added spinner for inference

app.py

@@ -2,12 +2,12 @@ import os
 import pandas as pd
 import numpy as np
 import torch
-from PIL import Image
 from transformers import SegformerForSemanticSegmentation, SegformerFeatureExtractor
 from torch import nn
 import streamlit as st
 
 
+st.title('Semantic Segmentation using SegFormer')
 raw_image = st.file_uploader('Raw Input Image')
 if raw_image is not None:
 	df = pd.read_csv('class_dict_seg.csv')
@@ -30,7 +30,9 @@ if raw_image is not None:
 	feature_extractor_inference = SegformerFeatureExtractor(do_random_crop=False, do_pad=False)
 	pixel_values = feature_extractor_inference(image, return_tensors="pt").pixel_values.to(device)
 	model.eval()
-	outputs = model(pixel_values=pixel_values)# logits are of shape (batch_size, num_labels, height/4, width/4)
+
+	with st.spinner('Running inference...'):
+		outputs = model(pixel_values=pixel_values)# logits are of shape (batch_size, num_labels, height/4, width/4)
 	logits = outputs.logits.cpu()
 	# First, rescale logits to original image size
 	upsampled_logits = nn.functional.interpolate(logits,