app.py

import gradio as gr
import nltk
from nltk.corpus import stopwords
import joblib

# Load the trained model and vectorizer outside the function for better performance
loaded_classifier = joblib.load("is_this_bible_model.pkl")
vectorizer = joblib.load("is_this_bible_vectorizer.pkl")

def parse_text(new_text):
    new_text_tfidf = vectorizer.transform([new_text])
    prediction = loaded_classifier.predict(new_text_tfidf)
    probabilities = loaded_classifier.predict_proba(new_text_tfidf)
    confidence_score = probabilities[0, 1]
    return 'תנ"ך' if prediction[0] == 1 else 'אחר', confidence_score

iface = gr.Interface(fn=parse_text, inputs="text", outputs=["text", "number"], title="Bible Text Classifier", description='הזן טקסט כדי לסווג אם הוא מהתנ"ך או לא.')
iface.launch()