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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import gradio as gr |
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import torch |
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title = " AI-test" |
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description = "A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)" |
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examples = [["How are you?"]] |
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tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large") |
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model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large") |
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def predict(input, history=[]): |
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new_user_input_ids = tokenizer.encode( |
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input + tokenizer.eos_token, return_tensors="pt" |
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) |
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bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1) |
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history = model.generate( |
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bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id |
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).tolist() |
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response = tokenizer.decode(history[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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] |
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return response, history |
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gr.Interface( |
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fn=predict, |
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title=title, |
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description=description, |
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examples=examples, |
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inputs=["text", "state"], |
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outputs=["chatbot", "state"], |
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theme="finlaymacklon/boxy_violet", |
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).launch() |
