# Word predictor in Python and TensorFlow

An example of a simple neural network using TensorFlow in Python that predicts the next word in a string:

import tensorflow as tf

# Define the input and expected output
input_text = "The cat sat on the"
expected_output_word = "mat"

# Preprocess the input text by splitting it into individual words
# and converting each word to lowercase
input_words = input_text.lower().split()

# Define the size of the vocabulary
vocab_size = 1000

# Create a one-hot encoded version of the input text
input_one_hot = tf.one_hot([input_words], depth=vocab_size)

# Define the size of the hidden layer in the neural network
hidden_layer_size = 10

# Create a dense layer with the specified hidden layer size
dense_layer = tf.keras.layers.Dense(hidden_layer_size)

# Pass the one-hot encoded input through the dense layer
output = dense_layer(input_one_hot)

# Use a softmax activation function to convert the output to probabilities
output = tf.nn.softmax(output)

# Get the index of the predicted word
predicted_word_index = tf.argmax(output)

# Look up the predicted word in the vocabulary
predicted_word = vocab[predicted_word_index]

# Check if the predicted word is the expected output word
if predicted_word == expected_output_word:
    print("The model predicted the correct next word!")
else:
    print(f"The model predicted the wrong next word. Expected {expected_output_word} but got {predicted_word}")

This is just a basic example to illustrate how a simple neural network using TensorFlow in Python can be used to predict the next word in a string. You may need to add additional preprocessing steps, layers, and hyperparameters to make the model more accurate.

## Training
To train this network, you will need to provide a dataset of input strings and their corresponding expected output words. Then, you can use the tf.keras.Model.fit() method to train the model on the dataset. Here is an example of how you can do this:

import tensorflow as tf

# Define the input and expected output
input_text = "The cat sat on the"
expected_output_word = "mat"

# Preprocess the input text by splitting it into individual words
# and converting each word to lowercase
input_words = input_text.lower().split()

# Define the size of the vocabulary
vocab_size = 1000

# Create a one-hot encoded version of the input text
input_one_hot = tf.one_hot([input_words], depth=vocab_size)

# Define the size of the hidden layer in the neural network
hidden_layer_size = 10

# Create a dense layer with the specified hidden layer size
dense_layer = tf.keras.layers.Dense(hidden_layer_size)

# Pass the one-hot encoded input through the dense layer
output = dense_layer(input_one_hot)

# Use a softmax activation function to convert the output to probabilities
output = tf.nn.softmax(output)

# Get the index of the predicted word
predicted_word_index = tf.argmax(output)

# Look up the predicted word in the vocabulary
predicted_word = vocab[predicted_word_index]

# Check if the predicted word is the expected output word
if predicted_word == expected_output_word:
    print("The model predicted the correct next word!")
else:
    print(f"The model predicted the wrong next word. Expected {expected_output_word} but got {predicted_word}")

# Define the training dataset
dataset = tf.data.Dataset.from_tensor_slices((input_one_hot, expected_output_word))

# Shuffle and batch the dataset
dataset = dataset.shuffle(1000).batch(32)

# Define the number of training epochs
epochs = 10

# Train the model on the dataset
model.fit(dataset, epochs=epochs)

This code trains the model on the dataset using the tf.keras.Model.fit() method. You can adjust the number of training epochs and the batch size to optimize the model's performance.

## Prepare the dataset
To prepare the dataset from a large amount of text, you will need to process the text to extract the input strings and their corresponding expected output words. You can do this using natural language processing techniques such as tokenization, stemming, and lemmatization.

For example, you can use the nltk library in Python to tokenize the text into individual words. You can then use the WordNetLemmatizer class in the nltk.stem module to lemmatize the words, which will convert each word to its base form.

Here is an example of how you can do this:

import nltk
from nltk.stem import WordNetLemmatizer

# Define the input text
input_text = "The cat sat on the mat. The dog barked at the cat."

# Tokenize the text into individual words
words = nltk.word_tokenize(input_text)

# Lemmatize the words
lemmatizer = WordNetLemmatizer()
lemmas = [lemmatizer.lemmatize(word) for word in words]

# Print the lemmatized words
print(lemmas)

This code will output the following lemmatized words:

['The', 'cat', 'sat', 'on', 'the', 'mat', '.', 'The', 'dog', 'barked', 'at', 'the', 'cat', '.']

You can then use the lemmatized words to create the input strings and expected output words for your dataset. For example, you could create a dataset where each input string is the sequence of n words before the expected output word, and the expected output word is the next word in the sequence.

For example, using the lemmatized words from the previous code, you could create a dataset where the input strings are sequences of 3 words and the expected output words are the next words in the sequence, like this:

Input string: "The cat sat on"
Expected output word: "the"

Input string: "cat sat on the"
Expected output word: "mat"

Input string: "sat on the mat"
Expected output word: "."

Input string: "on the mat ."
Expected output word: "The"

Input string: "the mat . The"
Expected output word: "dog"

Input string: "mat . The dog"
Expected output word: "barked"

Input string: "The dog barked at"
Expected output word: "the"

Input string: "dog barked at the"
Expected output word: "cat"

You can use this dataset to train your neural network, as described in the previous answer. You can also experiment with different values of n to see how it affects the model's performance.