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import streamlit as st |
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import pandas as pd |
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import numpy as np |
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import re |
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import pickle |
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from tensorflow.keras.preprocessing.sequence import pad_sequences |
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from tensorflow.keras.models import load_model |
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model = load_model('sherlock_model.h5') |
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tokenizer = pickle.load(open( "tokenizer.pkl", "rb")) |
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word_index_df = pd.read_csv("word_index.csv", header=None, index_col=0) |
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word_index = {} |
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for word, index in word_index_df.to_dict()[1].items(): |
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word_index[word] = int(index) |
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tokenizer.word_index = word_index |
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st.title('Next Word Generator :writing_hand:') |
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st.write("This app predicts the next word using a model trained on words from the book 'THE ADVENTURES OF SHERLOCK HOLMES'.") |
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st.write("Write a few words and indicate how many words you want them to guess.") |
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def next_words(seed_text, n): |
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for _ in range(n): |
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token_list = tokenizer.texts_to_sequences([seed_text])[0] |
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token_list = pad_sequences([token_list], maxlen=17, padding='pre') |
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predicted = np.argmax(model.predict(token_list), axis=-1) |
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output_word = "" |
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for word, index in tokenizer.word_index.items(): |
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if index == predicted: |
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output_word = word |
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break |
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seed_text += " " + output_word |
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return seed_text |
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text = st.text_area("Enter text", height=80) |
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n=st.number_input("Word number", 1,100) |
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if st.button("Predict"): |
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sonuc=next_words(text, n) |
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st.info(f'Prediction : {sonuc}') |
