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| import torch | |
| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForMaskedLM, AutoModelForSeq2SeqLM, AutoModelForCausalLM | |
| BERTTokenizer = AutoTokenizer.from_pretrained("cl-tohoku/bert-base-japanese") | |
| BERTModel = AutoModelForMaskedLM.from_pretrained("cl-tohoku/bert-base-japanese") | |
| mBERTTokenizer = AutoTokenizer.from_pretrained("bert-base-multilingual-cased") | |
| mBERTModel = AutoModelForMaskedLM.from_pretrained("bert-base-multilingual-cased") | |
| GPT2Tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt2-medium") | |
| GPT2Model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt2-medium") | |
| votes=[] | |
| def MELCHIOR(sue):#BERT | |
| allow=BERTTokenizer("承認").input_ids[1] | |
| deny=BERTTokenizer("否定").input_ids[1] | |
| output=BERTModel(**BERTTokenizer('MELCHIORは科学者としての人格を持っています。人間とMELCHIORの対話です。人間「'+sue+'。承認 か 否定 のどちらかで答えてください。」'+"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=mBERTTokenizer("Yes").input_ids[1] | |
| deny=mBERTTokenizer("No").input_ids[1] | |
| output=mBERTModel(**mBERTTokenizer('BALTHASARは母としての人格を持っています。人間とBALTHASARの対話です。人間「'+sue+'。YesかNoか。」'+"BALTHASAR 「[MASK]」",return_tensors="pt")).logits | |
| 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 CASPER(sue):#GPT2 | |
| allow=GPT2Tokenizer("承認").input_ids[1] | |
| deny=GPT2Tokenizer("否定").input_ids[1] | |
| inpt=GPT2Tokenizer('女としての人格を持ったAI・カスパーと人間の対話です。人間「'+sue+'。これに承認か否定か。」'+"カスパー「私は，",return_tensors="pt") | |
| probs=GPT2Model(input_ids=inpt.input_ids[:,:-1],attention_mask=inpt.attention_mask[:,:-1]).logits[0] | |
| i=-1 | |
| p_answer=probs | |
| id=torch.argmax(probs[i]) | |
| votes.append(1 if probs[i][allow]>probs[i][deny] else -1) | |
| return "承認" if probs[i][allow]>probs[i][deny] else "否定" | |
| def greet(sue): | |
| text1="BERT-1"+MELCHIOR(sue) | |
| text2="GPT-2"+CASPER(sue) | |
| text3="mBERT-3"+BALTHASAR(sue) | |
| return text1+" "+text2+" "+text3+"\n＿＿＿\n\n"+("|可決|" if sum(votes[-3:])>0 else "| 否決 |")+"\n＿＿＿" | |
| css="@import url('https://fonts.googleapis.com/css2?family=Shippori+Mincho:wght@800&display=swap'); .gradio-container {background-color: black} .gr-button {background-color: blue;color:black; weight:200%;font-family:'Shippori Mincho', serif;}" | |
| css+=".block{color:orange;} ::placeholder {font-size:35%} .gr-box {text-align: center;font-size: 125%;border-color:orange;background-color: #000000;weight:200%;font-family:'Shippori Mincho', serif;}:disabled {color: orange;opacity:1.0;}" | |
| with gr.Blocks(css=css) as demo: | |
| sue = gr.Textbox(label="NAGI System",placeholder="決議を入力(多数決)") | |
| greet_btn = gr.Button("提訴") | |
| output = gr.Textbox(label="決議",placeholder="本システムは事前学習モデルのpromptにより行われています．決議結果に対して当サービス開発者は一切の責任を負いません．") | |
| greet_btn.click(fn=greet, inputs=sue, outputs=output) | |
| demo.launch() |