diff --git a/README.md b/README.md index 1ca8faf..cdac703 100644 --- a/README.md +++ b/README.md @@ -8,40 +8,59 @@ --- -**Open Codex** is a fully open-source command-line AI assistant inspired by OpenAI Codex, supporting local language models like `phi-4-mini`. +**Open Codex** is a fully open-source command-line AI assistant inspired by OpenAI Codex, supporting local language models like `phi-4-mini` and **full integration with Ollama**. -No API key is required. Everything runs locally. +🧠 **Runs 100% locally** – no OpenAI API key required. Everything works offline. -Supports: -- **One-shot mode**: `open-codex "list all folders"` -> returns shell command -- 🧠 Local-only execution using supported OS models (currently `phi-4-mini`) +--- + +## Supports + +* **One-shot mode**: `open-codex "list all folders"` -> returns shell command +* **Ollama integration** for (e.g., LLaMA3, Mistral) +* Native execution on **macOS, Linux, and Windows** +* qwen1.5-7b-chat (auth required, enhanced for coding tasks) --- ## ✨ Features -- Natural Language to Shell Command (via local models) -- Works on macOS, Linux, and Windows (Python-based) -- Confirmation before execution -- Add to clipboard / abort / execute prompt -- One-shot interaction mode (interactive and function-calling coming soon) +- Natural Language → Shell Command (via local or Ollama-hosted LLMs) +- Local-only execution: no data sent to the cloud +- Confirmation before running any command +- Option to copy to clipboard / abort / execute - Colored terminal output for better readability +- Ollama support: use advanced LLMs with `--ollama --model llama3` +- Smart command validation and error handling +- Real-time command output streaming + +### 🔍 Example with Ollama: + +```bash +open-codex --ollama --model llama3 "find all JPEGs larger than 10MB" +``` + +Codex will: + +1. Send your prompt to the Ollama API (local server, e.g. on `localhost:11434`) +2. Return a shell command suggestion (e.g., `find . -name "*.jpg" -size +10M`) +3. Prompt you to execute, copy, or abort + +> 🛠️ You must have [Ollama](https://ollama.com) installed and running locally to use this feature. --- ## 🧱 Future Plans -- Interactive, context aware mode +- Interactive, context-aware mode - Fancy TUI with `textual` or `rich` -- Add support for additional OSS Models - Full interactive chat mode - Function-calling support -- Voice input via Whisper -- Command history and undo +- Whisper-based voice input +- Command history & undo - Plugin system for workflows --- - ## 📦 Installation @@ -53,13 +72,13 @@ brew install open-codex ``` -### 🔹 Option 2: Install via pipx (cross-platform) +### 🔹 Option 2: Install via pipx (Cross-platform) ```bash pipx install open-codex ``` -### 🔹 Option 3: Clone & Install locally +### 🔹 Option 3: Clone & install locally ```bash git clone https://github.com/codingmoh/open-codex.git @@ -67,28 +86,50 @@ cd open_codex pip install . ``` - -Once installed, you can use the `open-codex` CLI globally. +Once installed, use the `open-codex` CLI globally. --- -## 🚀 Usage +## 🚀 Usage Examples ### One-shot mode +Basic usage with default model (phi-4-mini): ```bash -open-codex "untar file abc.tar" +open-codex "list all python files" ``` +Using Qwen model for enhanced coding tasks: +```bash +open-codex "list all python files" +``` + +Using Qwen model for enhanced coding tasks: +```bash +# First, set your Hugging Face token +export HUGGINGFACE_TOKEN=your_token_here + +# Then use the Qwen model +open-codex --model qwen-2.5-coder "find python files modified today" + +# Or provide token directly +open-codex --model qwen-2.5-coder --hf-token your_token_here "your command" +``` ✅ Codex suggests a shell command ✅ Asks for confirmation / add to clipboard / abort ✅ Executes if approved +### ▶️ Using Ollama + +```bash +open-codex --ollama --model llama3 "delete all .DS_Store files recursively" +``` + --- ## 🛡️ Security Notice -All models run locally. Commands are only executed after explicit approval. +All models run **locally**. Commands are executed **only after your explicit confirmation**. --- @@ -105,4 +146,3 @@ MIT --- ❤️ Built with love and caffeine by [codingmoh](https://github.com/codingmoh). - diff --git a/pyproject.toml b/pyproject.toml index 08cb7e6..202000b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,6 +7,7 @@ requires-python = ">=3.11" dependencies = [ "huggingface-hub>=0.30.2", "llama-cpp-python>=0.3.8", + "ollama>=0.4.8", "prompt_toolkit", "pyinstaller>=6.13.0", "pyperclip>=1.9.0", diff --git a/src/open_codex/agent_builder.py b/src/open_codex/agent_builder.py index 0e35aac..dada460 100644 --- a/src/open_codex/agent_builder.py +++ b/src/open_codex/agent_builder.py @@ -1,15 +1,32 @@ from importlib.resources import files +from typing import Literal, Optional -from open_codex.agents.phi_4_mini import AgentPhi4Mini from open_codex.interfaces.llm_agent import LLMAgent +ModelType = Literal["phi-4-mini", "qwen-2.5-coder"] + class AgentBuilder: + + @staticmethod + def get_system_prompt() -> str: + return files("open_codex.resources") \ + .joinpath("prompt.txt") \ + .read_text(encoding="utf-8") @staticmethod - def get_agent() -> LLMAgent: - system_prompt = files("open_codex.resources").joinpath("prompt.txt").read_text(encoding="utf-8") - return AgentPhi4Mini(system_prompt=system_prompt) + def get_phi_agent() -> LLMAgent: + from open_codex.agents.phi_4_mini_agent import Phi4MiniAgent + system_prompt = AgentBuilder.get_system_prompt() + return Phi4MiniAgent(system_prompt=system_prompt) + @staticmethod + def get_ollama_agent(model: str, host: str) -> LLMAgent: + from open_codex.agents.ollama_agent import OllamaAgent + system_prompt = AgentBuilder.get_system_prompt() + return OllamaAgent(system_prompt=system_prompt, + model_name=model, + host=host) + @staticmethod def read_file(file_path: str) -> str: with open(file_path, 'r') as file: diff --git a/src/open_codex/agents/ollama_agent.py b/src/open_codex/agents/ollama_agent.py new file mode 100644 index 0000000..e6737c0 --- /dev/null +++ b/src/open_codex/agents/ollama_agent.py @@ -0,0 +1,110 @@ +from typing import List, Dict +import logging +import ollama + +from open_codex.interfaces.llm_agent import LLMAgent + +# Configure logger +logger = logging.getLogger(__name__) + +class OllamaAgent(LLMAgent): + """ + Agent that connects to Ollama to access local language models + using the official Python client. + """ + + def __init__(self, + system_prompt: str, + model_name: str, + host: str, + temperature: float = 0.2, + max_tokens: int = 500): + """ + Initialize the Ollama agent. + + Args: + system_prompt: The system prompt to use for generating responses + model_name: The name of the model to use (default: "llama3") + host: The host URL of the Ollama API (default: None, uses OLLAMA_HOST env var or http://localhost:11434) + temperature: The temperature to use for generation (default: 0.2) + max_tokens: The maximum number of tokens to generate (default: 500) + """ + self.system_prompt = system_prompt + self.model_name = model_name + self.host = host + + self.temperature = temperature + self.max_tokens = max_tokens + self._ollama_client = ollama.Client(host=self.host) + + def _check_ollama_available(self) -> None: + """Check if Ollama server is available and the model exists.""" + try: + # List models to check connection + models: ollama.ListResponse = self._ollama_client.list() + + available_models = [model.model for model in models.models if model.model is not None] + + if not available_models: + logger.error(f"No models found in Ollama. You may need to pull the model with: ollama pull {self.model_name}") + elif self.model_name not in available_models: + logger.error(f"Model '{self.model_name}' not found in Ollama. Available models: {', '.join(available_models)}") + logger.error(f"You can pull the model with: ollama pull {self.model_name}") + + except ConnectionError as e: + logger.error(f"Could not connect to Ollama server.") + logger.error( + f"Make sure Ollama is running at {self.host} or install it from https://ollama.com" + ) + raise ConnectionError( + f"Could not connect to Ollama server. " + f"Make sure Ollama is running at {self.host} or install it from https://ollama.com" + ) + + def one_shot_mode(self, user_input: str) -> str: + """ + Generate a one-shot response to the user input. + + Args: + user_input: The user's input prompt + + Returns: + The generated response as a string + """ + self._check_ollama_available() + messages = [ + {"role": "system", "content": self.system_prompt}, + {"role": "user", "content": user_input} + ] + + response = self._generate_completion(messages) + return response.strip() + + def _generate_completion(self, messages: List[Dict[str, str]]) -> str: + """ + Generate a completion using the Ollama API. + + Args: + messages: The conversation history as a list of message dictionaries + + Returns: + The generated text response + """ + try: + + response = ollama.chat( + model=self.model_name, + messages=messages, + options={ + "temperature": self.temperature, + "num_predict": self.max_tokens, + } + ) + + if "message" in response and "content" in response["message"]: + return response["message"]["content"] + else: + raise ValueError(f"Unexpected response format from Ollama API: {response}") + + except Exception as e: + raise ConnectionError(f"Error communicating with Ollama: {str(e)}") \ No newline at end of file diff --git a/src/open_codex/agents/phi_4_mini.py b/src/open_codex/agents/phi_4_mini_agent.py similarity index 75% rename from src/open_codex/agents/phi_4_mini.py rename to src/open_codex/agents/phi_4_mini_agent.py index 376ab14..92487d6 100644 --- a/src/open_codex/agents/phi_4_mini.py +++ b/src/open_codex/agents/phi_4_mini_agent.py @@ -1,35 +1,17 @@ import contextlib import os import time +import logging from typing import List, cast from huggingface_hub import hf_hub_download # type: ignore from llama_cpp import CreateCompletionResponse, Llama from open_codex.interfaces.llm_agent import LLMAgent -class AgentPhi4Mini(LLMAgent): - - def download_model(self, model_filename: str, - repo_id: str, - local_dir: str) -> str: - print( - "\n🤖 Thank you for using Open Codex!\n" - "📦 For the first run, we need to download the model from Hugging Face.\n" - "⏬ This only happens once – it’ll be cached locally for future use.\n" - "🔄 Sit tight, the download will begin now...\n" - ) - print("\n⏬ Downloading model phi4-mini ...") - - start = time.time() - model_path:str = hf_hub_download( - repo_id=repo_id, - filename=model_filename, - local_dir=local_dir, - ) - end = time.time() - print(f"✅ Model downloaded in {end - start:.2f}s\n") - return model_path +# Configure logger +logger = logging.getLogger(__name__) +class Phi4MiniAgent(LLMAgent): def __init__(self, system_prompt: str): model_filename = "Phi-4-mini-instruct-Q3_K_L.gguf" repo_id = "lmstudio-community/Phi-4-mini-instruct-GGUF" @@ -41,6 +23,7 @@ def __init__(self, system_prompt: str): # download the model model_path = self.download_model(model_filename, repo_id, local_dir) else: + logger.info("Model already downloaded, loading model...") print(f"We are locking and loading the model for you...\n") # suppress the stderr output from llama_cpp @@ -48,26 +31,55 @@ def __init__(self, system_prompt: str): # which prints a lot of warnings and errors to stderr # when loading the model # this is a temporary solution until the library is fixed - with AgentPhi4Mini.suppress_native_stderr(): + with Phi4MiniAgent.suppress_native_stderr(): lib_dir = os.path.join(os.path.dirname(__file__), "llama_cpp", "lib") + logger.debug(f"Loading model from {model_path}") self.llm: Llama = Llama( lib_path=os.path.join(lib_dir, "libllama.dylib"), model_path=model_path) self.system_prompt = system_prompt - + def download_model(self, model_filename: str, + repo_id: str, + local_dir: str) -> str: + logger.info("First run detected, downloading model from Hugging Face") + print( + "\n🤖 Thank you for using Open Codex!\n" + "📦 For the first run, we need to download the model from Hugging Face.\n" + "⏬ This only happens once – it'll be cached locally for future use.\n" + "🔄 Sit tight, the download will begin now...\n" + ) + logger.info(f"Downloading model phi4-mini from {repo_id}") + print("\n⏬ Downloading model phi4-mini ...") + + start = time.time() + model_path:str = hf_hub_download( + repo_id=repo_id, + filename=model_filename, + local_dir=local_dir, + ) + end = time.time() + download_time = end - start + logger.info(f"Model downloaded in {download_time:.2f}s to {model_path}") + print(f"✅ Model downloaded in {download_time:.2f}s\n") + return model_path + def one_shot_mode(self, user_input: str) -> str: + logger.debug("Preparing chat history with system prompt and user input") chat_history = [{"role": "system", "content": self.system_prompt}] chat_history.append({"role": "user", "content": user_input}) full_prompt = self.format_chat(chat_history) - with AgentPhi4Mini.suppress_native_stderr(): + + logger.debug("Generating completion with phi-4-mini model") + with Phi4MiniAgent.suppress_native_stderr(): output_raw = self.llm(prompt=full_prompt, max_tokens=100, temperature=0.2, stream=False) # unfortuntely llama_cpp has a union type for the output output = cast(CreateCompletionResponse, output_raw) assistant_reply : str = output["choices"][0]["text"].strip() + logger.debug(f"Generated response of length {len(assistant_reply)}") return assistant_reply @@ -83,7 +95,7 @@ def format_chat(self, messages: List[dict[str, str]]) -> str: @staticmethod def suppress_native_stderr(): """ - Redirect C‐level stderr (fd 2) into /dev/null, so llama.cpp logs vanish. + Redirect C‐level stderr (fd 2) into /dev/null, so llama.cpp logs vanish. """ devnull_fd = os.open(os.devnull, os.O_WRONLY) saved_stderr_fd = os.dup(2) diff --git a/src/open_codex/agents/qwen_25_coder.py b/src/open_codex/agents/qwen_25_coder.py new file mode 100644 index 0000000..0ec8f38 --- /dev/null +++ b/src/open_codex/agents/qwen_25_coder.py @@ -0,0 +1,124 @@ +import time +import os +import multiprocessing +from typing import cast, Optional, List +from llama_cpp import CreateCompletionResponse, Llama +from open_codex.interfaces.llm_agent import LLMAgent +import contextlib +from huggingface_hub import hf_hub_download, login + +class AgentQwen25Coder(LLMAgent): + def download_model(self, model_filename: str, + repo_id: str, + local_dir: str, + token: Optional[str] = None) -> str: + print( + "\n🤖 Welcome to Open Codex!\n" + "📦 First run requires downloading the model.\n" + "⚡️ This model is optimized for quick responses.\n" + ) + + start = time.time() + model_path:str = hf_hub_download( + repo_id=repo_id, + filename=model_filename, + local_dir=local_dir, + token=token, + force_download=True, # Force download to ensure the latest version + ) + duration = time.time() - start + print(f"✅ Model downloaded ({duration:.1f}s)") + return model_path + + def __init__(self, system_prompt: str, hf_token: Optional[str] = None): + model_filename = "Qwen2.5-Coder-1.5B-Instruct-F16.gguf" # Using correct model filename + repo_id = "unsloth/Qwen2.5-Coder-1.5B-Instruct-GGUF" # Using TheBloke's repository + local_dir = os.path.expanduser("~/.cache/open-codex") + model_path = os.path.join(local_dir, model_filename) + + if not hf_token: + hf_token = os.environ.get("HUGGINGFACE_TOKEN") + + if not os.path.exists(model_path): + model_path = self.download_model(model_filename, repo_id, local_dir, token=hf_token) + else: + print("🚀 Loading Qwen model...\n") + + # Get optimal thread count for the system + n_threads = min(4, multiprocessing.cpu_count()) + + with AgentQwen25Coder.suppress_native_stderr(): + self.llm: Llama = Llama( + model_path=model_path, + n_ctx=2048, # Smaller context for faster responses + n_threads=n_threads, # Use optimal thread count + n_batch=256, # Balanced batch size + use_mlock=True, # Lock memory to prevent swapping + use_mmap=True, # Use memory mapping for faster loading + ) + print("✨ Model ready!") + + self.system_prompt = system_prompt + + def one_shot_mode(self, user_input: str) -> str: + chat_history = [ + {"role": "system", "content": self.system_prompt}, + {"role": "user", "content": "I need a shell command to find all python files"}, + {"role": "assistant", "content": "find . -name \"*.py\""}, + {"role": "user", "content": user_input} + ] + full_prompt = self.format_chat(chat_history) + + with AgentQwen25Coder.suppress_native_stderr(): + try: + output_raw = self.llm( + prompt=full_prompt, + max_tokens=100, # Limit response length + temperature=0.1, # Lower temperature for more deterministic output + top_p=0.1, # Focus on most likely tokens + top_k=10, # Limit vocabulary for shell commands + repeat_penalty=1.1,# Prevent repetition + stop=["<|im_end|>", "<|im_start|>", "\n"], # Stop at appropriate tokens + stream=False + ) + + output = cast(CreateCompletionResponse, output_raw) + assistant_reply: str = output["choices"][0]["text"].strip() + + # Clean up response + assistant_reply = assistant_reply.split('\n')[0].strip() + assistant_reply = assistant_reply.replace("<|im_end|>", "").strip() + + # Basic validation of shell commands + if any(invalid_char in assistant_reply for invalid_char in ['<', '>', '|/']): + return "find . -name \"*.py\"" # fallback to safe command + + return assistant_reply + + except Exception as e: + print(f"⚠️ Model error: {str(e)}") + return "" + + def format_chat(self, messages: List[dict[str, str]]) -> str: + chat_prompt = "" + for msg in messages: + role_tag = "user" if msg["role"] == "user" else "assistant" + chat_prompt += f"<|{role_tag}|>\n{msg['content']}\n" + chat_prompt += "<|assistant|>\n" + return chat_prompt + + @contextlib.contextmanager + @staticmethod + def suppress_native_stderr(): + """ + Redirect C‐level stderr (fd 2) into /dev/null, so llama.cpp logs vanish. + """ + devnull_fd = os.open(os.devnull, os.O_WRONLY) + saved_stderr_fd = os.dup(2) + try: + os.dup2(devnull_fd, 2) + yield + finally: + os.dup2(saved_stderr_fd, 2) + os.close(devnull_fd) + os.close(saved_stderr_fd) diff --git a/src/open_codex/main.py b/src/open_codex/main.py index 73d218c..e6a6f21 100644 --- a/src/open_codex/main.py +++ b/src/open_codex/main.py @@ -1,21 +1,42 @@ import sys import argparse import subprocess +import pyperclip +import shutil +import os +from datetime import datetime + +from open_codex.agent_builder import AgentBuilder, ModelType +from open_codex.interfaces.llm_agent import LLMAgent + +# ANSI color codes + +from open_codex.agent_builder import AgentBuilder +from open_codex.interfaces.llm_agent import LLMAgent GREEN = "\033[92m" RED = "\033[91m" BLUE = "\033[94m" +YELLOW = "\033[93m" +BOLD = "\033[1m" RESET = "\033[0m" -# Capture single keypress (terminal) from the user -# and returns it as a string. It works on both Windows and Unix systems. +# Get terminal width for better formatting +TERM_WIDTH = shutil.get_terminal_size().columns -# Windows +def print_banner(text: str, color: str = BLUE, char: str = "=") -> None: + padding = char * ((TERM_WIDTH - len(text) - 2) // 2) + print(f"{color}{padding} {text} {padding}{RESET}") + +def print_timestamp(prefix: str = "") -> None: + timestamp = datetime.now().strftime("%H:%M:%S") + print(f"{YELLOW}{prefix}[{timestamp}]{RESET}") + +# Capture single keypress if sys.platform == "win32": import msvcrt def get_keypress(): return msvcrt.getch().decode("utf-8") -# Unix else: import termios, tty def get_keypress(): @@ -28,61 +49,87 @@ def get_keypress(): termios.tcsetattr(fd, termios.TCSADRAIN, old_settings) return key -def print_response(command: str): - print(f"{BLUE}Command found:\n=====================") - print(f"{GREEN}{command}{RESET}") - print(f"{BLUE}====================={RESET}") +def get_user_action(): print(f"{BLUE}What do you want to do with this command?{RESET}") print(f"{BLUE}[c] Copy [e] Execute [a] Abort{RESET}") print(f"{BLUE}Press key: ", end="", flush=True) - choice = get_keypress().lower() - print(f"{RESET}") + return choice - if choice == "e": - print(f"{BLUE}Executing command: {command}{RESET}") - result = subprocess.run(command, shell=True, capture_output=True, text=True) - print(f"{GREEN}Command output: {result.stdout}{RESET}") - if result.stderr: - print(f"{RED}Error: {result.stderr}{RESET}") - elif choice == "c": - import pyperclip # ⏱ lazy import - pyperclip.copy(command) +def run_user_action(choice: str, command: str): + if choice == "c": + print(f"{GREEN}Copying command to clipboard...{RESET}") + subprocess.run("pbcopy", universal_newlines=True, input=command) print(f"{GREEN}Command copied to clipboard!{RESET}") - elif choice == "a": - print(f"{BLUE}Aborted.{RESET}") + elif choice == "e": + print(f"{GREEN}Executing command...{RESET}") + subprocess.run(command, shell=True) + else: + print(f"{RED}Aborting...{RESET}") + sys.exit(1) + +def print_response(command: str): + print(f"{BLUE}Command found:\n=====================") + print(f"{GREEN}{command}{RESET}") + print(f"{BLUE}====================={RESET}") + print(f"{RESET}") + +def get_agent(args: argparse.Namespace) -> LLMAgent: + model: str = args.model + if args.ollama: + print(f"{BLUE}Using Ollama with model: {model}{RESET}") + return AgentBuilder.get_ollama_agent(model=model, host=args.ollama_host) else: - print(f"{RED}Unknown choice. Nothing happened.{RESET}") + print(f"{BLUE}Using model: phi-4-mini-instruct{RESET}") + return AgentBuilder.get_phi_agent() -def one_shot_mode(prompt: str): - from open_codex.agent_builder import AgentBuilder - print(f"{BLUE}Using model: phi-4-mini-instruct{RESET}") +def run_one_shot(agent: LLMAgent, user_prompt: str) -> str: try: - agent = AgentBuilder.get_agent() - response = agent.one_shot_mode(prompt) - print_response(response) + return agent.one_shot_mode(user_prompt) + except ConnectionError: + print(f"{RED}Could not connect to Model.{RESET}", file=sys.stderr) + sys.exit(1) except Exception as e: - print(f"{RED}Error: {e}{RESET}") + print(f"{RED}Unexpected error: {e}{RESET}", file=sys.stderr) + print(f"{RED}Exiting...{RESET}", file=sys.stderr) + sys.exit(1) -def print_help_message(): - print(f"{BLUE}Usage examples:{RESET}") - print(f"{GREEN}open-codex \"list all files in current directory\"") - print(f"{GREEN}open-codex \"find all python files modified in the last week\"") - print(f"{GREEN}open-codex \"create a tarball of the src directory\"") - print() +def get_help_message(): + return f""" + {BLUE}Usage examples:{RESET} + {GREEN}open-codex list all files in current directory + {GREEN}open-codex --ollama find all python files modified in the last week + {GREEN}open-codex --ollama --model llama3 "create a tarball of the src directory + """ -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("prompt", nargs="*", help="Natural language prompt") - args = parser.parse_args() - prompt = " ".join(args.prompt).strip() +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Open Codex is a command line interface for LLMs." + "It can be used to generate shell commands from natural language prompts.", + formatter_class=argparse.RawTextHelpFormatter, + epilog=get_help_message()) + + parser.add_argument("prompt", nargs="+", + help="Natural language prompt") + parser.add_argument("--model", type=str, + help="Model name to use (default: phi4-mini)", default="phi4-mini:latest") + parser.add_argument("--ollama", action="store_true", + help="Use Ollama for LLM inference, use --model to specify the model") + parser.add_argument("--ollama-host", type=str, default="http://localhost:11434", + help="Configure the host for the Ollama API. " \ + "If left empty, the default http://localhost:11434 is used.") - if not prompt or prompt == "--help": - print_help_message() - sys.exit(0) + return parser.parse_args() + +def main(): + args = parse_args() + agent = get_agent(args) - print(f"{BLUE}Prompt: {prompt}{RESET}", flush=True) - one_shot_mode(prompt) + # join the prompt arguments into a single string + prompt = " ".join(args.prompt).strip() + response = run_one_shot(agent, prompt) + print_response(response) + action = get_user_action() + run_user_action(action, response) if __name__ == "__main__": # We call multiprocessing.freeze_support() because we are using PyInstaller to build a frozen binary. @@ -95,4 +142,4 @@ def main(): # See: https://pyinstaller.org/en/stable/common-issues-and-pitfalls.html#when-to-call-multiprocessing-freeze-support from multiprocessing import freeze_support freeze_support() - main() \ No newline at end of file + main() diff --git a/uv.lock b/uv.lock index b13312b..a384fea 100644 --- a/uv.lock +++ 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