Update app.py

app.py

@@ -128,9 +128,9 @@ article = """
 
 # British Library Books genre detection demo
 
-This demo alows you to play with a 'genre' detection model which has been trained to predict, from the title of a book, whether it is 'fiction' or 'non-fiction'.
-The model was trained with the [fastai](https://docs.fast.ai/) library on training data drawn from ditised books at the British Library. These Books are mainly from the 19th Century. 
-The demo also shows you which parts of the input the model is using most to make its prediction. You can hover over the words to see the attenton score assigned to that word. This gives you some sense of which words are important to the model in making a prediction.
+This demo allows you to play with a 'genre' detection model which has been trained to predict, from the title of a book, whether it is 'fiction' or 'non-fiction'.
+The model was trained with the [fastai](https://docs.fast.ai/) library on training data drawn from digitised books at the British Library. These Books are mainly from the 19th Century. 
+The demo also shows you which parts of the input the model is using most to make its prediction. You can hover over the words to see the attention score assigned to that word. This gives you some sense of which words are important to the model in making a prediction.
 
 The examples include titles from the BL books collection. 
 
@@ -146,12 +146,11 @@ You can find more information about the model [here]((https://doi.org/10.5281/ze
 
 ## Training data
 
-The model is trained on a particular collection of books digitised by the British Library. As a result the model may do less well on titles that look different to this data.
-In particular the training data, was mostly English, and mostly from the 19th Century. 
+The model is trained on a particular collection of books digitised by the British Library. As a result, the model may do less well on titles that look different to this data. In particular, the training data, was mostly English, and mostly from the 19th Century. 
 
 ## Model performance
 
-The models performance on a held-out test set is as follows:
+The model's performance on a held-out test set is as follows:
 
 
 ```
@@ -168,7 +167,6 @@ weighted avg       0.93      0.93      0.93       850
 ### Credits
 > This work was partly supported by [Living with Machines](https://livingwithmachines.ac.uk/). This project, funded by the UK Research and Innovation (UKRI) Strategic Priority Fund, is a multidisciplinary collaboration delivered by the Arts and Humanities Research Council (AHRC), with The Alan Turing Institute, the British Library and the Universities of Cambridge, East Anglia, Exeter, and Queen Mary University of London. 
 > Code for showing attention was adapted from Zach Mueller's (@TheZachMueller) [fastinference](https://muellerzr.github.io/fastinference/) library. 
-
 """
 
 gr_interface = gr.Interface(