add more info

app.py

@@ -13,31 +13,6 @@ translator.to(device)
 summarizer.to(device)
 
 
-# def generate_multiple_choice_question(
-#     context
-# ):
-#     num_questions = 1
-#     question_item = question_generation_sampling(
-#         g1_model, g1_tokenizer,
-#         g2_model, g2_tokenizer,
-#         context, num_questions, device
-#     )[0]
-#     question = question_item['question']
-#     options = question_item['options']
-#     options[0] = f"{options[0]} [ANSWER]"
-#     random.shuffle(options)
-#     output_string = f"Question: {question}\n[A] {options[0]}\n[B] {options[1]}\n[C] {options[2]}\n[D] {options[3]}"
-#     return output_string
-#
-# demo = gr.Interface(
-#     fn=generate_multiple_choice_question,
-#     inputs=gr.Textbox(lines=8, placeholder="Context Here..."),
-#     outputs=gr.Textbox(lines=5, placeholder="Question: \n[A] \n[B] \n[C] \n[D] "),
-#     title="Multiple-choice Question Generator",
-#     description="Provide some context (e.g. news article or any passage) in the context box and click **Submit**. The models currently support English only. This demo is a part of MQAG - https://github.com/potsawee/mqag0.",
-#     allow_flagging='never'
-# )
-
 def generate_output(
     task,
     text,
@@ -76,7 +51,7 @@ demo = gr.Interface(
     # examples=[["Building a translation demo with Gradio is so easy!", "eng_Latn", "spa_Latn"]],
     cache_examples=False,
     title="English🇬🇧 to Thai🇹🇭 | Translation or Summarization",
-    description="Provide some text (in English) & select one of the tasks (Translation or Summarization). Note that currently the model only supports text up to 1024 tokens. The base architecture is mt5-large with the embeddings filtered to only English and Thai tokens and fine-tuned to XSum (Eng2Thai) Dataset (https://huggingface.co/datasets/potsawee/xsum_eng2thai).",
+    description="Provide some text (in English) & select one of the tasks (Translation or Summarization). Note that currently the model only supports text up to 1024 tokens. The base architecture is mt5-large with the embeddings filtered to only English and Thai tokens and fine-tuned to XSum (Eng2Thai) Dataset (https://huggingface.co/datasets/potsawee/xsum_eng2thai). This is only after training for 1 epoch of xsum (the quality is not production-ready), just a quick proof-of-concept about fine-tuning on translated texts.",
     allow_flagging='never'
 
 )