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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() |