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import gradio as gr
from transformers import LineByLineTextDataset
from transformers import DataCollatorForLanguageModeling
from transformers import GPT2Tokenizer, GPT2LMHeadModel
from transformers import Trainer, TrainingArguments
def load_model(model_path):
model = GPT2LMHeadModel.from_pretrained(model_path)
return model
def load_tokenizer(tokenizer_path):
tokenizer = GPT2Tokenizer.from_pretrained(tokenizer_path)
return tokenizer
def generate_text(sequence, max_new_tokens):
ids = tokenizer.encode(f'{sequence}', return_tensors='pt')
input_length = ids.size(1)
max_length = input_length + max_new_tokens
final_outputs = model.generate(
ids,
do_sample=True,
max_length=max_length,
pad_token_id=model.config.eos_token_id
)
return tokenizer.decode(final_outputs[0], skip_special_tokens=True)
model_path = r'./checkpoint/'
model = load_model(model_path)
tokenizer = load_tokenizer('./tokenizer/')
def generate_text(sequence, max_new_tokens):
ids = tokenizer.encode(f'{sequence}', return_tensors='pt')
input_length = ids.size(1)
max_length = input_length + max_new_tokens
final_outputs = model.generate(
ids,
do_sample=True,
max_length=max_length,
pad_token_id=model.config.eos_token_id
)
return tokenizer.decode(final_outputs[0], skip_special_tokens=True)
iface = gr.Interface(fn=generate_text, inputs="text", outputs="text")
iface.launch() |