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import streamlit as st
import tensorflow as tf
from keras.layers import Input, Dense, Embedding, MultiHeadAttention
from keras.layers import Dropout, LayerNormalization
from keras.models import Model
from keras.utils import pad_sequences
import numpy as np
class TransformerChatbot(Model):
def __init__(self, vocab_size, max_len, d_model, n_head, ff_dim, dropout_rate):
super(TransformerChatbot, self).__init__()
self.embedding = Embedding(vocab_size, d_model)
self.attention = MultiHeadAttention(num_heads=n_head, key_dim=d_model)
self.norm1 = LayerNormalization(epsilon=1e-6)
self.dropout1 = Dropout(dropout_rate)
self.dense1 = Dense(ff_dim, activation="relu")
self.dense2 = Dense(d_model)
self.norm2 = LayerNormalization(epsilon=1e-6)
self.dropout2 = Dropout(dropout_rate)
self.flatten = tf.keras.layers.Flatten()
self.fc = Dense(vocab_size, activation="softmax")
self.max_len = max_len
def call(self, inputs):
x = self.embedding(inputs)
# Masking
mask = self.create_padding_mask(inputs)
attn_output = self.attention(x, x, x, attention_mask=mask)
x = x + attn_output
x = self.norm1(x)
x = self.dropout1(x)
x = self.dense1(x)
x = self.dense2(x)
x = self.norm2(x)
x = self.dropout2(x)
x = self.fc(x)
return x
def create_padding_mask(self, seq):
mask = tf.cast(tf.math.equal(seq, 0), tf.float32)
return mask[:, tf.newaxis, tf.newaxis, :]
st.title("UniGLM TEXT completion Model")
st.subheader("Next Word Prediction AI Model by Webraft-AI")
#Picking what NLP task you want to do
option = st.selectbox('Model',('1')) #option is stored in this variable
#Textbox for text user is entering
st.subheader("Enter the text you'd like to analyze.")
text = st.text_input('Enter word: ') #text is stored in this variable
if option == '1':
loaded_dict = np.load("dict_predict3.bin.npz", allow_pickle=True)
word_to_num = loaded_dict["word_to_num"].item()
num_to_word = loaded_dict["num_to_word"].item()
X = loaded_dict["X"].item()
Y = loaded_dict["Y"].item()
X_train = pad_sequences([X])
y_train = pad_sequences([Y])
vocab_size = 100000
max_len = 1
d_model = 64 # 64 , 1024
n_head = 4 # 8 , 16
ff_dim = 256 # 256 , 2048
dropout_rate = 0.1 # 0.5 , 0.2
chatbot = TransformerChatbot(vocab_size, max_len, d_model, n_head, ff_dim, dropout_rate)
chatbot.load_weights("predict3")
chatbot.build(input_shape=(None, max_len)) # Build the model
chatbot.compile(optimizer="adam", loss="sparse_categorical_crossentropy")
other_text1 = text
for i in range(1):
other_text1 = other_text1.lower()
other_words1 = other_text1.split()
if len(other_words1) > 1:
st.write("Error: Found more than 1 word . There should not be more than one word in the prompt ")
for word in other_words1:
if word not in word_to_num:
st.write("Error: The word ` ",word," ` doesn't exist in the vocabulary and hence the model wasn't train on that. ")
else:
other_num1 = word_to_num[word]
given_X1 = other_num1
input_sequence1 = pad_sequences([given_X1], maxlen=max_len, padding='post')
output_sentence = other_text1 + ""
for _ in range(16):
predicted_token = np.argmax(chatbot.predict(input_sequence1), axis=-1)
predicted_token = predicted_token.item()
out = num_to_word[predicted_token]
output_sentence += " " + out
if out == ".":
break
given_X1 = given_X1[1:]
given_X1.append(predicted_token)
input_sequence1 = pad_sequences([given_X1], maxlen=max_len, padding='post')
out = output_sentence
else:
out = "Wrong Model"
st.write("Predicted Text: ")
st.write(out) |