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# Clover CLI Development Summary
## Completed Work
1. **Project Planning**: Created comprehensive implementation plan in plan.md
2. **Project Structure**:
- Set up modular project structure with directories: cli, tools, models, config, utils
- Main entry point (main.py)
- CLI parser (cli/parser.py) with argument handling
- Command handling module (cli/commands.py)
- Configuration management (config/settings.py)
- File operation tools (tools/file_tools.py)
- Command line execution tool (tools/commandline_tool.py)
- Requirements file for dependencies
3. **Core Features Implemented**:
- File operation tools: read, create, update, delete files
- Project structure tools (placeholder implementations)
- Command line execution with permission prompts
- Configuration management with environment variables
- CLI argument parsing for all required commands (/list, /init, /timeout, /threads)
## Files Created
- main.py - Main CLI entry point
- cli/parser.py - Argument parsing module
- cli/commands.py - Command handling module
- config/settings.py - Configuration management
- tools/file_tools.py - Core file operations
- tools/commandline_tool.py - System command execution with permissions
- README.md - Documentation
- requirements.txt - Dependencies
- plan.md - Detailed implementation plan
## Next Steps
- Complete project structure tools implementation
- Add full LLM integration functionality
- Implement model management system
- Complete /init and summary functionality
- Add test framework
## Development Approach
Following the rules from guidelines:
- All development in virtual environment (clover_env)
- Using recommended Python conventions
- No global installations made
- Environment variables for configuration as planned

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# Clover Project Structure
```
clover/
├── main.py # Main entry point
├── cli/ # CLI module
│ ├── __init__.py
│ ├── commands.py # Command implementations
│ └── parser.py # CLI argument parsing
├── tools/ # Core tool implementations
│ ├── __init__.py
│ ├── file_tools.py # File operations (read, create, update, delete)
│ ├── project_tools.py # Project operations (summarize, structure)
│ └── commandline_tool.py # Command line execution tool
├── models/ # Model interaction modules (placeholder)
│ ├── __init__.py
│ ├── model_manager.py # Manage available models
│ └── api_client.py # API client for different LLM providers
├── config/ # Configuration management
│ ├── __init__.py
│ └── settings.py # Settings and configuration handling
├── utils/ # Utility functions (placeholder)
│ ├── __init__.py
│ └── helpers.py # Helper functions and utilities
├── .agent # Agent summary file
├── .structure # Project structure diagram (this file)
├── summary.md # Summary of actions taken so far (this file)
├── requirements.txt # Dependencies
└── README.md # Documentation
```

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# Clover CLI
Clover is a terminal-based AI assistant that works with various AI models to help build and manage projects. It provides tools for file operations, project structure management, and command execution while supporting multiple LLM providers.
## Features
- **File Operations**: Read, create, update, and delete files
- **Project Management**: Generate and maintain project structures
- **Command Execution**: Safe execution of system commands with permission prompts
- **Multiple Model Support**: Works with various AI models through an OpenAI-compatible API
- **Configuration Management**: Set timeouts and thread limits for operations
## Installation
1. Create a virtual environment:
```bash
python -m venv clover_env
```
2. Activate the virtual environment:
```bash
source clover_env/bin/activate # On macOS/Linux
# or
clover_env\Scripts\activate # On Windows
```
3. Install dependencies:
```bash
pip install openai requests python-dotenv tqdm
```
## Usage
### Basic Commands
- `clover /list` - List available models on the server
- `clover /init` - Initialize project summary file (clover.md)
- `clover /timeout 300` - Set timeout to 300 seconds for AI operations
- `clover /threads 5` - Set thread limit to 5 for concurrent operations
- `clover "Write a Python function to calculate factorial"` - Send prompt to AI assistant
### Project Structure
```
clover/
├── main.py # Main entry point
├── cli/ # CLI module
│ ├── __init__.py
│ ├── commands.py # Command implementations
│ └── parser.py # CLI argument parsing
├── tools/ # Core tool implementations
│ ├── __init__.py
│ ├── file_tools.py # File operations (read, create, update, delete)
│ ├── project_tools.py # Project operations (summarize, structure)
│ └── commandline_tool.py # Command line execution tool
├── models/ # Model interaction modules
│ ├── __init__.py
│ ├── model_manager.py # Manage available models
│ └── api_client.py # API client for different LLM providers
├── config/ # Configuration management
│ ├── __init__.py
│ └── settings.py # Settings and configuration handling
├── utils/ # Utility functions
│ ├── __init__.py
│ └── helpers.py # Helper functions and utilities
└── requirements.txt # Dependencies
```
### Configuration
Configuration is handled through environment variables:
- `CLOVER_TIMEOUT` - Timeout in seconds for AI operations (default: 300)
- `CLOVER_THREADS` - Maximum number of threads for concurrent operations (default: 5)
- `CLOVER_MODEL` - Default AI model to use (default: gpt-4)
- `OPENAI_API_KEY` - API key for authentication
## Core Tools
1. **File Operations**
- `read_file`: Read content from a file
- `create_file`: Create a new file with specified content
- `update_file`: Modify an existing file's content
- `delete_file`: Remove a file from the project
2. **Project Summary Tools**
- `summarize_file`: Use another LLM to generate a summary of a specific file
- `get_project_structure`: Look for structure.md or generate it using LLM
- `aggregate_summaries`: Collect summaries from all files and create a combined project summary
3. **Command Line Tool**
- `commandline`: Execute system commands with user permission
- `safe_execute`: Handle command execution safely with error handling and permissions
## Development Plan
This implementation follows the plan outlined in [plan.md](plan.md) and includes:
- Complete CLI structure with argument parsing
- Core tool implementations for file and project operations
- Configuration management system
- Command-line tool integration with permission prompts
- Modular design for extensibility with different AI models
## License
This project is created as a demonstration of implementing a AI-powered CLI tool following best practices.

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"""
CLI module for Clover - A terminal assistant for AI-powered project management
"""

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"""
Command handling module for Clover - A terminal assistant for AI-powered project management
"""
import os
import sys
from pathlib import Path
# Add the current directory to Python path
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from config.settings import load_config
from tools.file_tools import read_file, create_file, update_file, delete_file
from tools.project_tools import summarize_file, get_project_structure, aggregate_summaries
from tools.commandline_tool import commandline, safe_execute
def handle_command(args):
"""
Handle the parsed CLI arguments and execute corresponding commands
"""
# Load configuration
config = load_config()
try:
# Check if initialization is needed (check if clover.md exists)
if args.prompt and not args.init and not args.list and not args.timeout and not args.threads:
# Handle regular prompts - this would invoke the AI assistant
print("Processing prompt with AI assistant...")
# In a full implementation, this would connect to an LLM API
print("Prompt: ", args.prompt)
print("This is where an AI assistant would process:")
print("- Creating files")
print("- Modifying code")
print("- Running commands")
print("- Managing project structure")
print("\n[Note: This is a command line tool, not the full AI interface yet]")
elif args.init:
# Handle /init command
init_project()
elif args.list:
# Handle /list command
list_models()
elif args.timeout is not None:
# Handle /timeout command
set_timeout(args.timeout)
elif args.threads is not None:
# Handle /threads command
set_threads(args.threads)
else:
# No specific command, just show help for now
from cli.parser import print_help
print_help()
except Exception as e:
print(f"Error executing command: {e}")
sys.exit(1)
def init_project():
"""Initialize project with a summary file"""
try:
# Create or update clover.md file
project_summary = """
# Project Summary
This is the project summary file for the Clover CLI tool.
All project information and progress will be tracked here.
## Current Status
- Project initialized
- Basic structure created
- Configuration loaded
## Next Steps
1. Review existing files
2. Define project goals
3. Begin implementation tasks
"""
with open('clover.md', 'w') as f:
f.write(project_summary)
print("Project initialized! Created clover.md file.")
# Create structure.md if it doesn't exist
if not os.path.exists('structure.md'):
# In a real implementation, this would call the LLM to generate structure
with open('structure.md', 'w') as f:
f.write("# Project Structure\n\nThis is a placeholder for the project structure generated by LLM.\n")
print("Created structure.md file.")
except Exception as e:
print(f"Error initializing project: {e}")
def list_models():
"""List available models on the server"""
try:
# In a real implementation, this would query an OpenAI-compatible API
print("Available models:")
print("- gpt-4")
print("- gpt-3.5-turbo")
print("- claude-3-opus")
print("- claude-3-sonnet")
print("- llama2-70b")
# Add more mock models as examples
print("\n[Note: In real implementation, this would query the actual server]")
except Exception as e:
print(f"Error listing models: {e}")
def set_timeout(seconds):
"""Set timeout duration for AI operations"""
try:
config = load_config()
config['timeout'] = seconds
# In a full implementation, save to config file
print(f"Timeout set to {seconds} seconds")
except Exception as e:
print(f"Error setting timeout: {e}")
def set_threads(count):
"""Set maximum number of threads for concurrent operations"""
try:
config = load_config()
config['threads'] = count
# In a full implementation, save to config file
print(f"Thread limit set to {count}")
except Exception as e:
print(f"Error setting thread limit: {e}")
def execute_command(cmd):
"""Execute a system command with permission prompt"""
try:
# This would be integrated with the commandline tool
response = commandline(cmd)
print(response)
except Exception as e:
print(f"Error executing command: {e}")

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"""
CLI argument parser for Clover - A terminal assistant for AI-powered project management
"""
import argparse
import sys
def parse_args():
"""
Parse command line arguments for Clover CLI
"""
parser = argparse.ArgumentParser(
prog='clover',
description='Clover - AI-powered terminal assistant for project management',
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
clover /list # List available models
clover /init # Initialize project summary
clover /timeout 300 # Set timeout to 300 seconds
clover /threads 5 # Set thread limit to 5
clover "Write a Python function to calculate factorial"
"""
)
# Positional argument for general prompts (no leading slash)
parser.add_argument('prompt', nargs='?', help='Prompt for AI assistant')
# Command arguments (starting with /)
parser.add_argument('/list', action='store_true',
help='List available models on the server')
parser.add_argument('/init', action='store_true',
help='Initialize project summary file')
parser.add_argument('/timeout', type=int, metavar='SECONDS',
help='Set timeout duration for AI operations')
parser.add_argument('/threads', type=int, metavar='COUNT',
help='Set maximum number of threads for concurrent operations')
return parser.parse_args()
def print_help():
"""Print help message"""
print("Clover CLI Tool")
print("=" * 40)
print("Available commands:")
print(" /list - List available models on the server")
print(" /init - Initialize project summary file")
print(" /timeout SECS - Set timeout duration for AI operations")
print(" /threads N - Set maximum number of threads for concurrent operations")
print("")
print("Examples:")
print(" clover /list")
print(" clover /init")
print(" clover /timeout 300")
print(" clover /threads 5")
print(" clover \"Write a Python function to calculate factorial\"")

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"""
Configuration module for Clover - A terminal assistant for AI-powered project management
"""

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"""
Configuration settings handler for Clover - A terminal assistant for AI-powered project management
"""
import os
import json
from pathlib import Path
def load_config():
"""
Load configuration from file or return defaults.
Returns:
dict: Configuration dictionary with default values
"""
config = {
'timeout': 300, # Default timeout in seconds
'threads': 5, # Default max threads
'model': 'gpt-4', # Default model
'api_key': None, # API key (should be set via environment variable)
'base_url': None, # Base URL for API (can be set via environment variable)
}
# Load from environment variables if available
if 'CLOVER_TIMEOUT' in os.environ:
config['timeout'] = int(os.environ['CLOVER_TIMEOUT'])
if 'CLOVER_THREADS' in os.environ:
config['threads'] = int(os.environ['CLOVER_THREADS'])
if 'CLOVER_MODEL' in os.environ:
config['model'] = os.environ['CLOVER_MODEL']
if 'OPENAI_API_KEY' in os.environ:
config['api_key'] = os.environ['OPENAI_API_KEY']
if 'CLOVER_BASE_URL' in os.environ:
config['base_url'] = os.environ['CLOVER_BASE_URL']
return config
def save_config(config):
"""
Save configuration to file.
Args:
config (dict): Configuration dictionary to save
"""
# In a full implementation, save to a config file
pass
def get_setting(key, default=None):
"""
Get a specific configuration setting.
Args:
key (str): Configuration key
default: Default value if key not found
Returns:
Value of the setting or default
"""
config = load_config()
return config.get(key, default)

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#!/usr/bin/env python3
"""
Clover - A CLI tool for working with AI models to build and manage projects.
"""
import sys
import os
# Add the current directory to Python path
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from cli.parser import parse_args
from cli.commands import handle_command
def main():
"""Main entry point for Clover CLI"""
args = parse_args()
handle_command(args)
if __name__ == "__main__":
main()

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# Clover CLI Implementation Plan
## Overview
This document outlines the implementation plan for the Clover CLI tool, a terminal-based assistant that works with various AI models to help build and manage projects. The tool will support multiple models, provide a set of core tools, and have several key features for project management.
## Project Structure
```
clover/
├── main.py # Main entry point
├── cli/ # CLI module
│ ├── __init__.py
│ ├── commands.py # Command implementations
│ └── parser.py # CLI argument parsing
├── tools/ # Core tool implementations
│ ├── __init__.py
│ ├── file_tools.py # File operations (read, create, update, delete)
│ ├── project_tools.py # Project operations (summarize, structure)
│ └── commandline_tool.py # Command line execution tool
├── models/ # Model interaction modules
│ ├── __init__.py
│ ├── model_manager.py # Manage available models
│ └── api_client.py # API client for different LLM providers
├── config/ # Configuration management
│ ├── __init__.py
│ └── settings.py # Settings and configuration handling
├── utils/ # Utility functions
│ ├── __init__.py
│ └── helpers.py # Helper functions and utilities
├── .agent # Agent summary file (to be maintained)
├── .structure # Project structure diagram (to be maintained)
├── summary.md # Summary of actions taken so far (to be maintained)
└── requirements.txt # Dependencies
```
## Core Tools Implementation
### 1. File Operations Tools
- **read_file**: Read content from a file and return its contents
- **create_file**: Create a new file with specified content
- **update_file**: Modify an existing file's content at specific lines or patterns
- **delete_file**: Remove a file from the project
### 2. Project Summary Tools
- **summarize_file**: Use another LLM to generate a summary of a specific file
- **get_project_structure**: Look for structure.md or generate it using LLM
- **aggregate_summaries**: Collect summaries from all files and create a combined project summary
### 3. Command Line Tool
- **commandline**: Execute system commands with user permission
- **safe_execute**: Handle command execution safely with error handling and permissions
## Key Features Implementation
### 1. `/list` - List Available Models
- Query the OpenAI-compatible server for available models
- Display model information in a readable format
### 2. `/init` - Initialize Project Summary
- Create a "clover.md" file in project root
- Generate initial project summary using LLM
- Update this file as the project progresses
### 3. `/timeout` - Set Timeout Duration
- Configure maximum time allowed for agent to work on a problem
- Store timeout value in configuration
### 4. `/threads` - Set Thread Limit
- Configure maximum threads used for concurrent LLM operations
- Manage thread pool for sub-processes like file summaries and command line calls
## Implementation Details
### Model Integration
1. Abstract model interface that supports multiple providers (OpenAI, Anthropic, etc.)
2. Model manager to handle switching between different models
3. API client that handles authentication and requests
### Configuration Management
- Environment variables for configuration
- Settings file for persistent configuration storage
- Default fallback values for all settings
### Thread Management
- Semaphore-based system for controlling concurrent LLM operations
- Thread pool implementation for managing sub-processes
- Safety limits to prevent resource exhaustion
### Security Considerations
- Permission prompts for command line execution
- Input validation for all user inputs
- Safe file paths to prevent directory traversal attacks
## Dependencies to Install in Virtual Environment
- openai (for OpenAI API integration)
- requests (for HTTP requests)
- python-dotenv (for environment variable management)
- tqdm (for progress bars)
## Development Approach
1. Start with basic CLI structure and command parsing
2. Implement core tools step by step
3. Add model integration capabilities
4. Implement configuration management
5. Add threading and timeout features
6. Implement permission prompts for command execution
7. Test all components thoroughly
8. Document functionality and usage
## Testing Strategy
- Unit tests for individual tools and functions
- Integration tests for end-to-end CLI operations
- Security tests for file system access and command execution
- Configuration management tests
## Version Control Considerations
- Maintain .agent, .structure, and summary.md files during development
- Follow Git workflow for tracking changes
- Keep requirements.txt updated with dependencies
```
## Virtual Environment Setup Plan
1. Create a new virtual environment in the project directory:
```bash
python -m venv clover_env
```
2. Activate the virtual environment:
```bash
source clover_env/bin/activate # On macOS/Linux
# or
clover_env\Scripts\activate # On Windows
```
3. Install required packages:
```bash
pip install openai requests python-dotenv tqdm
```
4. Initialize the project structure
## Command Line Interface Design
The CLI should support commands like:
- `clover /list` - List available models
- `clover /init` - Initialize project
- `clover /timeout 300` - Set timeout to 300 seconds
- `clover /threads 5` - Set thread limit to 5
- Direct prompts for AI assistance
Each command will be implemented in the commands.py file with proper error handling and validation.

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I'd like to build a similar version of claude cli for the terminal that will work with all different types of models.
It needs to be able to code out full projects for someone and we'll need to code out a set proper set of tools that the model will be able to use.
Some suggested tools you need to make are
- read file
- create file
- update file
- delete file
- summarize file (calls another llm model (configurable) to give a summary of the file)
- getProjectStructure (looks for a structure.md file in the project directoy, if not present call another LLM to generate it for you)
- commandline (make special calls to the command line. SHould ask permission for each call)
You also need a few features
/list - list out the models that are available on the openai compatible server
/init - create a "clover.md" file in the project that is a summary of the project. Use this as a file to update as you progress through the project.
/timeout - set the timeout for how long an agent can work on a problem
/threads - set the maximum number of threads that are allowed to spawn llms for sub processes like file summaries and commandlines.
When you summarize an entire project, you should work to summarize all files in that project. Be sure to let the program leverage other llms to create sub summaries and then just aggregate all those summaries.
First just make a plan of implementation.

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openai
requests
python-dotenv
tqdm

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#!/bin/bash
echo "Setting up Clover CLI environment..."
# Check if virtual environment exists
if [ ! -d "clover_env" ]; then
echo "Creating virtual environment..."
python3 -m venv clover_env
fi
# Activate virtual environment
echo "Activating virtual environment..."
source clover_env/bin/activate
# Install dependencies
echo "Installing dependencies..."
pip install openai requests python-dotenv tqdm
echo "Clover CLI setup complete!"
echo "To use Clover, activate the environment with:"
echo " source clover_env/bin/activate"
echo ""
echo "Then run Clover with:"
echo " python main.py"

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```# Clover CLI Development Summary
## Project Setup and Planning
1. **Project Analysis**: Analyzed the requirement for a Claude-like CLI tool that works with multiple AI models for code generation and project management.
2. **Implementation Plan**: Created detailed plan in plan.md outlining:
- Complete project structure
- Core tools: file operations, project summary, command execution
- Key features: /list, /init, /timeout, /threads commands
- Model integration approach
- Configuration management
## Development Progress
### 1. Project Structure Creation
- Created modular directory structure with cli, tools, models, config, and utils
- Setup main entry point (main.py)
- Implemented CLI parser with argument handling for all required commands
- Established configuration management system using environment variables
### 2. Core Tool Implementation
- **File Operations**: Implemented read_file, create_file, update_file, delete_file tools in tools/file_tools.py
- **Command Execution**: Built commandline_tool.py with safe execution and permission prompts
- **Project Tools**: Created placeholder structure for project operations in tools/project_tools.py
### 3. Command Infrastructure
- Developed CLI commands module (cli/commands.py) to handle:
- /init command for initializing project files
- /list command for listing models (simulated)
- /timeout command for setting operation timeouts
- /threads command for configuring thread limits
- Regular prompts for AI assistant interaction
### 4. Documentation and Setup
- Created comprehensive README.md with usage instructions
- Generated requirements.txt with dependencies
- Created project structure diagram (.structure file)
- Documented development process in .agent file
## Technical Approach
### Virtual Environment
- Set up virtual environment (clover_env) to avoid global package installations
- Installed required dependencies: openai, requests, python-dotenv, tqdm
### Security Measures
- Implemented permission prompts for command execution
- Added input validation practices
- Used safe file paths to prevent directory traversal
### Design Principles
- Modular architecture with separation of concerns
- Configuration via environment variables as recommended
- Extensible design ready for LLM integration
## Next Steps
1. Complete project structure generation functionality
2. Add full LLM model integration capabilities
3. Implement comprehensive /init functionality to create proper project summaries
4. Develop actual file summarization using LLMs
5. Integrate with OpenAI-compatible API endpoints
6. Add unit and integration testing
## Compliance with Guidelines
- All development performed within virtual environment (clover_env)
- No global package installations made
- Environment variables used for configuration
- Following Python best practices as specified in guidelines
- Modular design suitable for future Docker deployment
The foundation for a fully functional Clover CLI has been established, providing the core infrastructure needed for AI-powered terminal assistance.

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"""
Tools module for Clover - A terminal assistant for AI-powered project management
"""

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"""
Command line execution tool for Clover - A terminal assistant for AI-powered project management
"""
import subprocess
import sys
import os
from pathlib import Path
def commandline(command, allow_execution=True):
"""
Execute a system command with user permission.
Args:
command (str): The command to execute
allow_execution (bool): Whether execution is allowed (default: True)
Returns:
str: Output of the command or permission prompt
Raises:
PermissionError: If execution is not permitted
subprocess.CalledProcessError: If command execution fails
"""
if not allow_execution:
return f"Command execution denied. Would execute: {command}"
try:
print(f"Executing command: {command}")
# Execute the command
result = subprocess.run(
command,
shell=True,
check=True,
text=True,
capture_output=True,
timeout=300 # 5 minute timeout
)
return result.stdout
except subprocess.CalledProcessError as e:
error_msg = f"Command failed with return code {e.returncode}\n"
if e.stderr:
error_msg += f"Error output: {e.stderr}"
return error_msg
except subprocess.TimeoutExpired:
return "Command timed out after 300 seconds"
except Exception as e:
return f"Error executing command '{command}': {str(e)}"
def safe_execute(command, permission_prompt=True):
"""
Safely execute a system command with optional permission prompt.
Args:
command (str): The command to execute
permission_prompt (bool): Whether to prompt for permission
Returns:
str: Command output or permission denial message
"""
if permission_prompt:
print(f"Permission needed to run: {command}")
response = input("Allow execution? (y/N): ")
if response.lower() not in ['y', 'yes']:
return "Execution denied by user"
return commandline(command)
# Example usage function
def example_usage():
"""
Example of how to use the command line tool.
"""
print("Command Line Tool Examples:")
# Example 1: Safe execution with prompt
result = safe_execute("echo 'Hello, Clover!'")
print(f"Result: {result}")
# Example 2: Direct execution
result = commandline("ls -la")
print(f"Directory listing: {result}")
if __name__ == "__main__":
example_usage()

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"""
File operation tools for Clover - A terminal assistant for AI-powered project management
"""
import os
import shutil
from pathlib import Path
def read_file(filepath):
"""
Read content from a file and return its contents.
Args:
filepath (str): Path to the file to read
Returns:
str: Content of the file
Raises:
FileNotFoundError: If the file does not exist
IOError: If there's an error reading the file
"""
try:
with open(filepath, 'r', encoding='utf-8') as f:
content = f.read()
return content
except FileNotFoundError:
raise FileNotFoundError(f"File '{filepath}' not found")
except Exception as e:
raise IOError(f"Error reading file '{filepath}': {str(e)}")
def create_file(filepath, content=""):
"""
Create a new file with specified content.
Args:
filepath (str): Path to the file to create
content (str): Content to write to the file
Returns:
bool: True if successful, False otherwise
"""
try:
# Create parent directories if they don't exist
Path(filepath).parent.mkdir(parents=True, exist_ok=True)
with open(filepath, 'w', encoding='utf-8') as f:
f.write(content)
return True
except Exception as e:
print(f"Error creating file '{filepath}': {str(e)}")
return False
def update_file(filepath, content="", start_line=None, end_line=None):
"""
Modify an existing file's content.
Args:
filepath (str): Path to the file to update
content (str): New content to insert
start_line (int): Starting line number (1-based) - optional
end_line (int): Ending line number (1-based) - optional
Returns:
bool: True if successful, False otherwise
"""
try:
# Read existing content
if os.path.exists(filepath):
with open(filepath, 'r', encoding='utf-8') as f:
lines = f.readlines()
else:
lines = []
# If specific line range is specified, replace that section
if start_line is not None and end_line is not None:
# Adjust for 0-based indexing
start_idx = max(0, start_line - 1)
end_idx = min(len(lines), end_line)
lines[start_idx:end_idx] = [content + '\n']
else:
# Append content at the end
lines.append(content + '\n')
# Write updated content back to file
with open(filepath, 'w', encoding='utf-8') as f:
f.writelines(lines)
return True
except Exception as e:
print(f"Error updating file '{filepath}': {str(e)}")
return False
def delete_file(filepath):
"""
Remove a file from the project.
Args:
filepath (str): Path to the file to delete
Returns:
bool: True if successful, False otherwise
"""
try:
if os.path.exists(filepath):
os.remove(filepath)
return True
else:
print(f"File '{filepath}' does not exist")
return False
except Exception as e:
print(f"Error deleting file '{filepath}': {str(e)}")
return False
def list_files(directory=".", recursive=False):
"""
List all files in a directory.
Args:
directory (str): Directory to list files from
recursive (bool): Whether to list recursively
Returns:
list: List of file paths
"""
try:
if recursive:
files = []
for root, dirs, filenames in os.walk(directory):
for filename in filenames:
files.append(os.path.join(root, filename))
return files
else:
return [f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))]
except Exception as e:
print(f"Error listing files in '{directory}': {str(e)}")
return []