Update app.py

app.py

@@ -1,4 +1,68 @@
 import streamlit as st
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+from keras.datasets import imdb
+from keras.preprocessing import sequence
+import keras
+import tensorflow as tf
+import os
+import numpy as np
+
+VOCAB_SIZE = 88584
+
+MAXLEN = 250
+BATCH_SIZE = 64
+
+(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words = VOCAB_SIZE)
+train_data[1]
+
+train_data = sequence.pad_sequences(train_data, MAXLEN)
+test_data = sequence.pad_sequences(test_data, MAXLEN)
+
+model = tf.keras.Sequential([
+    tf.keras.layers.Embedding(VOCAB_SIZE, 32),
+    tf.keras.layers.LSTM(32),
+    tf.keras.layers.Dense(1, activation="sigmoid")
+])
+
+model.compile(loss="binary_crossentropy",optimizer="rmsprop",metrics=['acc'])
+
+history = model.fit(train_data, train_labels, epochs=20, validation_split=0.2)
+
+results = model.evaluate(test_data, test_labels)
+print(results)
+
+word_index = imdb.get_word_index()
+
+def encode_text(text):
+  tokens = keras.preprocessing.text.text_to_word_sequence(text)
+  tokens = [word_index[word] if word in word_index else 0 for word in tokens]
+  return sequence.pad_sequences([tokens], MAXLEN)[0]
+
+text = "that movie was just amazing, so amazing"
+encoded = encode_text(text)
+print(encoded)
+
+reverse_word_index = {value: key for (key, value) in word_index.items()}
+
+def decode_integers(integers):
+    PAD = 0
+    text = ""
+    for num in integers:
+      if num != PAD:
+        text += reverse_word_index[num] + " "
+
+    return text[:-1]
+
+print(decode_integers(encoded))
+
+def predict(text):
+  encoded_text = encode_text(text)
+  pred = np.zeros((1,250))
+  pred[0] = encoded_text
+  result = model.predict(pred)
+  print(result[0])
+
+positive_review = "That movie was! really loved it and would great watch it again because it was amazingly great"
+st.write(predict(positive_review))
+negative_review = "that movie really sucked. I hated it and wouldn't watch it again. Was one of the worst things I've ever watched"
+st.write(predict(negative_review))