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fc18efa
1
Parent(s):
4853fe0
Update app.py
Browse files
app.py
CHANGED
@@ -371,7 +371,7 @@ def topics(output_file, input_checks):
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with gr.Blocks() as demo:
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with gr.Row():
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with gr.Column(scale=1
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gr.Markdown("""
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""")
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with gr.Column(scale=6, min_width=600):
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@@ -382,11 +382,11 @@ with gr.Blocks() as demo:
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This demo was made to demonstrate the EmotioNL model, a transformer-based classification model that analyses emotions in Dutch texts. The model uses [RobBERT](https://github.com/iPieter/RobBERT), which was further fine-tuned on the [EmotioNL dataset](https://lt3.ugent.be/resources/emotionl/). The resulting model is a classifier that, given a sentence, predicts one of the following emotion categories: _anger_, _fear_, _joy_, _love_, _sadness_ or _neutral_. The demo can be used either in **sentence mode**, which allows you to enter a sentence for which an emotion will be predicted; or in **dataset mode**, which allows you to upload a dataset or see the full functuonality of with example data.
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""")
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with gr.Column(scale=1
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gr.Markdown("""
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""")
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with gr.Row():
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-
with gr.Column(scale=1
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gr.Markdown("""
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""")
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with gr.Column(scale=6, min_width=600):
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@@ -469,12 +469,12 @@ with gr.Blocks() as demo:
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next_button_topics.click(fn=topics, inputs=[output_file,input_checks], outputs=output_topics)
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send_btn.click(fn=unavailable, inputs=[input_file,input_checks], outputs=[output_markdown,message])
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demo_btn.click(fn=showcase, inputs=[input_file], outputs=[output_markdown,message,output_file,next_button_freq,next_button_dist,next_button_peaks,next_button_topics])
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with gr.Column(scale=1
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gr.Markdown("""
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""")
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with gr.Row():
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-
with gr.Column(scale=1
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gr.Markdown("""
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""")
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with gr.Column(scale=6, min_width=600):
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@@ -483,7 +483,7 @@ with gr.Blocks() as demo:
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<div style="display: flex"><img style="margin-right: 1em" alt="LT3 logo" src="https://lt3.ugent.be/static/images/logo_v2_single.png" width="136" height="58"> <img style="margin-right: 1em" alt="FWO logo" src="https://www.fwo.be/images/logo_desktop.png" height="58"></div>
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""")
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with gr.Column(scale=1
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gr.Markdown("""
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""")
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demo.launch()
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with gr.Blocks() as demo:
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with gr.Row():
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+
with gr.Column(scale=1):
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gr.Markdown("""
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""")
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with gr.Column(scale=6, min_width=600):
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This demo was made to demonstrate the EmotioNL model, a transformer-based classification model that analyses emotions in Dutch texts. The model uses [RobBERT](https://github.com/iPieter/RobBERT), which was further fine-tuned on the [EmotioNL dataset](https://lt3.ugent.be/resources/emotionl/). The resulting model is a classifier that, given a sentence, predicts one of the following emotion categories: _anger_, _fear_, _joy_, _love_, _sadness_ or _neutral_. The demo can be used either in **sentence mode**, which allows you to enter a sentence for which an emotion will be predicted; or in **dataset mode**, which allows you to upload a dataset or see the full functuonality of with example data.
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""")
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+
with gr.Column(scale=1):
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gr.Markdown("""
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""")
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with gr.Row():
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+
with gr.Column(scale=1):
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gr.Markdown("""
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""")
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with gr.Column(scale=6, min_width=600):
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next_button_topics.click(fn=topics, inputs=[output_file,input_checks], outputs=output_topics)
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send_btn.click(fn=unavailable, inputs=[input_file,input_checks], outputs=[output_markdown,message])
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demo_btn.click(fn=showcase, inputs=[input_file], outputs=[output_markdown,message,output_file,next_button_freq,next_button_dist,next_button_peaks,next_button_topics])
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with gr.Column(scale=1):
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gr.Markdown("""
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""")
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with gr.Row():
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with gr.Column(scale=1):
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gr.Markdown("""
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""")
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with gr.Column(scale=6, min_width=600):
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<div style="display: flex"><img style="margin-right: 1em" alt="LT3 logo" src="https://lt3.ugent.be/static/images/logo_v2_single.png" width="136" height="58"> <img style="margin-right: 1em" alt="FWO logo" src="https://www.fwo.be/images/logo_desktop.png" height="58"></div>
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""")
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with gr.Column(scale=1):
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gr.Markdown("""
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""")
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demo.launch()
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