import gradio as gr
from simplet5 import SimpleT5


model = SimpleT5()
model.load_model("t5","snrspeaks/t5-one-line-summary")

def sentiment_analysis(text):
    result = model.predict(text)

    return result[0]
    
demo = gr.Interface(fn=sentiment_analysis, 
                    inputs='text',
                    outputs='text',
                    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."]],
                    title = "摘要"
                    )


if __name__ == "__main__":
    demo.queue(concurrency_count=3)
    demo.launch(server_name = "0.0.0.0", server_port = 7028)