train_spam_model.py

import pandas as pd
import mlflow
import mlflow.sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Load dữ liệu
df = pd.read_csv("https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset/spam.csv", encoding='latin-1')[['v1', 'v2']]
df.columns = ['label', 'text']
df['label'] = df['label'].map({'ham': 0, 'spam': 1})

X_train, X_test, y_train, y_test = train_test_split(df['text'], df['label'], test_size=0.2, random_state=42)

# Pipeline gồm TF-IDF + Naive Bayes
pipeline = Pipeline([
    ('tfidf', TfidfVectorizer()),
    ('clf', MultinomialNB(alpha=1.0))  # bạn có thể thay đổi alpha để tạo version mới
])

pipeline.fit(X_train, y_train)
y_pred = pipeline.predict(X_test)
acc = accuracy_score(y_test, y_pred)

with mlflow.start_run():
    mlflow.log_param("alpha", 1.0)
    mlflow.log_metric("accuracy", acc)
    mlflow.sklearn.log_model(pipeline, "model", registered_model_name="SpamClassifier")
    print(f"Logged model with acc={acc}")