import pickle
import os
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression

MODEL_PATH = "models/logistic_regression.pkl"
VECTORIZER_PATH = "models/vectorizer.pkl"

def load_model():
    """Load trained model and vectorizer from disk."""
    if os.path.exists(MODEL_PATH) and os.path.exists(VECTORIZER_PATH):
        with open(MODEL_PATH, "rb") as model_file, open(VECTORIZER_PATH, "rb") as vec_file:
            model = pickle.load(model_file)
            vectorizer = pickle.load(vec_file)
        return model, vectorizer
    else:
        raise FileNotFoundError("Model or vectorizer not found!")

def predict(text, model, vectorizer):
    """Make predictions using the trained model."""
    text_vectorized = vectorizer.transform([text])
    prediction = model.predict(text_vectorized)[0]
    return prediction