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',('12M Param')) #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 == '12M Param':
    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)