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import tensorflow.keras.backend as K
from tensorflow.keras.layers import LSTM
from pickle import load
import numpy as np
import tensorflow as tf
import gradio as gr
model_V2 = 'ByteLevelLM.h5'
K.clear_session()
tf.keras.backend.clear_session()
np.random.seed(42)
tf.random.set_seed(42)
HeNormal = tf.keras.initializers.he_normal()
daily_V2 = tf.keras.models.load_model(model_V2,
custom_objects={'HeNormal': HeNormal},compile=False)
#Tokenizer
def tokenize():
import json
with open('Tokenizer.json', encoding='utf-8') as f:
data = json.load(f)
tokenizer = tf.keras.preprocessing.text.tokenizer_from_json(data)
with open('index2char.json', encoding='utf-8') as f:
index2char = json.load(f)
char2index = dict((int(v),int(k)) for k,v in index2char.items())
tokenizer.word_index = char2index
return tokenizer
def model2_preds(news_headline_input):
headline = news_headline_input
headline = '<s>' + headline + '<\s'
tokenizer = tokenize()
sample_2 = headline.encode('utf-8')
sample_2 = tokenizer.texts_to_sequences([sample_2])
predict_v2 = daily_V2.predict(sample_2, verbose = 0)[0,0]
# app_type = ui_display(title = "Model 2 Predictions (256 Bits Embeddings)")
return "Probability of Buy Signal from News Headline/s: %f" % predict_v2
# Create an instance of the Gradio Interface application function with the appropriate parameters.
app = gr.Interface(fn=model2_preds,
title="Event Driven Trading (Byte Level Language Modelling)",
description='News headlines from OverNight concatenated for next day Buy/Sell Probability/Signal',
inputs = gr.Textbox(label="News Headline/s", info='Separate several news headlines by a space'),
outputs=gr.Textbox(show_label = True,label="Prediction", info='This is the probability to buy at market close today and sell market close tomorrow'),
submit_btn = 'Predict')
# Launch the app
if __name__ == '__main__':
app.launch(share=True)
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