_spam,
+    inputs=gr.Textbox(lines=10, placeholder="Enter an SMS message..."),
+    outputs=gr.Textbox(lines=10),
+    description="# 📱💬 SMS Spam Classifier\n\nEnter an SMS message to classify it as 'spam' or 'ham' using the best model.",
+    examples=['Congratulations! You have won a $1,000 Walmart gift card. Go to http://bit.ly/123456 to claim your prize now. Reply STOP to opt out.',
+              'Hi, how are you?']
+)
+
+iface.launch()

requirements.txt

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+joblib
+scikit-learn
+gradio
+pandas