Upload 3 files

app.py

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+import streamlit as st
+from keras.models import load_model
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import pickle
+import numpy as np
+
+max_sequence_len = 40
+
+st.title("Next Word Prediction using LSTM")
+seed_text = st.text_input('Enter initial text to start generating next words')
+next_words_count = st.slider('How many words to generate',  1, 8, 4)
+model = load_model('text_generator.h5')
+
+with open("tokenizer.pickle", "rb") as handle:
+    tokenizer = pickle.read(handle)
+
+def generate_text(seed_text, next_words, model, max_sequence_len):
+
+    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 = model.predict(token_list, verbose=0)
+
+        classes_x = np.argmax(predicted,axis=1)
+
+        output_word = ""
+        for word,index in tokenizer.word_index.items():
+            if index == classes_x:
+                output_word = word
+                break
+        seed_text += " "+output_word
+    return seed_text.title()
+
+if st.button("Submit", type="primary"):
+    output = generate_text(seed_text, next_words_count,model,max_sequence_len)
+    st.write(output)
+    
+
+
+
+
+

text_generator.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f3e920a7ddcdaed698f74b6647e582a70dea540569adbfeae84966c9a61ce9a6
+size 22930136

tokenizer.pickle

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3e9b15fba8b8707bf03b18603193b182b0b0ff46478b157edde18636b773be2b
+size 328855