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Parent(s):
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Update app.py
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app.py
CHANGED
@@ -115,12 +115,28 @@ def gr_predict(text):
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return out_str
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# Gradio Interface
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iface = gr.Interface(
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fn=gr_predict,
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inputs=gr.Textbox(),
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outputs=gr.Label() # Use Label widget for output
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)
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# Launch the Gradio app
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iface.launch(debug=True)
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return out_str
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descr = """
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This app uses [Covid-twitter-BERT-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2)
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fine tuned on a custom subset of [Caves dataset](https://arxiv.org/abs/2204.13746) sent by [FIRE 2023](http://fire.irsi.res.in/fire/2023/home)
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conference to do multi-label classification of tweets expressing concerns towards vaccines. The different concerns/classes are
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('side-effect', 'ineffective', 'rushed', 'pharma', 'mandatory', 'unnecessary', 'political', 'ingredients', 'conspiracy', 'country', 'religious').
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Each tweet can be expressing multiple of these concerns. If a tweet is not expressing any concern falling into any of these categories
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it will be classified as 'None'.\n
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[Source files](https://github.com/Ranjit246/AISoME_FIRE_2023)\n
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Try it out with some ridiculous statements about vaccines. You can use the examples below as a start.
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"""
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# Gradio Interface
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iface = gr.Interface(
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fn=gr_predict,
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inputs=gr.Textbox(),
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outputs=gr.Label(), # Use Label widget for output
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examples=["This vaccine gave me mumps", "Chinese vaccine will infect our brain",
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"Trump is gonna use these vaccines to control us and become the president"],
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title="Vaccine Concerns ML",
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description=descr
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)
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# Launch the Gradio app
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iface.launch(debug=True)
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