fawern is a Python library designed to assist developers with various AI-powered tools for code generation, analysis, refactoring, and more. It utilizes advanced models to provide a wide range of functionalities, from generating Python code based on prompts to analyzing and fixing code, generating documentation, and even converting code from other languages to Python.
- ChatPython: Generate Python code based on a given input prompt.
- CodeAnalyzer: Analyze, refactor, and optimize Python code.
- CodeFormatter: Format Python code according to PEP8 standards.
- ErrorLogAnalyzer: Log and analyze Python errors, providing suggestions for fixes.
- CodeReviewer: Review Python code and provide feedback on structure, clarity, and quality.
- DocumentationGenerator: Generate detailed docstrings and inline comments for Python code.
- ConvertToPython: Convert code from other programming languages to Python.
- CodeVisualizer: Generate visual representations such as flowcharts or class diagrams for Python code.
- BugFixer: Automatically identify and fix bugs in Python code.
- UnitTestGenerator: Generate unit tests for Python code to ensure correctness.
Please check the package on PyPi to get the latest version and more information about the package. https://pypi.org/project/fawern/
You can install Fawern using pip:
pip install fawernGenerate Python code based on a prompt:
from fawern import ChatPython
assistant = ChatPython()
prompt = "Create a snake game using Pygame in Python. The snake should move with the arrow keys, grow when it eats food, and the game should end if it collides with the walls or itself. Display the current score during gameplay and the final score when the game ends."
code = assistant.generate_code(prompt, write_code_to_file=True, run_code=True)
print(code)This will generate Python code for a snake game and write it to a file named related to the prompt. The code will be executed, and the output will be displayed.
Here's an example of how to use the CodeAnalyzer feature to analyze and refactor Python code:
from fawern import CodeAnalyzer
analyzer = CodeAnalyzer()
code = """
def process_data(data):
result = []
for i in range(len(data)):
if data[i] % 2 == 0:
result.append(data[i] * 2)
return result
data = [1, 2, 3, 4, 5]
print(process_data(data))
"""
# Analyze the code for potential improvements
analysis = analyzer.analyze_code(code)
# Analyze the python file for potential improvements
analysis = analyzer.analyze_code("path/to/python/file.py")
print("Analysis Report:\n", analysis)
# Fix detected issues and suggest improvements
fixed_code = analyzer.find_syntax_errors(code)
# Fix detected issues and suggest improvements for the python file
fixed_code = analyzer.find_syntax_errors("path/to/python/file.py")
print("\nFixed Code:\n", fixed_code)
# Suggest optimizations for the fixed code
optimizations = analyzer.suggest_optimizations(fixed_code)
# Suggest optimizations for the fixed python file
optimizations = analyzer.suggest_optimizations("path/to/fixed/python/file.py")
print("\nOptimized Code:\n", optimizations)This will analyze the given Python code, find syntax errors, and suggest optimizations to improve the code.
Format Python code according to PEP8 standards:
from fawern import CodeFormatter
formatter = CodeFormatter()
code = """
def calculateArea( length, width ) :
return length *width
a = 5
b = 10
print ( calculateArea (a, b ) )
"""
# Format the code
formatted_code = formatter.format_code(code)
# Format the python file
formatted_code = formatter.format_code("path/to/python/file.py")
print("Formatted Code:\n", formatted_code)This will format the given Python code according to PEP8 standards.
Log and analyze Python errors, providing suggestions for fixes:
from fawern import ErrorLogAnalyzer
error_analyzer = ErrorLogAnalyzer()
# Log an error from a web application
error_message = """
Traceback (most recent call last):
File "/app.py", line 22, in <module>
app.run()
File "/flask/app.py", line 940, in run
run_simple(host, port, self, **options)
TypeError: 'str' object is not callable
"""
# Analyze logged errors and get suggestions
analysis = error_analyzer.analyze_errors(error_message)
print("Error Analysis:\n", analysis)This will log an error message from a web application, analyze the logged errors, and provide suggestions for fixes.
Review Python code and provide feedback on structure, clarity, and quality:
from fawern import CodeReviewer
reviewer = CodeReviewer()
code = """
import requests
from bs4 import BeautifulSoup
def scrape_website(url):
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
titles = soup.find_all('h2')
for title in titles:
print(title.get_text())
"""
# Review the code
review = reviewer.review_code(code)
# Review the python file
review = reviewer.review_code("path/to/python/file.py")
print("Code Review:\n", review)This will review the given Python code and provide feedback on its structure, clarity, and quality.
Generate detailed docstrings and inline comments for Python code:
from fawern import DocumentationGenerator
doc_generator = DocumentationGenerator()
code = """
class Calculator:
def add(self, a, b):
return a + b
def subtract(self, a, b):
return a - b
def multiply(self, a, b):
return a * b
def divide(self, a, b):
if b == 0:
return 'Error: Division by zero'
return a / b
"""
# Generate docstrings and inline comments
documentation = doc_generator.generate_docstrings(code)
# Generate docstrings and inline comments for the python file
documentation = doc_generator.generate_docstrings("path/to/python/file.py")
print("Generated Documentation:\n", documentation)This will generate detailed docstrings and inline comments for the given Python code.
Convert code from other programming languages to Python:
from fawern import ConvertToPython
converter = ConvertToPython()
cpp_code = """
#include <iostream>
using namespace std;
int main() {
int a = 5, b = 10;
int sum = a + b;
cout << "Sum: " << sum << endl;
return 0;
}
"""
# Convert C++ code to Python
python_code = converter.convert_code(cpp_code)
# Convert code from a file
python_code = converter.convert_code("path/to/cpp/file.cpp")
print("Converted Python Code:\n", python_code)This will convert the given Java code to Python code.
Generate visual representations such as flowcharts or class diagrams for Python code:
from fawern import CodeVisualizer
visualizer = CodeVisualizer()
code = """
class Shape:
def __init__(self, color):
self.color = color
def area(self):
pass
class Rectangle(Shape):
def __init__(self, color, width, height):
super().__init__(color)
self.width = width
self.height = height
def area(self):
return self.width * self.height
class Circle(Shape):
def __init__(self, color, radius):
super().__init__(color)
self.radius = radius
def area(self):
return 3.14 * self.radius * self.radius
"""
# Visualize the class hierarchy
visualization = visualizer.visualize_code(code)
# Visualize the class hierarchy for the python file
visualization = visualizer.visualize_code("path/to/python/file.py")
print("Class Diagram:\n", visualization)This will generate a class diagram for
Automatically identify and fix bugs in Python code:
from fawern import BugFixer
bug_fixer = BugFixer()
code = """
def divide_numbers(a, b):
return a / b
print(divide_numbers(10, 0))
"""
# Fix bugs in the code
fixed_code = bug_fixer.fix_bugs(code)
# Fix bugs in the python file
fixed_code = bug_fixer.fix_bugs("path/to/python/file.py")
print("Fixed Code:\n", fixed_code)This will automatically identify and fix bugs in the given Python code.
Generate unit tests for Python code to ensure correctness:
from fawern import UnitTestGenerator
test_generator = UnitTestGenerator()
code = """
def multiply(a, b):
return a * b
"""
# Generate unit tests for the code
unit_tests = test_generator.generate_tests(code)
# Generate unit tests for the python file
unit_tests = test_generator.generate_tests("path/to/python/file.py")
print("Generated Unit Tests:\n", unit_tests)This will generate unit tests for the given Python code to ensure its correctness.
Contributions are welcome! But fawern is not public yet. We will make it public soon.
This project is licensed under the MIT License - see the LICENSE file for details.
- This project was inspired by the need for AI-powered tools to assist developers in various aspects of software development.