30 lines
962 B
Python
30 lines
962 B
Python
import gradio as gr
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from transformers import pipeline, AutoTokenizer, AutoConfig, AutoModelForSequenceClassification
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modelName="Seethal/sentiment_analysis_generic_dataset"
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tokenizer = AutoTokenizer.from_pretrained(modelName)
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model = AutoModelForSequenceClassification.from_pretrained(modelName)
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sentimentPipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
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Label2Des = {
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"LABEL_0": "NEGATIVE",
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"LABEL_1": "NEUTRAL",
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"LABEL_2": "POSITIVE"
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}
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def sentiment_analysis(text):
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results = sentimentPipeline(text)
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return f"Sentiment: {Label2Des.get(results[0]['label'])}, Score: {results[0]['score']:.2f}"
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demo = gr.Interface(fn=sentiment_analysis,
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inputs='text',
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outputs='text',
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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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