Update app.py

app.py

@@ -1,17 +1,15 @@
 import streamlit as st
-import transformers
-import torch
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-# Define the paths of the pre-trained models
-model1_path = "saisi/finetuned-Sentiment-classfication-ROBERTA-model"
-model2_path = "saisi/finetuned-Sentiment-classfication-DISTILBERT-model"
+# Define the model names or identifiers
+model1_name = "saisi/finetuned-Sentiment-classfication-ROBERTA-model"
+model2_name = "saisi/finetuned-Sentiment-classfication-DISTILBERT-model"
 
 # Initialize the tokenizer and models for sentiment analysis
-tokenizer1 = AutoTokenizer.from_pretrained(model1_path)
-model1 = AutoModelForSequenceClassification.from_pretrained(model1_path)
-tokenizer2 = AutoTokenizer.from_pretrained(model2_path)
-model2 = AutoModelForSequenceClassification.from_pretrained(model2_path)
+tokenizer1 = AutoTokenizer.from_pretrained(model1_name)
+model1 = AutoModelForSequenceClassification.from_pretrained(model1_name)
+tokenizer2 = AutoTokenizer.from_pretrained(model2_name)
+model2 = AutoModelForSequenceClassification.from_pretrained(model2_name)
 
 # Define a function to preprocess the text data
 def preprocess(text):