| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer | |
| import torch | |
| tokenizer = AutoTokenizer.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq") | |
| from IPython.core import history | |
| def predict(input, history=[]): | |
| instruction = 'Instruction: given a dialog context, you need to response empathically' | |
| knowledge = '' | |
| s = list(sum(history, ())) | |
| s.append(input) | |
| dialog = ' EOS ' .join(s) | |
| print(dialog) | |
| query = f"{instruction} [CONTEXT] {dialog} {knowledge}" | |
| top_p = 0.9 | |
| min_length = 8 | |
| max_length = 64 | |
| new_user_input_ids = tokenizer.encode(f"{query}", return_tensors='pt') | |
| 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() | |
| response = tokenizer.decode(output[0], skip_special_tokens=True) | |
| history.append((input, response)) | |
| return history, history | |
| import gradio as gr | |
| gr.Interface(fn=predict, | |
| inputs=["text", 'state'], | |
| outputs =["chatbot", 'state']).launch(debug = True, share = True) |