35 lines
2.0 KiB
Python
35 lines
2.0 KiB
Python
import gradio as gr
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from simplet5 import SimpleT5
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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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model = SimpleT5()
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model.load_model("t5","snrspeaks/t5-one-line-summary")
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def sentiment_analysis(text):
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result = model.predict(text)
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return result[0]
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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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examples=[["We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production machinelearning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a set of novel high-level, declarative abstractions. Overton's vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks. In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year, Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing. In that time, Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors 1.7-2.9 times versus production systems."]],
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theme = theme,
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title = "摘要"
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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")
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