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import gradio as gr | |
from transformers import pipeline | |
import keras_nlp | |
import tensorflow as tf | |
import gdown | |
from tensorflow import keras | |
import time | |
def download_model_NLP(): | |
preprocessor = keras_nlp.models.GPT2CausalLMPreprocessor.from_preset( | |
"gpt2_base_en", | |
sequence_length=128,) | |
model = keras_nlp.models.GPT2CausalLM.from_preset( | |
"gpt2_base_en", preprocessor=preprocessor) | |
model_clear = model | |
#output = "GPT2_keras_deer" | |
#id_weights = "106fk9vGF_sHB8St9B2cKYE9jQVNSdKW_" | |
#gdown.download(id=id_weights, output=output, quiet=False) | |
#id_index = "10EWM2kJG4djpI5T6VLuj0UkGCbVIjmJx" | |
#gdown.download(id=id_index, output=output, quiet=False) | |
#id_check_point = "10Et7a0EzyrnnBhMWzt6frXLiNVm8a2qz" | |
#gdown.download(id=id_check_point, output=output, quiet=False) | |
#id_folder = "1zi3hSBRTP9uwHDVwQMqpGeXb8RBhJfrO" | |
#gdown.download_folder(id=id_folder, quiet=True, use_cookies=False) | |
# id = "1-CiBtOhzyVuiUGShONZqluey7TyX9-Y6" | |
output = "123.h5" | |
id = "1-CiBtOhzyVuiUGShONZqluey7TyX9-Y6" | |
gdown.download(id=id, output=output, quiet=False) | |
model.load_weights(output) | |
#model = tf.keras.models.load_model(output) | |
return model_clear, model | |
def get_model(): | |
return pipeline('text-generation', model='gpt-2') | |
def complete_text(start_of_sentence): | |
result_clear = model_clear.generate(start_of_sentence, max_length=100) | |
result = model.generate(start_of_sentence, max_length=100) | |
#result = model(start_of_sentence, max_length=50, do_sample=True)[0]['generated_text'] | |
return result | |
model_clear, model = download_model_NLP() | |
iface = gr.Interface(fn=complete_text, inputs=gr.inputs.Textbox(lines=2, placeholder='Start of Sentence Here...'), outputs=["text", "text"],) | |
iface.launch() |