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finished the predict function
Browse files- Pipfile +14 -0
- Pipfile.lock +0 -0
- app.py +44 -0
- requirements.txt +3 -0
Pipfile
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[[source]]
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url = "https://pypi.org/simple"
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verify_ssl = true
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name = "pypi"
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[packages]
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gradio = "*"
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transformers = "*"
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tensorflow = "*"
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[dev-packages]
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[requires]
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python_version = "3.8"
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Pipfile.lock
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app.py
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from transformers import pipeline
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import gradio as gr
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# loading the model
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model_checkpoint = 'zinoubm/e-comerce-category-classification'
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model = pipeline(
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"text-classification", model=model_checkpoint,
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)
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def predict(input):
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predictions = model(input)
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predictions = [prediction['label'] for prediction in predictions]
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return ' '.join(predictions)
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# defining demo content
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title = 'E-Commerce Category Prediction.'
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description = '''
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This is a classification model that predicts the category of an input.
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We have 4 Categories, Electronics, Household, Books and Clothing & Accessories.
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'''
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article = '''
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# How to use this interface
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Using the interface is straight forward, just type some text that falls in one of these 4 categories: **Electronics**, **Household**, **Books** or **Clothing & Accessories**.
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and then hit **Submit**. the results will be in the output cell. You can also try one of the provided examples.
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'''
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examples = [
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['I want to sell a laptop'],
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['This is a beatiful T-shirt'],
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['Save 50% on detergent powder']
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]
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# launching the interface
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gr.Interface(fn=predict,
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inputs="text",
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title=title,
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description=description,
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article=article,
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outputs="text",
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examples = examples,
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theme='default',
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).launch()
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requirements.txt
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gradio
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transformers
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tensorflow
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