48 lines
1.6 KiB
Python
48 lines
1.6 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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classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
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def sentiment_analysis(text):
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results = classifier(text)
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total_result = ""
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for result in results[0]:
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total_result += f"Sentiment: {result.get('label')}, Score: {result.get('score'):.2f}"
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total_result += '\r\n'
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return total_result
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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!']], 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")
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