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import gradio as gr
import model_wrapper
model = model_wrapper.PredictionModel()
def pretty_print_opinion(opinion_dict):
res = []
maxlen = max([len(key) for key in opinion_dict.keys()]) + 2
maxlen = 0
for key, value in opinion_dict.items():
if key == 'Polarity':
res.append(f'{(key + ":").ljust(maxlen)} {value}')
else:
res.append(f'{(key + ":").ljust(maxlen)} \'{" ".join(value[0])}\'')
return '\n'.join(res) + '\n'
def predict(text):
print(f'Input message "{text}"')
try:
predictions = model.predict([text])
prediction = predictions[0]
results = []
if not prediction['opinions']:
return 'No opinions detected'
for opinion in prediction['opinions']:
results.append(pretty_print_opinion(opinion))
print(f'Successfully predicted SA for input message "{text}": {results}')
return '\n'.join(results)
except Exception as e:
print(f'Error for input message "{text}": {e}')
raise e
markdown_text = '''
<h1>Structured Sentiment Analysis for Norwegian</h1>
<p align="left">
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 <a href="https://github.com/ltgoslo/norec_fine">NoReC_fine</a> 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>
For a given sentence, the model will attempt to identify the following components if it is found to be sentiment-bearing:
<ul>
<li> <i>source expressions</i> (the opinion holder), </li>
<li> <i>target expressions</i> (what the opinion is directed towards), </li>
<li> <i>polar expressions</i> (the part of the text indicating that an opinion is expressed), </li>
<li> and finally the <i>polarity</i> (positive or negative). </li>
</ul>
<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")
with gr.Row() as row:
text_button = gr.Button("submit")
text_button.click(fn=predict, inputs=text_input, outputs=text_output)
demo.launch()
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