Update app.py
Browse files
app.py
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@@ -72,7 +72,9 @@ def infer(seed):
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data = data.round(decimals = 2)
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return f"{round(pred.flatten()[0]*100,
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# get the inputs
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inputs = [gr.Slider(minimum=0, maximum=3000, step=1, label='Choose a random number', value=5)]
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@@ -87,7 +89,7 @@ output = [
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title = 'Imbalanced Classification with Tensorflow'
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description = 'Imbalanced
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article = "Author: <a href=\"https://huggingface.co/geninhu\">Nhu Hoang</a>. Based on this <a href=\"https://keras.io/examples/structured_data/imbalanced_classification/\">keras example</a> by <a href=\"https://twitter.com/fchollet\">fchollet.</a> HuggingFace Model <a href=\"https://huggingface.co/keras-io/imbalanced_classification\">here</a> "
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data = data.round(decimals = 2)
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#return f"{round(pred.flatten()[0]*100, 5)}%", data.values.tolist()
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result = 'This is fraudulent transaction!' if pred.flatten()[0] > 0.5 else "This is real transaction!"
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return result, data.values.tolist()
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# get the inputs
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inputs = [gr.Slider(minimum=0, maximum=3000, step=1, label='Choose a random number', value=5)]
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]
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title = 'Imbalanced Classification with Tensorflow'
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description = 'Imbalanced Classification in predicting Credit card Fraud.'
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article = "Author: <a href=\"https://huggingface.co/geninhu\">Nhu Hoang</a>. Based on this <a href=\"https://keras.io/examples/structured_data/imbalanced_classification/\">keras example</a> by <a href=\"https://twitter.com/fchollet\">fchollet.</a> HuggingFace Model <a href=\"https://huggingface.co/keras-io/imbalanced_classification\">here</a> "
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