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7664bb57f5
* demo fix * changelog * fix * demo --------- Co-authored-by: Abubakar Abid <abubakar@huggingface.co>
48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
import gradio as gr
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
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model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
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def user(message, history):
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return "", history + [[message, None]]
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def bot(history):
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user_message = history[-1][0]
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new_user_input_ids = tokenizer.encode(
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user_message + tokenizer.eos_token, return_tensors="pt"
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)
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# append the new user input tokens to the chat history
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bot_input_ids = torch.cat([torch.LongTensor([]), new_user_input_ids], dim=-1)
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# generate a response
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response = model.generate(
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bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id
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).tolist()
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# convert the tokens to text, and then split the responses into lines
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response = tokenizer.decode(response[0]).split("<|endoftext|>")
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response = [
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(response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
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] # convert to tuples of list
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history[-1] = response[0]
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return history
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with gr.Blocks() as demo:
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chatbot = gr.Chatbot()
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msg = gr.Textbox()
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clear = gr.Button("Clear")
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msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
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bot, chatbot, chatbot
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)
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clear.click(lambda: None, None, chatbot, queue=False)
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if __name__ == "__main__":
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demo.launch()
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