from transformers import AutoTokenizer, AutoModelForMaskedLM import torch BERTTokenizer = AutoTokenizer.from_pretrained("cl-tohoku/bert-base-japanese") BERTModel = AutoModelForMaskedLM.from_pretrained("cl-tohoku/bert-base-japanese") from transformers import AutoTokenizer, AutoModelForSeq2SeqLM mT5Tokenizer = AutoTokenizer.from_pretrained("google/mt5-base") mT5Model = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-base") from transformers import AutoTokenizer, AutoModelForCausalLM GPT2Tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt2-medium") GPT2Model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt2-medium") import gradio as gr votes=[] BERT=None mT5=None GPT2=None def MELCHIOR(sue): #BERT allow=BERTTokenizer("承認").input_ids[1] deny=BERTTokenizer("否定").input_ids[1] output=BERTModel(**BERTTokenizer('科学者としての人格を持ったMELCHIORは次の決議に答えます。人間「'+sue+'承認か否定どちらですか?」'+"MELCHIOR 「[MASK]」",return_tensors="pt")).logits BERTTokenizer.batch_decode(torch.argmax(output,-1)) mask=output[0,-3,:] votes.append(1 if mask[allow]>mask[deny] else -1) return "承認" if mask[allow]>mask[deny] else "否定" def BALTHASAR(sue): #mT5 allow=mT5Tokenizer("承認").input_ids[1] deny=mT5Tokenizer("否定").input_ids[1] encoder_output=mT5Model.encoder(**mT5Tokenizer('母としての人格を持ったBALTHASARは次の決議に答えます。人間「'+sue+'承認か否定どちらですか?」'+"BALTHASAR 「」",return_tensors="pt")) id=None p_answer=None probs=None i=0 txt="" probs=mT5Model(inputs_embeds=encoder_output.last_hidden_state,decoder_input_ids=mT5Tokenizer(txt,return_tensors="pt").input_ids).logits[0] id=torch.argmax(probs[i+1]) txt=txt+"" i=i+1 probs=mT5Model(inputs_embeds=encoder_output.last_hidden_state,decoder_input_ids=mT5Tokenizer(txt,return_tensors="pt").input_ids).logits[0] id=torch.argmax(probs[i+1]) txt=txt+mT5Tokenizer.decode(id) votes.append(1 if probs[i+1][allow]>probs[i+1][deny] else -1) return "承認" if probs[i+1][allow]>probs[i+1][deny] else "否定" def CASPER(sue): #GPT2 allow=GPT2Tokenizer("承認").input_ids[1] deny=GPT2Tokenizer("否定").input_ids[1] probs=GPT2Model(**GPT2Tokenizer('女としての人格を持ったCASPERは次の決議に答えます。人間「'+sue+'承認か否定どちらですか?」'+"CASPER 「",return_tensors="pt")).logits[0] i=0 p_answer=probs id=torch.argmax(probs[0]) votes.append(1 if probs[0][allow]>probs[1][deny] else -1) return "承認" if probs[0][allow]>probs[1][deny] else "否定" def greet(sue): text1="BERT-1"+MELCHIOR(sue) text2="GPT-2"+CASPER(sue) text3="mT5-3"+BALTHASAR(sue) return text1+" "+text2+" "+text3+"\n____\n\n"+("|可決|" if sum(votes[-3:])>0 else "|否決|")+"\n____" css=".gradio-container {background-color: black} .gr-button {background-color: blue;color:black; weight:200%;font-family:YuMincho}.block{color:orange;} .gr-box {text-align: center;font-size: 125%;border-color:orange;background-color: #000000;weight:200%;font-family:YuMincho}" with gr.Blocks(css=css) as demo: sue = gr.Textbox(label="NAGI System",placeholder="ここに決議内容を入力し,提訴を押してください.") greet_btn = gr.Button("提訴") output = gr.Textbox(label="決議") greet_btn.click(fn=greet, inputs=sue, outputs=output) demo.launch()