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import gradio as gr
from interfaces.cap import demo as cap_demo
from interfaces.manifesto import demo as manifesto_demo
from interfaces.sentiment import demo as sentiment_demo
from interfaces.emotion import demo as emotion_demo
from interfaces.ner import demo as ner_demo
from interfaces.ner import download_models as download_spacy_models
from spacy import glossary
entities = ["CARDINAL", "DATE", "EVENT", "FAC", "GPE", "LANGUAGE", "LAW", "LOC", "MONEY", "NORP", "ORDINAL", "ORG", "PERCENT", "PERSON", "PRODUCT", "QUANTITY", "TIME", "WORK_OF_ART"]
ent_dict = glossary.GLOSSARY
ent_sum = [f'{ent} = <i>{ent_dict[ent]}</i>' for ent in entities ]
with gr.Blocks() as demo:
gr.Markdown(
f"""
<style>
.list {{
display: grid;
grid-template-columns: 1fr 1fr 1fr;
}}
</style>
<div style="display: block; text-align: left; padding:0; margin:0;">
<h1 style="text-align: center">Babel Machine Demo</h1>
<p>This is a demo for text classification using language models finetuned on data labeled by <a href="https://www.comparativeagendas.net/">CAP</a>, <a href="https://manifesto-project.wzb.eu/">Manifesto Project</a>, sentiment, and emotion coding systems.<br>
For the coding of complete datasets, please visit the official <a href="https://babel.poltextlab.com/">Babel Machine</a> site.<br>
Please note that named entity inputs are case sensitive.<br>
For named entity recognition the following labels are used:</p>
<ul class="list">
<li class="list-item"> {'</li class="list-item"> <li>'.join(ent_sum)} </li>
</ul>
</div>
""")
gr.TabbedInterface(
interface_list=[cap_demo, manifesto_demo, sentiment_demo, emotion_demo, ner_demo],
tab_names=["CAP", "Manifesto", "Sentiment (3)", "Emotions (8)", "Named Entity Recognition"],
)
if __name__ == "__main__":
download_spacy_models()
demo.launch()
# TODO: add all languages & domains |