Update app.py

app.py

@@ -1,14 +1,15 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
 import tensorflow as tf
 tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+from transformers import XLMRobertaTokenizer, AutoModelForSequenceClassification, pipeline
 
+# Load tokenizer and model
+tokenizer = XLMRobertaTokenizer.from_pretrained("cardiffnlp/twitter-xlm-roberta-base-sentiment")
+model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-xlm-roberta-base-sentiment")
 
-# tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-xlm-roberta-base-sentiment")
-# model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-xlm-roberta-base-sentiment")
-
-# # Load pre-trained model and tokenizer
-nlp = pipeline("text-classification", model="cardiffnlp/twitter-xlm-roberta-base-sentiment")
+# Define pipeline explicitly
+nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)
 
 # Define function to analyze sentiment
 def analyze_sentiment(text):