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"""TareaGradio.ipynb |
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Automatically generated by Colaboratory. |
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Original file is located at |
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https://colab.research.google.com/drive/1AW2m1E1wDh6Mx_6peo9439iAN5VdnCk5 |
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""" |
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! pip install transformers gradio -q |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq") |
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model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq") |
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from IPython.core import history |
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def predict(input, history=[]): |
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instruction = 'Instruction: given a dialog context, you need to response empathically' |
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knowledge = '' |
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s = list(sum(history, ())) |
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s.append(input) |
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dialog = ' EOS ' .join(s) |
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print(dialog) |
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query = f"{instruction} [CONTEXT] {dialog} {knowledge}" |
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top_p = 0.9 |
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min_length = 8 |
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max_length = 64 |
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new_user_input_ids = tokenizer.encode(f"{query}", return_tensors='pt') |
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output = model.generate(new_user_input_ids, min_length=int(min_length), max_length=int(max_length), top_p=top_p, do_sample=True).tolist() |
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response = tokenizer.decode(output[0], skip_special_tokens=True) |
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history.append((input, response)) |
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return history, history |
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import gradio as gr |
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gr.Interface(fn=predict, |
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inputs=["text", 'state'], |
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outputs =["chatbot", 'state']).launch(debug = True, share = True) |
