Delete app.py
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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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import pandas as pd
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models = []
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models.append("MoritzLaurer/mDeBERTa-v3-base-mnli-xnli") ### 0
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models.append("MoritzLaurer/multilingual-MiniLMv2-L6-mnli-xnli") ### 1 Fast
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models.append("MoritzLaurer/deberta-v3-large-zeroshot-v2.0") ### 2
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models.append("facebook/bart-large-mnli") ### 3
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models.append("MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7") ### 4
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model = models[1]
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#classifier = pipeline("zero-shot-classification", model=model)
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def coding(model, text, codetext):
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classifier = pipeline("zero-shot-classification", model=model)
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codelist = codetext.split(',')
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output = classifier(text, codelist, multi_label=True)
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return output
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def read_excel_file(file):
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# Read the Excel file
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data = pd.read_excel(file.name, sheet_name="codelist")
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return data
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iface = gr.Interface(
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fn=coding,
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inputs=[
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# gr.HTML(
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# "<a style='font-size:120%' href='https://huggingface.co/models?pipeline_tag=zero-shot-classification&language=zh&sort=downloads'>https://huggingface.co/models</a>"
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# ),
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gr.Radio(
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[
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"facebook/bart-large-mnli",
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"MoritzLaurer/multilingual-MiniLMv2-L6-mnli-xnli",
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"MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7",
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"MoritzLaurer/mDeBERTa-v3-base-mnli-xnli",
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"MoritzLaurer/deberta-v3-large-zeroshot-v2.0",
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#"joeddav/xlm-roberta-large-xnli"
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],
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#min_width=200,
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#scale=2,
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value="facebook/bart-large-mnli",
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label="Model"
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),
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gr.TextArea(
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label='Comment',
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value='感覺性格溫和,適合香港人,特別係亞洲人的肌膚,不足之處就是感覺很少有優惠,價錢都比較貴'
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),
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gr.Textbox(
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label='Code list',
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value='非常好/很好/好滿意,價錢合理/實惠/不太貴/親民/價格適中/價格便宜/價錢大眾化,價錢貴/不合理/比日本台灣貴/可以再平d/Sasa 卓悅買會平啲'
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)
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],
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outputs=[
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#gr.Textbox(label='Result')
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gr.JSON()
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#gr.BarPlot()
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],
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title="NuanceTree Coding Test",
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description="Test Zero-Shot Classification",
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allow_flagging='never'
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)
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# #iface.clear
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#gr.close_all()
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#iface.launch(server_name="0.0.0.0", server_port=7777)
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iface.launch()
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