Initial requirements.txt

app.py

@@ -1,31 +1,14 @@
-# import gradio as gr
-# from transformers import BertTokenizer, BartForConditionalGeneration
-#
-#
-# tokenizer = BertTokenizer.from_pretrained("HIT-TMG/dialogue-bart-large-chinese")
-# model = BartForConditionalGeneration.from_pretrained("HIT-TMG/dialogue-bart-large-chinese")
-#
-#
-# def generate_reponse(history):
-#     history_prefix = "对话历史："
-#     history = history_prefix + history
-#
-#     input_ids = tokenizer(history, return_tensors='pt').input_ids
-#     output_ids = model.generate(input_ids)[0]
-#
-#     return tokenizer.decode(output_ids, skip_special_tokens=True)
-#
-#
-# iface = gr.Interface(fn=generate_reponse, inputs="text", outputs="text")
-# iface.launch()
-
-
 import gradio as gr
 from transformers import BertTokenizer, BartForConditionalGeneration
 
 
-title = "标题"
-description = "描述"
+title = "HIT-TMG/dialogue-bart-large-chinese"
+description = """
+This is a seq2seq model fine-tuned on several Chinese dialogue datasets, from bart-large-chinese.
+See some details of model card at https://huggingface.co/HIT-TMG/dialogue-bart-large-chinese .
+
+Input example: 可以 认识 一下 吗 ？[SEP]当然 可以 啦 ， 你好 。[SEP]嘿嘿 你好 ， 请问 你 最近 在 忙 什么 呢 ？[SEP]我 最近 养 了 一只 狗狗 ， 我 在 训练 它 呢 。
+"""
 
 
 tokenizer = BertTokenizer.from_pretrained("HIT-TMG/dialogue-bart-large-chinese")
@@ -45,7 +28,7 @@ def chat(history):
 chatbot = gr.Chatbot().style(color_map=("green", "pink"))
 demo = gr.Interface(
     chat,
-    inputs=gr.Textbox(lines=2, placeholder="输入你的对话历史"),
+    inputs=gr.Textbox(lines=2, placeholder="输入你的对话历史（请以'[SEP]'作为每段对话的间隔）\nInput the dialogue history (Please split utterances by '[SEP]')"),
     title=title,
     description=description,
     outputs =["text"]