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()