App updated

app.py

@@ -1,19 +1,17 @@
 import streamlit as st
-import transformers
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-# Load the model and tokenizer
-model = transformers.AutoModelForSequenceClassification.from_pretrained("MrDdz/mytuned_test_trainer-base-cased1")
-tokenizer = transformers.AutoTokenizer.from_pretrained("xlnet-base-cased")
 
 # Define the function for sentiment analysis
 @st.cache_resource
 def predict_sentiment(text):
-    # Load the pipeline.
-    pipeline = transformers.pipeline("sentiment-analysis")
+   # Load the model and tokenizer
+    model = transformers.AutoModelForSequenceClassification.from_pretrained("MrDdz/mytuned_test_trainer-base-cased1")
+    tokenizer = transformers.AutoTokenizer.from_pretrained("xlnet-base-cased")
 
     # Predict the sentiment.
-    prediction = pipeline(text)
+    prediction = (text)
     sentiment = prediction[0]["label"]
     score = prediction[0]["score"]