Update app.py

app.py

@@ -3,10 +3,13 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipe
 import tensorflow as tf
 tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
 
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
+model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 # Load pre-trained model and tokenizer
-saved_directory = "models/cardiffnlp/twitter-xlm-roberta-base-sentiment"
-tokenizer = AutoTokenizer.from_pretrained(saved_directory)
-model = AutoModelForSequenceClassification.from_pretrained(saved_directory)
+
 nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
 
 # Define function to analyze sentiment