Update app.py

app.py

@@ -143,20 +143,11 @@
 import streamlit as st
 import transformers
 import torch
-import tokenizers  # Import tokenizers explicitly
 
 # Load the model and tokenizer
 model = transformers.AutoModelForSequenceClassification.from_pretrained("DeeeTeeee01/mytest_trainer_roberta-base")
 tokenizer = transformers.AutoTokenizer.from_pretrained("DeeeTeeee01/mytest_trainer_roberta-base")
 
-# Define the custom hash function for tokenizers.Tokenizer (Unused in this version)
-def tokenizer_hash_func(tokenizer):
-    return tokenizer.__str__()
-
-# Define the custom hash function for tokenizers.AddedToken (Unused in this version)
-def added_token_hash_func(token):
-    return token.__str__()
-
 # Define the function for sentiment analysis without caching
 def predict_sentiment(text):
     # Load the pipeline
@@ -196,9 +187,9 @@ if predict_button and text:
     
     # Display individual percentages
     st.write("Sentiment Breakdown:")
-    st.write(f"- Negative: {score['LABEL_0']*100:.2f}%")
-    st.write(f"- Positive: {score['LABEL_1']*100:.2f}%")
-    st.write(f"- Neutral: {score['LABEL_2']*100:.2f}%")
+    st.write(f"- Negative: {score[0]*100:.2f}%")
+    st.write(f"- Positive: {score[1]*100:.2f}%")
+    st.write(f"- Neutral: {score[2]*100:.2f}%")
 
 # Define the CSS style for the app
 st.markdown(