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import gradio as gr
from transformers import pipeline
from transformers import Conversation
pipe = pipeline("conversational", model="PygmalionAI/pygmalion-6b")
conversation = Conversation()
conversation.add_message({"role": "assistant", "content": "How can I help you?"})
memory = []
def run_conversation(message, history):
#cache memory from user
memory.append({"role": "user", "content": message})
print(history)
if len(history) > 0:
for i in history:
conversation.add_message({"role":"user", "content":i[0]})
conversation.add_message({"role": "assistant", "content": i[1]})
for i in memory:
conversation.add_message(i)
print(conversation)
pipe(conversation)
return conversation.generated_responses[-1]
demo = gr.ChatInterface(
run_conversation,
chatbot = gr.Chatbot(height=500),
textbox = gr.Textbox(placeholder="Chat with me!", scale=7),
title = "Test",
description="Chat with me!",
examples=["hello"]
)
if __name__ == "__main__":
demo.queue().launch()
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