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| import streamlit as st | |
| import numpy as np | |
| st.set_page_config( | |
| page_title="NLP WEB APP" | |
| ) | |
| st.title("NEXT WORD PREDICTOR") | |
| st.sidebar.success("Select a page above") | |
| string1 = st.text_area("Enter the training text (Note : This may take time depending upon the data size )") | |
| test = st.text_input("ENTER THE WORD") | |
| number = st.number_input("Enter the number of next words" ) | |
| number = int(number) | |
| import tensorflow as tf | |
| import numpy as np | |
| from tensorflow.keras.preprocessing.text import Tokenizer | |
| from tensorflow.keras.preprocessing.sequence import pad_sequences | |
| from tensorflow.keras.utils import to_categorical | |
| from tensorflow.keras.models import Sequential | |
| from tensorflow.keras.layers import Embedding,Dense,LSTM | |
| if st.button("PREDICT"): | |
| tokenizer = Tokenizer() | |
| tokenizer.fit_on_texts([string1]) | |
| input_sequences =[] | |
| for sentence in string1.split("\n"): | |
| tokenized_sentences = tokenizer.texts_to_sequences([sentence])[0] | |
| for i in range(1,len(tokenized_sentences)): | |
| input_sequences.append(tokenized_sentences[:i+1]) | |
| max_len = max([len(x) for x in input_sequences]) | |
| use = max_len-1 | |
| padded_input_sentences = pad_sequences(input_sequences , maxlen = max_len , padding ="pre") | |
| X = padded_input_sentences[:,:-1] | |
| Y = padded_input_sentences[:,-1] | |
| num_class = len(tokenizer.word_index) | |
| Y = to_categorical(Y , num_classes=num_class+1) | |
| model = Sequential() | |
| model.add(Embedding(num_class+1,100,input_length = None)) | |
| model.add(LSTM(250)) | |
| model.add(Dense(num_class+1,activation ="softmax")) | |
| model.compile(loss="categorical_crossentropy" , optimizer="adam" , metrics=["accuracy"]) | |
| model.fit(X,Y,epochs=100) | |
| for i in range(number): | |
| output_token = tokenizer.texts_to_sequences([test])[0] | |
| padded_token = pad_sequences([output_token] , maxlen=max_len, padding="pre") | |
| output = np.argmax(model.predict(padded_token)) | |
| for word,index in tokenizer.word_index.items(): | |
| if index == output: | |
| test =test + " " + word | |
| st.header(test) | |