Update main.py

main.py

@@ -1,13 +1,20 @@
-from transformers import pipeline
+from simplet5 import SimpleT5
+model = SimpleT5()
 
-summarizer = pipeline("summarization")
+model.load_model("t5","snrspeaks/t5-one-line-summary")
 
-def get_summary(text, max_length=200, min_length=50):
-  summary = summarizer(text, max_length, min_length, do_sample=Flase)
-  return summary
- 
- for i in range(10):
-   my_input = input("Enter the text to summerize: ")
-   output = get_summary(my_input)
-   print(my_output)
-   
+abstract = """We describe a system called Overton, whose main design goal is to 
+support engineers in building, monitoring, and improving production machine learning 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.
+"""
+
+model.predict(abstract)