46 lines
1.5 KiB
Python
46 lines
1.5 KiB
Python
import gradio as gr
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from transformers import pipeline
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from gradio.themes.utils import sizes
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theme = gr.themes.Default(radius_size=sizes.radius_none).set(
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block_label_text_color = '#4D63FF',
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block_title_text_color = '#4D63FF',
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button_primary_text_color = '#4D63FF',
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button_primary_background_fill='#FFFFFF',
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button_primary_border_color='#4D63FF',
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button_primary_background_fill_hover='#EDEFFF',
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)
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sentimentPipeline = pipeline('zero-shot-classification', model='roberta-large-mnli')
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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 am happy', 'negative, netural, positive'], ['I am sad', 'negative, netural, positive']],
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theme = theme,
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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=10)
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demo.launch(server_name = "0.0.0.0")
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