Update app.py

app.py

@@ -36,29 +36,19 @@ def predict(text):
 
 
 markdown_text = '''
+<h1>Structured Sentiment Analysis for Norwegian</h1>
+<p align="left">
+<br>
+This space provides a gradio demo of a <a href="https://huggingface.co/ltg/ssa-perin">pretrained model</a> for structured sentiment analysis (SSA) of Norwegian text, trained on the [NoReC_fine](https://github.com/ltgoslo/norec_fine) dataset by the <a href"https://www.mn.uio.no/ifi/english/research/groups/ltg/">Language Technology Group</a> at the University of Oslo. It implements a method described in the paper <a href="https://aclanthology.org/2022.acl-short.51/">Direct parsing to sentiment graphs</a> by Samuel et al. 2022.
 <br>
-This space provides a gradio demo of a <a href="https://huggingface.co/ltg/ssa-perin">pretrained model</a> (with an easy-to-run wrapper) for structured sentiment analysis (SSA) of Norwegian text, trained on the [NoReC_fine](https://github.com/ltgoslo/norec_fine) dataset. It implements a method described in the paper [Direct parsing to sentiment graphs](https://aclanthology.org/2022.acl-short.51/) by Samuel et al. 2022.
-
 The model will attempt to identify the following components for a given sentence it deems to be sentiment-bearing: _source expressions_ (the opinion holder), _target expressions_ (what the opinion is directed towards), _polar expressions_ (the part of the text indicating that an opinion is expressed), and finally the _polarity_ (positive or negative).
-
-See the code below for an example of how you can use the model yourself for predicting such sentiment tuples (along with character offsets in the text):
-
-```python
->>> import model_wrapper
->>> model = model_wrapper.PredictionModel()
->>> model.predict(['vi liker svart kaffe'])
-[{'sent_id': '0',
-  'text': 'vi liker svart kaffe',
-  'opinions': [{'Source': [['vi'], ['0:2']],
-    'Target': [['svart', 'kaffe'], ['9:14', '15:20']],
-    'Polar_expression': [['liker'], ['3:8']],
-    'Polarity': 'Positive'}]}]
-```
-
-To download the model and find more in-depth documentation, please see https://huggingface.co/ltg/ssa-perin
+<br>
+To download the model and find more in-depth documentation, please see <a href="https://huggingface.co/ltg/ssa-perin">https://huggingface.co/ltg/ssa-perin</a>
+</p>
 '''
 
 with gr.Blocks() as demo:
+    gr.Markdown(markdown_text)
     with gr.Row() as row:
         text_input = gr.Textbox(label="input")
         text_output = gr.Textbox(label="output")
@@ -67,7 +57,5 @@ with gr.Blocks() as demo:
 
     text_button.click(fn=predict, inputs=text_input, outputs=text_output)
 
-    gr.Markdown(markdown_text)
-
 
 demo.launch()