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# Workaround to install the lib without "setup.py"
import sys
from git import Repo
Repo.clone_from("https://github.com/dimitreOliveira/hub.git", "./hub")
sys.path.append("/hub")
import gradio as gr
import tensorflow as tf
import tensorflow_text as text # Registers the ops. (needed for "bert_preprocessor")
from hub.tensorflow_hub.hf_utils import pull_from_hub
def model_fn(preprocessor, encoder):
text_input = tf.keras.layers.Input(shape=(), dtype=tf.string)
encoder_inputs = preprocessor(text_input)
outputs = encoder(encoder_inputs)
pooled_output = outputs["pooled_output"]
return tf.keras.Model(text_input, pooled_output)
def predict_fn(text):
print(text)
print(tf.constant([text]))
embed = model(tf.constant([text]))[0].numpy()
return embed
preprocessor = pull_from_hub(repo_id="Dimitre/bert_en_cased_preprocess")
encoder = pull_from_hub(repo_id="Dimitre/bert_en_cased_L-12_H-768_A-12")
model = model_fn(preprocessor, encoder)
iface = gr.Interface(fn=predict_fn,
title="BERT sentence embeddings",
description="Get the embeddings from your sentences using BERT",
inputs=gr.Textbox(lines=2, placeholder="Text input here...", label="Text"),
outputs="text",
examples=[["Hello! This is a random sentence"]])
iface.launch() |