| from transformers import pipeline, Conversation | |
| import gradio as gr | |
| #这个Space主要是演示了可以直接使用Huggingface的pipeline构建AIApp，然后还刚好可以和Gradio的ChatInterface对应上！ | |
| chatbot = pipeline(model="facebook/blenderbot-400M-distill") #Working! | |
| #https://huggingface.co/facebook/blenderbot-400M-distill/tree/main | |
| #这个模型文件大小：730MB或1.46GB | |
| #https://huggingface.co/facebook/blenderbot-400M-distill/tree/main?library=true | |
| # Use a pipeline as a high-level helper | |
| #from transformers import pipeline | |
| #pipe = pipeline("conversational", model="facebook/blenderbot-400M-distill") | |
| #chatbot = pipeline(model="HuggingFaceH4/starchat-beta") | |
| #https://huggingface.co/HuggingFaceH4/starchat-beta/tree/main | |
| #由于这个模型太大了（9.96+9.86+9.86+1.36GB），会导致如下错误： | |
| #Runtime error | |
| #Memory limit exceeded (16Gi) | |
| #chatbot = pipeline(model="...") | |
| message_list = [] | |
| response_list = [] | |
| def vanilla_chatbot(message, history): | |
| conversation = Conversation(text=message, past_user_inputs=message_list, generated_responses=response_list) | |
| conversation = chatbot(conversation) | |
| return conversation.generated_responses[-1] | |
| demo_chatbot = gr.ChatInterface(vanilla_chatbot, title="Vanilla Chatbot", description="Enter text to start chatting.") | |
| demo_chatbot.launch() |