services/train_model.py

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import pickle
import os

# Load the dataset
file_path = "data/sms_process_data_main.xlsx"
df = pd.read_excel(file_path)

# Prepare the features and labels
X = df['MessageText']  # SMS messages
y = df['label']        # Labels: 'Transaction' or 'Offer'

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize the TF-IDF Vectorizer
vectorizer = TfidfVectorizer(max_features=5000)

# Fit the vectorizer on the training data and transform the training data
X_train_vec = vectorizer.fit_transform(X_train)

# Initialize and train the logistic regression model
classifier = LogisticRegression()
classifier.fit(X_train_vec, y_train)

# Save the trained model and vectorizer
models_dir = "models"
if not os.path.exists(models_dir):
    os.makedirs(models_dir)

# Save the classifier model
with open(os.path.join(models_dir, 'sms_classifier_model.pkl'), 'wb') as model_file:
    pickle.dump(classifier, model_file)

# Save the vectorizer
with open(os.path.join(models_dir, 'tfidf_vectorizer.pkl'), 'wb') as vectorizer_file:
    pickle.dump(vectorizer, vectorizer_file)

print("Model and vectorizer saved successfully!")