prueba como en la documentación

app.py

@@ -9,16 +9,14 @@ tokenizer = T5Tokenizer.from_pretrained(model_name)
 # Define a function to generate text using T5
 def generate_text(prompt):
     # Tokenize input and generate output
-    input_ids = tokenizer.encode(prompt, return_tensors="pt", max_length=512, truncation=True)
+    #input_ids = tokenizer.encode(prompt, return_tensors="pt", max_length=512, truncation=True)
+    input_ids = tokenizer.encode(prompt, return_tensors="pt").input_ids
+    
     output_ids = model.generate(input_ids)
     
-    for i in range(len(output_ids)):
-        print(f"-----partes del output: ({i})")
-        print(tokenizer.decode(output_ids[i], skip_special_tokens=True))
-        print("---")
-
     # Decode the generated output
-    generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    #generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    generated_text = tokenizer.decode(outputs[0])
     
     return generated_text