Update app.py

app.py

@@ -1,11 +1,9 @@
-from flask import Flask, render_template, request
+import gradio as gr
 import nltk
 from nltk.corpus import stopwords
 import joblib
 
-app = Flask(__name__)
-
-# Load the trained model and vectorizer outside the routes for better performance
+# 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")
 
@@ -16,18 +14,5 @@ def parse_text(new_text):
     confidence_score = probabilities[0, 1]
     return 'תנ"ך' if prediction[0] == 1 else 'אחר', confidence_score
 
-@app.route('/', methods=['GET', 'POST'])
-def index():
-    prediction = None
-    confidence_score = None
-    new_text = None
-
-    if request.method == 'POST':
-        new_text = request.form['new_text']
-        if new_text:
-            prediction, confidence_score = parse_text(new_text)
-    return render_template('index.html', new_text=new_text, prediction=prediction, confidence_score=confidence_score)
-
-
-if __name__ == '__main__':
-    app.run(debug=True, host='0.0.0.0')
+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()