Update README.md

README.md

@@ -35,7 +35,6 @@ Below is an example of how to load and use the model for sentiment classificatio
 ```python
 from transformers import BertTokenizer, BertForSequenceClassification
 import torch
-import streamlit as st
 
 # Load the tokenizer and model
 tokenizer = BertTokenizer.from_pretrained(
@@ -50,7 +49,7 @@ outputs = model(**inputs)
 logits = outputs.logits
 sentiment = torch.argmax(logits, dim=1).item()
 
-print(f"Predicted sentiment: {'Positive' if sentiment == 1 else 'Negative'}")
+print(f"Predicted sentiment: {'Positive' if sentiment else 'Negative'}")