44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
import gradio as gr
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from transformers import pipeline, AutoTokenizer, AutoConfig, AutoModelForSequenceClassification
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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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modelName="finiteautomata/bertweet-base-sentiment-analysis"
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sentimentPipeline = pipeline("sentiment-analysis", model=modelName)
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def sentiment_analysis(text):
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results = sentimentPipeline(text)
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return f"Sentiment: {results[0].get('label')}, Score: {results[0].get('score'):.2f}"
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with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo:
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gr.Markdown("""
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<div align='center' ><font size='60'>文本情感分析</font></div>
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""")
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with gr.Row():
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with gr.Column():
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box1 = gr.Textbox(label="文本")
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with gr.Row():
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button = gr.Button("提交", variant="primary")
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clear = gr.Button("清除", variant="primary")
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box2 = gr.Textbox(label="文本")
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button.click(fn=sentiment_analysis, inputs=box1, outputs=box2)
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clear.click(lambda x: gr.update(value=''), [], [box1])
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examples = gr.Examples(examples=[['I am happy.'], ['I am sad].'], ['This is a cat.']], inputs=[box1], label="例子")
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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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