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import gradio as gr
import transformers
# import tokenizers
import torch
from transformers import pipeline, set_seed
from transformers import GPT2Model, GPT2Config, GPT2LMHeadModel, AutoModel
from transformers import BertTokenizerFast, BertTokenizer
# https://huggingface.co/docs/hub/spaces-sdks-gradio
# tokenizer_bert = BertTokenizer.from_pretrained('bert-base-chinese',
# additional_special_tokens=["<s>","<pad>","</s>","<unk>","<mask>"],
# pad_token='<pad>', max_len=512)
# configuration = GPT2Config(vocab_size=25000, n_layer=8)
# model = GPT2LMHeadModel(config=configuration)
# path2pytorch_model = "/home/binxuwang/Datasets/ancChn_L8_LB_cont_output/checkpoint-100000/pytorch_model.bin"
# model.load_state_dict(torch.load(path2pytorch_model))
# model.from_pretrained("binxu/Ziyue-GPT2")
#%%
# model = GPT2LMHeadModel.from_pretrained("binxu/Ziyue-GPT2")
model = GPT2LMHeadModel.from_pretrained("binxu/Ziyue-GPT2-deep")
generator = pipeline('text-generation', model=model, tokenizer='bert-base-chinese')
def generate(prompt, num_beams, max_length, repetition_penalty, ):
torch.manual_seed(42)
outputs = generator(prompt, max_length=max_length, num_return_sequences=5, num_beams=num_beams, repetition_penalty=repetition_penalty)
output_texts = [output['generated_text'] for output in outputs]
output_all = "\n\n".join(output_texts)
return output_all
examples = [["子曰", 10, 50, 1.5, ],
["子墨子曰", 10, 50, 1.5, ],
["孟子", 10, 50, 1.5, ],
["秦王", 10, 50, 1.5, ],
["子路问仁", 10, 50, 1.5, ],
["孙行者笑道", 10, 50, 1.5, ],
["牛魔王与红孩儿", 10, 50, 1.5, ],
["鲲鹏", 10, 50, 1.5, ],
["宝玉道", 10, 50, 1.5, ],
["黛玉行至贾母处", 10, 50, 1.5, ],]
iface = gr.Interface(fn=generate,
inputs=[gr.inputs.Textbox(lines=2, label="Prompt"),
gr.inputs.Slider(minimum=1, maximum=20, default=10, step=1, label="Number of beams"),
gr.inputs.Slider(minimum=10, maximum=100, default=50, step=1, label="Max length"),
gr.inputs.Slider(minimum=1, maximum=5, default=1.5, label="Repetition penalty"),
],
outputs=gr.outputs.Textbox(label="Generated Text"),
examples=examples)
iface.launch()
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