Smart Calculator with NLP
1. Introduction
A Smart Calculator with NLP interprets natural language queries to perform mathematical operations. This project combines basic Natural Language Processing (NLP) techniques with Python's computation capabilities to create a calculator that understands user queries in plain language.
2. Prerequisites
• Python: Install Python 3.x from the official Python
website.
• Required Libraries:
- nltk: Install using pip install nltk
- sympy: Install using pip install
sympy
- num2words (optional): Install using
pip install num2words (for word-to-number conversion)
• Basic knowledge of Python programming and Natural Language Processing.
3. Project Setup
1. Create a Project Directory:
- Name your project folder, e.g., `SmartCalculator`.
- Inside this folder, create a Python script file (`smart_calculator.py`).
2. Install Required Libraries:
Ensure nltk, sympy, and num2words are installed using `pip`.
4. Writing the Code
Below is the Python code for the Smart Calculator:
import nltk
from sympy import sympify
from num2words import num2words
from word2number import w2n
# Ensure nltk corpus is downloaded
nltk.download('punkt')
# Function to process and evaluate natural language math queries
def evaluate_expression(query):
try:
# Tokenize and preprocess the
input query
tokens =
nltk.word_tokenize(query.lower())
numbers = {"zero": 0,
"one": 1, "two": 2, "three": 3, "four":
4,
"five": 5,
"six": 6, "seven": 7, "eight": 8,
"nine": 9}
processed_query = "
".join(str(numbers[word]) if word in numbers else word for word in tokens)
# Parse and compute the
expression
result = sympify(processed_query)
return result
except Exception as e:
return f"Error interpreting
the query: {str(e)}"
# Main loop for user input
if __name__ == "__main__":
print("Welcome to the Smart
Calculator!")
print("Type 'exit' to
quit.")
while True:
user_query = input("Enter
your math query: ")
if user_query.lower() == 'exit':
break
print("Result:",
evaluate_expression(user_query))
5. Key Components
• Tokenization: Breaks the query into smaller parts (tokens)
for easier processing.
• Word-to-Number Conversion: Converts numbers in word form (e.g., 'two') into
digits.
• SymPy: A symbolic mathematics library to parse and evaluate mathematical
expressions.
6. Testing
1. Run the script:
python smart_calculator.py
2. Enter natural language math queries like:
- 'What is two plus
three?'
- 'Multiply five by six.'
3. The program will return the computed result. Type 'exit' to quit.
7. Enhancements
• Add Support for Complex Operations: Include support for
trigonometry, logarithms, etc.
• Voice Input: Integrate a speech-to-text module to accept spoken math queries.
• Error Handling: Enhance error messages for invalid inputs.
8. Troubleshooting
• Incorrect Results: Ensure the query uses proper syntax and
supported operations.
• Module Not Found: Verify all required libraries are installed.
• Tokenization Issues: Ensure nltk corpus is downloaded correctly.
9. Conclusion
This project demonstrates how to use NLP to interpret natural language math queries. With added functionalities, it can evolve into a full-fledged voice-activated assistant or smart chatbot.