Update app.py

app.py

@@ -6,6 +6,9 @@ from io import BytesIO
 from PIL import Image
 from transformers import pipeline
 
+pipe1 = pipeline("text-classification", model="papluca/xlm-roberta-base-language-detection")
+pipe2 = pipeline("text-classification", model="shahrukhx01/question-vs-statement-classifier")
+
 # Define maximum dimensions for the fortune image (in pixels)
 MAX_SIZE = (400, 400)
 
@@ -34,16 +37,16 @@ if "stick_clicked" not in st.session_state:
 
 # Update the check functions to use the "question" parameter properly.
 def check_sentence_is_english_model(question):
-    pipe = pipeline("text-classification", model="papluca/xlm-roberta-base-language-detection")
+    globals pipe1
     # If the model predicts the label "en", we consider it English.
-    if pipe(question)[0]['label'] == 'en':
+    if pipe1(question)[0]['label'] == 'en':
         return True
     return False
 
 def check_sentence_is_question_model(question):
-    pipe = pipeline("text-classification", model="shahrukhx01/question-vs-statement-classifier")
+    globals pipe1
     # If the model predicts "LABEL_1", we consider it a question.
-    if pipe(question)[0]['label'] == 'LABEL_1':
+    if pipe2(question)[0]['label'] == 'LABEL_1':
         return True
     return False