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| from transformers import AutoModelForCausalLM,AutoTokenizer | |
| import torch | |
| import gradio as gr | |
| tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large") | |
| model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large") | |
| def predict(input, history=[]): | |
| new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt') | |
| bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1) | |
| history = model.generate(bot_input_ids,max_length=1000,pad_token_id=tokenizer.eos_token_id).tolist() | |
| response = tokenizer.decode(history[0]).split("<|endoftext|>") | |
| response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)] | |
| return response, history | |
| demo = gr.Interface(fn=predict, | |
| inputs=["text", "state"], | |
| outputs=["chatbot", "state"]) | |
| demo.launch() |