model.py

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression

data = pd.read_csv('dataset.csv')
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(data['text'])
y = data['label']

classifier = LogisticRegression()
classifier.fit(X, y)

def predict(text):
    text_vectorized = vectorizer.transform([text])
    prediction = classifier.predict(text_vectorized)[0]
    if prediction == 'AI':
        score = classifier.predict_proba(text_vectorized)[0][0]
    else:
        score = 1 - classifier.predict_proba(text_vectorized)[0][1]
    response = [
        {
            'label': prediction,
            'score': round(float(score), 4)
        }
    ]
    return response