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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 predict(input, history=[]): |
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new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt') |
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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(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist() |
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response = tokenizer.decode(history[0]).split("<|endoftext|>") |
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response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)] |
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return response, history |
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demo = gr.Blocks() |
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with demo: |
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gr.Markdown("Start typing below and then click **Run** to see the output.") |
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with gr.Row(): |
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inp = gr.Textbox(["text", "state"]) |
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out = gr.Textbox(["text", "state"]) |
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btn = gr.Button("Run") |
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btn.click(fn=predict, inputs=inp, outputs=out) |
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demo.launch() |