Update Home.py

Home.py

@@ -18,7 +18,7 @@ After that, a web interface would be built to demonstrate the interpretations, h
 Due to the limitation in time and resources, the project only used the tiny-sized ConvNeXt model, which was trained on ImageNet-1k at resolution 224x224 and used 50,000 images in validation set of ImageNet-1k for demo purpose.
 
 In this web app, two visualization techniques were implemented and demonstrated, they are **Maximally activating patches** and **SmoothGrad**. 
-Besides, this web app also helps in investigate the effect on **adversarial attacks** on ConvNeXt interpretations. 
+Besides, this web app also helps investigate the effect of **adversarial attacks** on ConvNeXt interpretations. 
 Last but not least, there is a last webpage that stores 50,000 images in the **ImageNet-1k** validation set, facilitating the two web pages above in searching and referencing. 
 """
 st.write(intro_text)