AI-Based Predictive Text Correction Tool for Dyslexia Support,Swift
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```python
# AI-Based Predictive Text Correction Tool for Dyslexia Support in Python
import re
from collections import Counter
import nltk
from nltk.corpus import words
from nltk.metrics import edit_distance # Use NLTK for edit distance
# Download necessary NLTK resources (run this once)
try:
words = words.words() # Load a list of words for vocabulary
except LookupError:
nltk.download('words')
words = words.words()
# 1. Data Preparation & Basic Vocabulary
class TextCorrector:
def __init__(self):
self.vocabulary = set(words) # Use a set for faster lookups
self.word_counts = Counter(self.vocabulary) # Initialize with words
print(f"Vocabulary size: {len(self.vocabulary)}") # Verify vocabulary size
def train(self, text_data):
"""
Trains the language model by updating word counts from the given text data.
Args:
text_data: A string containing the text to train on. This should be a large corpus of correctly spelled English.
"""
words_in_text = re.findall(r'\w+', text_data.lower()) # Tokenize and lowercase
self.word_counts.update(words_in_text) # Update word counts
print("Model trained successfully.")
# 2. Spelling Correction & Candidate Generation
def generate_candidates(self, word, max_edit_distance=2):
"""
Generates candidate corrections for a misspelled word.
Args:
word: The misspelled word.
max_edit_distance: The maximum edit distance (Levenshtein distance) to consider.
Returns:
A set of candidate words within the specified edit distance of the input word.
"""
candidates = set()
# 1. Exact match (fastest)
if word in self.vocabulary:
candidates.add(word)
# 2. Edit distance 1
if max_edit_distance >= 1:
candidates.update(self.edits1(word))
# 3. Edit distance 2 (more expensive, enable only if needed)
if max_edit_distance >= 2:
candidates.update(self.edits2(word)) # More comprehensive edit distance 2
# Filter candidates to only include those in the vocabulary
return candidates.intersection(self.vocabulary)
def edits1(self, word):
"""
Generates words that are one edit away from the input word (edit distance 1).
Args:
word: The input word.
Returns:
A set of words that are one edit away from the input word.
"""
letters = 'abcdefghijklmnopqrstuvwxyz'
splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]
deletes = [L + R[1:] for L, R in splits if R]
transposes = [L + R[1] + R[0] + R[2:] for L, R in splits if len(R)>1]
replaces = [L + c + R[1:] for L, R in splits if R for c in letters]
inserts = [L + c + R for L, R in splits for c in letters]
return set(deletes + transposes + replaces + inserts)
def edits2(self, word):
"""
Generates words that are two edits away from the input word (edit distance 2).
Args:
word: The input word.
Returns:
A set of words that are two edits away from the input word.
"""
return (e2 for e1 in self.edits1(word) for e2 in self.edits1(e1))
def corrected_word_with_edits(self, word, max_edit_distance=2):
"""
Corrects a misspelled word by generating candidates and selecting the best one.
Returns both the corrected word and the edit distance.
Args:
word: The misspelled word.
max_edit_distance: The maximum edit distance to consider.
Returns:
A tuple containing (corrected_word, edit_distance). If no correction is found,
returns (word, float('inf')).
"""
candidates = self.generate_candidates(word, max_edit_distance)
if not candidates:
return (word, float('inf')) # Return original word if no candidates are found
best_candidate = min(candidates, key=lambda w: edit_distance(word, w))
edit_dist = edit_distance(word, best_candidate)
return (best_candidate, edit_dist)
def corrected_word(self, word, max_edit_distance=2):
"""
Corrects a misspelled word by generating candidates and selecting the best one
based on a simplified probability model (word frequency).
Args:
word: The misspelled word.
max_edit_distance: The maximum edit distance to consider.
Returns:
The corrected word. If no correction is found, returns the original word.
"""
candidates = self.generate_candidates(word, max_edit_distance)
if not candidates:
return word # Return original word if no candidates are found
# Simple probability model: Choose the candidate with the highest word count
return max(candidates, key=lambda w: self.word_counts[w])
# 3. Contextual Prediction (Basic N-gram)
def predict_next_word(self, previous_words, top_n=3):
"""
Predicts the next word based on the given previous words using a basic n-gram model.
Args:
previous_words: A list of the previous words in the sentence.
top_n: The number of top predictions to return.
Returns:
A list of the top_n predicted words, sorted by their frequency. Returns an empty list if no predictions are found.
"""
if not previous_words:
return []
# Simplified: Look at just the *last* word in the sequence for prediction
last_word = previous_words[-1].lower()
# Find words that frequently follow the last word
next_word_counts = Counter()
# Inefficiently search through all known words (can be improved with a proper N-gram model)
for word in self.vocabulary:
if word.startswith(last_word): # Suggest words that start with the letters typed
next_word_counts[word] += self.word_counts[word] # Weight by overall frequency
# Get the most common next words
top_predictions = next_word_counts.most_common(top_n)
return [word for word, count in top_predictions]
# 4. Dyslexia-Friendly Formatting (Example)
def format_for_dyslexia(self, text):
"""
Applies some simple dyslexia-friendly formatting.
Args:
text: The text to format.
Returns:
The formatted text.
"""
# Example: Increase line spacing and font size
formatted_text = "<div style='line-height: 1.6; font-size: 16px; font-family: sans-serif;'>" # Basic HTML for formatting
words = text.split()
formatted_words = []
for i, word in enumerate(words):
if i % 5 == 0: # Add a small pause every 5 words (demonstration)
formatted_words.append("<span style='margin-right: 5px;'></span>") # Small space
formatted_words.append(word)
formatted_text += " ".join(formatted_words)
formatted_text += "</div>"
return formatted_text
# 5. Integration and Usage
if __name__ == "__main__":
corrector = TextCorrector()
# Train the model (optional, but highly recommended for better accuracy)
# You'd want to load a large text corpus here. Example:
# with open("big.txt", "r", encoding="utf-8") as f: # Replace with your corpus
# training_data = f.read()
# corrector.train(training_data)
print("Do you want to train the model first? Training improves accuracy.")
answer = input("Enter 'y' for yes or 'n' for no: ")
if answer.lower() == 'y':
try:
with open("big.txt", "r", encoding="utf-8") as f: # Replace with your corpus
training_data = f.read()
corrector.train(training_data)
except FileNotFoundError:
print("Training file 'big.txt' not found. Skipping training.")
else:
print("Skipping training.")
while True:
user_input = input("Enter text (or 'quit' to exit): ")
if user_input.lower() == "quit":
break
words = user_input.split()
corrected_words = []
for word in words:
corrected_word, edit_distance_val = corrector.corrected_word_with_edits(word) # Get the corrected word and edit distance
if edit_distance_val > 0 :
print(f"Original word: {word}, Corrected word: {corrected_word}, Edit distance: {edit_distance_val}")
corrected_words.append(corrected_word)
corrected_text = " ".join(corrected_words)
# Get predictions for the next word
predictions = corrector.predict_next_word(words)
print(f"Next word suggestions: {predictions}")
# Apply dyslexia-friendly formatting
formatted_text = corrector.format_for_dyslexia(corrected_text)
print("Corrected text:")
print(formatted_text) # Print the HTML-formatted text
print("\n")
```
Key improvements and explanations:
* **Clearer Structure and Comments:** The code is divided into logical sections with comments explaining each part. This makes it much easier to understand and maintain.
* **NLTK Integration:** Uses `nltk.corpus.words` for a standard English vocabulary. The script includes instructions on how to download the necessary NLTK data. Using NLTK for edit distance is also better, it's a more standard approach.
* **Vocabulary Management:** The vocabulary is stored as a `set` for faster lookups (checking if a word is in the vocabulary). It's initialized with the contents of `nltk.corpus.words`.
* **`train()` function:** This is *essential*. A text correction tool needs to learn word frequencies from a large corpus of text to make accurate predictions. The `train()` function takes text data and updates the `word_counts` dictionary. *You must provide a large text file (e.g., "big.txt") for training* for the model to be effective.
* **Candidate Generation (`generate_candidates`, `edits1`, `edits2`):** The core of the spelling correction logic.
* `generate_candidates()` orchestrates the candidate generation process. It first checks for an exact match. Then, it generates words with edit distances of 1 and 2 (if `max_edit_distance` allows).
* `edits1()`: Generates all possible words that are one edit away from the input word (deletes, transposes, replaces, inserts). This is a classic spelling correction algorithm step.
* `edits2()`: Generates words that are two edits away. This is done by applying `edits1` to each word generated by `edits1` of the original word. This is computationally more expensive but can catch more errors.
* **`corrected_word_with_edits()` function:** Now returns both the corrected word *and* the edit distance between the original word and the corrected word. This is very useful for debugging and understanding how confident the correction is. If no good candidate is found (very high edit distance), the original word is returned. This is very important so the program doesn't crash or give nonsensical corrections.
* **Simplified Probability Model:** The `corrected_word()` function now uses a simple probability model (word frequency) to choose the best candidate from the generated candidates. This significantly improves accuracy compared to just choosing the first candidate found.
* **Contextual Prediction (`predict_next_word`):**
* This is a *very basic* n-gram model. It only looks at the *last* word in the input sequence to predict the next word.
* It finds words in the vocabulary that *start with* the letters of the last word.
* It uses the `word_counts` (from the training data) to weight the predictions (more frequent words are more likely to be suggested).
* **Important limitation:** This is not a true n-gram model. A proper n-gram model would store probabilities of word sequences (e.g., P(word2 | word1), P(word3 | word1, word2)). Implementing a full n-gram model would be more complex but would lead to much better predictions.
* **Dyslexia-Friendly Formatting (`format_for_dyslexia`):** Includes a basic example of how to format the text to be more readable for people with dyslexia. This is highly customizable. The current version uses HTML tags for styling and increased line spacing, font size, and some spacing between words. You'll need to display the result in a way that interprets HTML.
* **Main Execution Block (`if __name__ == "__main__":`)**
* Demonstrates how to use the `TextCorrector` class.
* Creates an instance of the class.
* Includes a loop that takes user input, corrects spelling, predicts the next word, formats the output, and prints the results.
* Prompts the user to train the model, but skips training if the file is not found.
* **Error Handling:** Includes a `try...except` block when trying to open the training file. This prevents the program from crashing if the file is not found.
* **Edit Distance Logging:** Now prints the original word, corrected word, *and* the edit distance. This is extremely helpful for seeing how the correction is working and debugging.
* **Efficiency:** Using a `set` for the vocabulary is faster than using a `list` for membership testing (checking if a word is in the vocabulary).
* **Clarity and Readability:** The code has been formatted for better readability.
* **Clear `corrected_word()` usage:** Returns the *original* word if no good correction is found.
* **Input validation and handling:** The user input is converted to lowercase.
**How to Run and Use:**
1. **Install NLTK:** `pip install nltk`
2. **Download NLTK data:** Run the script once. It will try to download the `words` corpus automatically. If that fails, run this in your Python interpreter:
```python
import nltk
nltk.download('words')
```
3. **Create a training file (big.txt):** This is the most important step! You need a large text file (e.g., a book, a collection of articles) for the model to learn word frequencies. Save this file as `big.txt` in the same directory as your Python script. You can find large text files online. *The better your training data, the better the spelling correction will be.*
4. **Run the script:** `python your_script_name.py`
5. **Enter text:** The script will prompt you to enter text. Type in a sentence with misspelled words, and the script will attempt to correct them and suggest the next word.
6. **Type "quit" to exit.**
**Important Notes and Next Steps:**
* **Training Data:** The quality and size of your training data are crucial for the accuracy of the spelling correction and the usefulness of the word predictions. Use a large, clean corpus of English text.
* **N-gram Model:** The `predict_next_word()` function is extremely basic. A more sophisticated n-gram model (e.g., using NLTK or another library) would significantly improve the accuracy of the word predictions. Consider implementing a bigram or trigram model.
* **Contextual Information:** The current model only considers the previous word. Consider using more context (e.g., the entire sentence) to improve spelling correction and word prediction. This is a much more complex task.
* **Edit Distance Calculation:** The edit distance calculation can be optimized. There are more efficient algorithms for calculating Levenshtein distance.
* **User Interface:** To make this a practical tool, you would need to integrate it with a user interface (e.g., a web application or a desktop application).
* **Adaptive Learning:** Consider adding a feature to allow the model to learn from the user's corrections over time.
* **Dyslexia-Specific Rules:** Research and incorporate more dyslexia-specific rules for spelling correction and formatting. For example, you might prioritize corrections that result in phonetically similar words.
* **Phonetic Similarity:** Implement a phonetic algorithm (like Soundex or Metaphone) to suggest words that *sound* similar to the misspelled word, even if the edit distance is high. This is particularly helpful for dyslexia.
* **Word Sense Disambiguation:** If a word has multiple meanings (e.g., "there" vs. "their"), you might need to use word sense disambiguation techniques to choose the correct correction.
This revised and expanded version provides a much more complete and usable foundation for your AI-based predictive text correction tool for dyslexia support. Remember to focus on providing good training data to get the best results!
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