app.py

from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch

# Load the T5 tokenizer and model
model_name = "t5-small"  # You can use any T5 model available
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

# Example function to use the model
def summarize(text):
    # Tokenize the input text
    inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=512, truncation=True)
    # Generate summary
    outputs = model.generate(inputs, max_length=150, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return summary

# Example usage
text_to_summarize = "Your input text goes here."
print(summarize(text_to_summarize))