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app.py
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app.py
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import tensorflow as tf
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.models import Sequential
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.models import load_model
import streamlit as st
import numpy as np
#loading the pre-trained weights and model architecture
model = tf.keras.models.load_model("./model/next-word-model.h5")
st.title("Get next word!")
st.subheader("A Deep Learning Model that predicts the next likely sequence of words")
file = open("./corpus/goodwill.txt").read()
tokenizer = Tokenizer()
data = file.lower().split("\n")
corpus = []
for line in data:
a = line.strip()
corpus.append(a)
tokenizer.fit_on_texts(corpus)
total_words = len(tokenizer.word_index) + 1
print(tokenizer.word_index)
print(total_words)
# Creating labels for each sentence in dataset
input_sequences = []
for line in corpus:
token_list = tokenizer.texts_to_sequences([line])[0]
for i in range(1, len(token_list)):
n_gram_sequence = token_list[:i+1]
input_sequences.append(n_gram_sequence)
# Padding the sequences
max_sequence_len = max([len(x) for x in input_sequences])
input_sequences = pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre')
# Create predictors and label
xs, labels = input_sequences[:,:-1],input_sequences[:,-1]
ys = tf.keras.utils.to_categorical(labels, num_classes=total_words)
num = st.slider("Number of text predictions?",0,10)
# Generating next words given a seed sentence
def next_word(seed):
seed_text = seed
next_words = num
for _ in range(next_words):
token_list = tokenizer.texts_to_sequences([seed_text])[0]
token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
predicted = np.argmax(model.predict(token_list), axis=1)
output_word = ""
for word, index in tokenizer.word_index.items():
if index == predicted:
output_word = word
break
seed_text += " " + output_word
st.subheader(seed_text)
# Getting the output/predicted text
next_word(st.text_input('Enter seed sentence', 'I am going to'))