Update app.py

app.py

@@ -2,20 +2,20 @@ import re
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
-
-# Load the model and tokenizer
-model_name = "csebuetnlp/mT5_m2m_crossSum"
-tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-
-get_lang_id = lambda lang: tokenizer._convert_token_to_id(
-    model.config.task_specific_params["langid_map"][lang][1]
-)
 
 # Function for cross-lingual summarization
 def cross_lingual_summarization(article_text, target_language):
     target_language = ""
+    WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
+
+    # Load the model and tokenizer
+    model_name = "csebuetnlp/mT5_m2m_crossSum"
+    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+    get_lang_id = lambda lang: tokenizer._convert_token_to_id(
+        model.config.task_specific_params["langid_map"][lang][1]
+    )
     input_ids = tokenizer(
     [WHITESPACE_HANDLER(article_text)],
     return_tensors="pt",