Update app.py

app.py

@@ -4,11 +4,11 @@ import tensorflow as tf
 tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
 
 
-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
+# tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-xlm-roberta-base-sentiment")
+# model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-xlm-roberta-base-sentiment")
 
-nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+# # Load pre-trained model and tokenizer
+nlp = pipeline("text-classification", model="cardiffnlp/twitter-xlm-roberta-base-sentiment")
 
 # Define function to analyze sentiment
 def analyze_sentiment(text):
@@ -32,7 +32,7 @@ with col1:  # Main content area
     st.markdown("All feedback gathered from LDS, including both structured and unstructured data, will be incorporated into the model to enhance its domain specificity and maximize accuracy.")
 
     
-    st.title("Sentiment Analysis Prototype Tool by orYx Models")
+    st.title("Sentiment Analysis Prototype Tool by orYx Models (De)")
     user_input = st.text_area("Enter text to analyze:", height=200)
     
     submit_button = st.button("Analyze")