Create app.py

app.py

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+from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+# Load the pretrained T5 model
+model_name = "t5-small"
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+
+# Your input text
+input_text = "LLMs are pre-trained on a massive amount of data"
+"They are extremely flexible because they can be trained to perform a variety of tasks"
+"such as text generation, summarization, and translation"
+"They are also scalable because they can be fine-tuned to specific tasks, which can improve their performance"
+# Prefix the input with a prompt so T5 knows this is a summarization task
+prompt = "summarize: " + input_text
+
+# Tokenize and generate the summary
+inputs = tokenizer.encode(prompt, return_tensors="pt", max_length=512, truncation=True)
+summary_ids = model.generate(inputs, max_length=150, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True)
+summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+print("Summary:")
+print(summary)