| import pandas as pd | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.pipeline import Pipeline | |
| from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer | |
| from sklearn.naive_bayes import MultinomialNB | |
| from sklearn.metrics import accuracy_score, classification_report | |
| def remove_tags(text): | |
| tags = ['\n', '\''] | |
| for tag in tags: | |
| text = text.replace(tag, '') | |
| return text | |
| def load_data(filepath): | |
| df = pd.read_csv(filepath) | |
| df['text'] = df['text'].apply(remove_tags) | |
| return df | |
| def split_data(df): | |
| y = df['generated'] | |
| x = df['text'] | |
| x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42) | |
| return x_train, x_test, y_train, y_test | |
| def create_pipeline(x_train, y_train): | |
| pipeline = Pipeline([ | |
| ('count_vectorizer', CountVectorizer()), | |
| ('tfidf_transformer', TfidfTransformer()), | |
| ('classifier', MultinomialNB()) | |
| ]) | |
| pipeline.fit(x_train, y_train) | |
| return pipeline | |
| def predict_text(text, pipeline): | |
| processed_text = remove_tags(text) | |
| prediction = pipeline.predict([processed_text])[0] | |
| return "AI-generated" if prediction else "Human-written" | |