Update app.py

app.py

@@ -1,44 +1,44 @@
-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)
-    
-
-
-
-
-
+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.load(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)
+    
+
+
+
+
+