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# -*- coding: utf-8 -*- | |
""" | |
Created on Mon May 8 23:57:50 2023 | |
@author: ME | |
""" | |
from sklearn.feature_extraction.text import TfidfVectorizer | |
from src.preprocessor import Preprocessing | |
import joblib | |
# Load the saved TF-IDF preprocessor using joblib | |
path = "Artifacts/tfidf_preprocessor.pkl" | |
class Prediction: | |
def __init__(self,pred_data,model): | |
self.pred_data = pred_data | |
self.model = model | |
def predict(self): | |
preprocess_data = Preprocessing(self.pred_data).preprocess_text() | |
loaded_tfidf = joblib.load(path) | |
data = loaded_tfidf.transform(preprocess_data) | |
predicted = self.model.predict(data) | |
proba = self.model.predict_proba(data) | |
if predicted[0] == 0: | |
return "The news is fake",proba | |
else: | |
return "The news is real",proba | |