35 lines
1.3 KiB
Python
35 lines
1.3 KiB
Python
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import gradio as gr
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from transformers import pipeline
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sentimentPipeline = pipeline("zero-shot-classification", model="valhalla/distilbart-mnli-12-1")
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def sentiment_analysis(text, labels):
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candidate_labels = labels.split(',')
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results = sentimentPipeline(text, candidate_labels)
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total_results = ""
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index = 0
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for candidate_label in candidate_labels:
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total_results += f"Sentiment: {results.get('labels')[index]}, Score: {results.get('scores')[index]}"
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total_results += '\r\n'
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index += 1
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return total_results
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demo = gr.Interface(fn=sentiment_analysis,
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inputs=[
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gr.components.Textbox(label="Text"),
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gr.components.Textbox(label="Label")
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],
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outputs='text',
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examples=[['I have a problem with my iphone that needs to be resolved asap!!', 'urgent, not urgent, phone, tablet, computer'], ['Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.', 'mobile, website, billing, account access']],
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title = "文本情感分析"
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)
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if __name__ == "__main__":
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demo.queue(concurrency_count=3)
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demo.launch(server_name = "0.0.0.0", server_port = 7028)
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