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Update app.py
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app.py
CHANGED
@@ -75,22 +75,23 @@ The TabPFN will classify the values for all empty cells in the label column.
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Please, provide everything but the label column as numeric values.
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You can also upload datasets to fill the table automatically.
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""")
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examples = gr.Examples(examples=['iris.csv', 'balance-scale.arff'],
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inputs=[inp_file],
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outputs=[inp_table],
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fn=upload_file,
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cache_examples=True)
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inp_table = gr.DataFrame(type='numpy', value=example, headers=[''] * 3)
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upload_file('iris.csv')
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inp_file = gr.File(
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label='Drop either a .csv (without header, only numeric values for all but the labels) or a .arff file.')
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btn = gr.Button("Predict Empty Table Cells")
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inp_file.change(fn=upload_file, inputs=inp_file, outputs=inp_table)
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out_text = gr.Markdown()
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out_table = gr.DataFrame()
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btn.click(fn=compute, inputs=inp_table, outputs=[out_text, out_table])
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demo.launch()
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Please, provide everything but the label column as numeric values.
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You can also upload datasets to fill the table automatically.
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""")
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inp_table = gr.DataFrame(type='numpy', value=example, headers=[''] * 3)
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upload_file('iris.csv')
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btn = gr.Button("Predict Empty Table Cells")
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btn.click(fn=compute, inputs=inp_table, outputs=[out_text, out_table])
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out_text = gr.Markdown()
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out_table = gr.DataFrame()
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examples = gr.Examples(examples=['iris.csv', 'balance-scale.arff'],
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inputs=[inp_file],
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outputs=[inp_table],
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fn=upload_file,
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cache_examples=True)
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inp_file = gr.File(
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label='Drop either a .csv (without header, only numeric values for all but the labels) or a .arff file.')
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inp_file.change(fn=upload_file, inputs=inp_file, outputs=inp_table)
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demo.launch()
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