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Update app.py
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app.py
CHANGED
@@ -55,7 +55,6 @@ def upload_file(file):
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elif file.name.endswith('.csv') or file.name.endswith('.data'):
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df = pd.read_csv(file.name, header=None)
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df.columns = np.arange(len(df.columns))
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print(df)
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return df
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@@ -76,14 +75,15 @@ 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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inp_table = gr.DataFrame(type='numpy', value=example, headers=[''] * 3)
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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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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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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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elif file.name.endswith('.csv') or file.name.endswith('.data'):
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df = pd.read_csv(file.name, header=None)
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df.columns = np.arange(len(df.columns))
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return df
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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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