Updated layout :)

app.py

@@ -13,8 +13,8 @@ except Exception:
     os.system("python -m spacy download pt_core_news_sm")
     nlp = spacy.load("pt_core_news_sm")
 
-model = AutoModelForTokenClassification.from_pretrained("Emanuel/bertimbau-base-pos")
-tokenizer = AutoTokenizer.from_pretrained("Emanuel/bertimbau-base-pos")
+model = AutoModelForTokenClassification.from_pretrained("Emanuel/porttagger-news-base")
+tokenizer = AutoTokenizer.from_pretrained("Emanuel/porttagger-news-base")
 logger = logging.getLogger()
 logger.setLevel(logging.DEBUG)
 
@@ -64,7 +64,13 @@ def text_analysis(text):
     for token, label in zip(tokens, labels):
         pos_tokens.extend([(token, label), (" ", None)])
 
-    return pos_tokens, pos_count
+    output_highlighted.update(visible=True)
+    output_df.update(visible=True)
+
+    return {
+        output_highlighted: output_highlighted.update(visible=True, value=(pos_tokens)),
+        output_df: output_df.update(visible=True, value=pos_count),
+    }
 
 
 css = open("style.css").read()
@@ -73,13 +79,7 @@ bottom_html = open("bottom.html").read()
 
 with gr.Blocks(css=css) as demo:
     gr.HTML(top_html)
-    text = gr.Textbox(placeholder="Insira um texto...", label="Texto de entrada")
-    output_highlighted = gr.HighlightedText()
-    output_df = gr.Dataframe()
-    submit_btn = gr.Button("Enviar")
-    submit_btn.click(
-        fn=text_analysis, inputs=text, outputs=[output_highlighted, output_df]
-    )
+    text = gr.Textbox(placeholder="Enter your text here...", label="Input")
     examples = gr.Examples(
         examples=[
             [
@@ -88,9 +88,15 @@ with gr.Blocks(css=css) as demo:
             ["Filme 'Star Wars : Os Últimos Jedi' ganha trailer definitivo; assista."],
         ],
         inputs=[text],
-        label="Exemplos",
+        label="Select an example",
+    )
+    output_highlighted = gr.HighlightedText(label="Colorful output", visible=False)
+    output_df = gr.Dataframe(label="Tabular output", visible=False)
+    submit_btn = gr.Button("Send")
+    submit_btn.click(
+        fn=text_analysis, inputs=text, outputs=[output_highlighted, output_df]
     )
     gr.HTML(bottom_html)
 
 
-demo.launch()
+demo.launch(debug=True)

bottom.html

@@ -8,4 +8,7 @@
         <img class="column" alt="ICMC-USP logo"
             src="https://upload.wikimedia.org/wikipedia/commons/thumb/c/c9/Webysther_20170627_-_Logo_ICMC-USP.svg/2560px-Webysther_20170627_-_Logo_ICMC-USP.svg.png" />
     </div>
+</div>
+<div style="text-align: center;">
+    <p>This app was made with ☕ by <a href="https://github.com/huberemanuel">Emanuel Huber</a> 🤗</p>
 </div>

style.css

@@ -65,5 +65,6 @@ a {
 
 .column {
     flex: 33.33%;
-    padding: 5px;
+    padding-left: 40px;
+    padding-right: 40px;
 }

top.html

@@ -1,7 +1,7 @@
 <div style="text-align: center; max-width: 650px; margin: 0 auto;">
     <div>
         <h1 style="font-weight: 900; font-size: 3rem; margin: 20px;">
-            PorPos tagger
+            PorttTagger tagger
         </h1>
         <p class="slogan">A Brazilian Portuguese part-of-speech tagger according to Universal
             Dependencies</p>
@@ -11,7 +11,7 @@
             href="https://sites.google.com/icmc.usp.br/poetisa/resources-and-tools">Porttinari-base</a> corpus which is
         a collection of news extracted from the Folha de São Paulo newspaper site. The trained model is a fine-tuned
         version
-        of <a src="https://huggingface.co/neuralmind/bert-base-portuguese-cased">Bertimbau</a> that receives tokens and
+        of <a href="https://huggingface.co/neuralmind/bert-base-portuguese-cased">Bertimbau</a> that receives tokens and
         outputs part-of-speech tags. Since the model expects a sequence of
         tokens
         for its inputs, <a src="https://spacy.io/models/pt">Spacy's</a> tokenization is used to tokenize the input text.