Updating layout

app.py

@@ -22,14 +22,14 @@ dt_tokenizer = DanteTokenizer()
 default_model = "News"
 model_choices = {
     "News": "Emanuel/porttagger-news-base",
-    "Tweets": "Emanuel/porttagger-tweets-base",
-    "Oil and Gas": "Emanuel/porttagger-oilgas-base",
+    "Tweets (stock market)": "Emanuel/porttagger-tweets-base",
+    "Oil and Gas (academic texts)": "Emanuel/porttagger-oilgas-base",
     "Multigenre": "Emanuel/porttagger-base",
 }
 pre_tokenizers = {
     "News": nlp,
-    "Tweets": dt_tokenizer.tokenize,
-    "Oil and Gas": nlp,
+    "Tweets (stock market)": dt_tokenizer.tokenize,
+    "Oil and Gas (academic texts)": nlp,
     "Multigenre": nlp,
 }
 logger = logging.getLogger()
@@ -177,7 +177,8 @@ with gr.Blocks(css=css) as demo:
     with gr.Tab("Multiple sentences"):
         gr.HTML(
             """
-        <p>Upload a plain text file with sentences in it. 
+        <p align="justify"">
+        &emsp;Upload a plain text file with sentences in it. 
         Find below an example of what we expect the content of the file to look like. 
         Sentences are automatically split by spaCy's sentencizer. 
         To force an explicit segmentation, manually separate the sentences using a new line for each one.</p>

top.html

@@ -7,8 +7,8 @@
                 href="https://universaldependencies.org/">Universal Dependencies</a> model
         </p>
     </div>
-    <p style="margin-top: 30px; margin-bottom: 10px; font-size: 94%; text-align: left;">
-        Porttagger is a state of the art part of speech tagger for Brazilian Portuguese that automatically assigns
+    <p style="margin-top: 30px; margin-bottom: 10px; font-size: 94%; text-align: justify;">
+        &emsp;Porttagger is a state of the art part of speech tagger for Brazilian Portuguese that automatically assigns
         morphosyntactic classes to the words of sentences, following the Universal Dependencies international model. You
         may provide single sentences or multiple sentences (using plain text files with several sentences) to be tagged.
         You may also choose which trained model to use. The options include a model trained on news texts (using the