Update app.py

app.py

@@ -37,7 +37,7 @@ def predict(text):
 
 markdown_text = '''
 <br>
-This space provides a gradio demo of a [pretrained model](https://huggingface.co/ltg/ssa-perin) (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.
+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).
 
@@ -55,7 +55,7 @@ See the code below for an example of how you can use the model yourself for pred
     'Polarity': 'Positive'}]}]
 ```
 
-To download the model and find more in-depth documentation, please see (https://huggingface.co/ltg/ssa-perin)[https://huggingface.co/ltg/ssa-perin]
+To download the model and find more in-depth documentation, please see https://huggingface.co/ltg/ssa-perin
 '''
 
 with gr.Blocks() as demo: