v1.1.0 Feature improvements (#1)

* Rubber Ducky: A pair programming tool for developers to debug and think through design.

Features:

* Process files with
* Custom prompt support with
* Process directories with
* Chain commands with
* Use the Ollama model for feedback and suggestions

Example output: [Screenshot of ducky](image.png)

* Quick fix for directory function

README.md

@@ -5,7 +5,7 @@
 - Install ollama
 - `ollama run codellama` (first time and then you can just have application in background)
 - There are probably other dependencies which I forgot to put in setup.py sorry in advance.
-- run with `ducky --file <path> --prompt (optional) <prompt to override>`
+- Run with `ducky -f <file path>`
 
 ## Why did I make this 
 
@@ -22,12 +22,25 @@ You will need Ollama installed on your machine. The model I use for this project
 For the first installation you can run `ollama run codellama` and it should pull the necessary binaries for you. Ollama is also great because it'll spin up a server which can run in the background and can even do automatic model switching as long as you have it installed.
 
 ## Usage
-Make sure you have the package installed. Easiest through [pypi](https://pypi.org/project/rubber-ducky/). 
 
-`pip install rubber-ducky` also works.
+Install through [pypi](https://pypi.org/project/rubber-ducky/):
 
-To run:
+`pip install rubber-ducky` .
 
-`ducky --file <path> --prompt (optional) <prompt to override>`
+### Simple run
+`ducky`
 
-I have yet to implement some methods so if you do something I don't say that's on you.
+### To use additional options:
+
+`ducky --file <path> --prompt <prompt> --directory <directory> --chain --model <model>`
+
+Where:
+- `--prompt` or `-p`: Custom prompt to be used
+- `--file` or `-f`: The file to be processed
+- `--directory` or `-d`: The directory to be processed
+- `--chain` or `-c`: Chain the output of the previous command to the next command
+- `--model` or `-m`: The model to be used (default is "codellama")
+
+
+## Example output
+![Screenshot of ducky](image.png)

ducky/ducky.py

@@ -2,43 +2,95 @@
 from typing import Optional
 from langchain.llms.ollama import Ollama
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from termcolor import colored
 
+class RubberDuck:
+    """
+    This class is a wrapper around the Ollama model.
+    """
+    def __init__(self, model: str = "codellama") -> None:
+        """
+        This function initializes the RubberDuck class.
+
+        Args:
+            model (str, optional): The model to be used. Defaults to "codellama".
+        """
+        self.system_prompt = """You are a pair progamming tool to help developers debug, think through design, and write code. 
+        Help the user think through their approach and provide feedback on the code."""
+        self.llm = Ollama(model=model, callbacks=[StreamingStdOutCallbackHandler()], system=self.system_prompt)
+
+
+    def call_llama(self, code: str = "", prompt: Optional[str] = None, chain: bool = False) -> None:
+        """
+        This function calls the Ollama model to provide feedback on the given code.
+
+        Args:
+            code (str): The code to be reviewed.
+            prompt (Optional[str]): Custom prompt to be used. Defaults to None.
+        """
+        if prompt is None:
+            prompt = "review the code, find any issues if any, suggest cleanups if any:" + code
+        else:
+            prompt = prompt + code
 
-def call_llama(code: str, prompt: Optional[str] = None) -> None:
+
+        self.llm(prompt)
+        if chain:
+            while(True):
+                prompt = input(colored("\n What's on your mind? \n ", 'green'))
+                self.llm(prompt)
+
+
+def read_files_from_dir(directory: str) -> str:
     """
-    This function calls the Ollama model to provide feedback on the given code.
+    This function reads all the files from a directory and returns the concatenated string.
 
     Args:
-        code (str): The code to be reviewed.
-        prompt (Optional[str], optional): Custom prompt to be used. Defaults to None.
-    """
-    if prompt is None:
-        prompt = "review the code, find any issues if any, suggest cleanups if any:" + code
-    else:
-        prompt = prompt + code
-    system_prompt = """You are a pair progamming tool to help developers debug, think through design, and write code. Help the user rubber duck by providing feedback on the code."""
+        directory (str): The directory to be processed.
 
-    # TODO: find out how to enable the python trained model
-    llm = Ollama(model="codellama", callbacks=[StreamingStdOutCallbackHandler()], system=system_prompt)
+    Returns:
+        str: The concatenated string of all the files.
+    """
+    import os
+    files = os.listdir(directory)
+    code = ""
+    for file in files:
+        code += open(directory + "/" + file).read()
+    return code
 
-    # TODO: add chaining if it makes sense
-    llm(prompt)
 
 def ducky() -> None:
     """
     This function parses the command line arguments and calls the Ollama model.
     """
     parser = argparse.ArgumentParser()
-    parser.add_argument("--prompt", help="Custom prompt to be used", default=None)
-    parser.add_argument("--file", help="The file to be processed", default=None)
-    parser.add_argument("--directory", help="The directory to be processed", default=None)
+    parser.add_argument("--prompt", "-p", help="Custom prompt to be used", default=None)
+    parser.add_argument("--file", "-f", help="The file to be processed", default=None)
+    parser.add_argument("--directory", "-d", help="The directory to be processed", default=None)
+    parser.add_argument("--chain", "-c", help="Chain the output of the previous command to the next command", action="store_true", default=False)
+    parser.add_argument("--model", "-m", help="The model to be used", default="codellama")
     args, _ = parser.parse_known_args()
 
+    # My testing has shown that the codellama:7b-python is good for returning python code from the program.
+    # My intention with this tool was to give more general feedback and have back a back and forth with the user.
+    rubber_ducky = RubberDuck(model=args.model)
+    if args.file is None and args.directory is None:
+        if args.chain:
+            while(True):
+                prompt = input(colored("\n What's on your mind? \n ", 'green'))
+                rubber_ducky.call_llama(prompt=prompt, chain=args.chain)
+        else:
+            prompt = input(colored("\n What's on your mind? \n ", 'green'))
+            rubber_ducky.call_llama(prompt=prompt, chain=args.chain) 
+
     if args.file is not None:
         code = open(args.file).read()
-        call_llama(code=code, prompt=args.prompt)
-    else:
-        raise Exception("No file provided")
+        rubber_ducky.call_llama(code=code, prompt=args.prompt, chain=args.chain)
+
+    elif args.directory is not None:
+        code = read_files_from_dir(args.directory)
+        rubber_ducky.call_llama(code=code, prompt=args.prompt, chain=args.chain)
+
 
 if __name__ == "__main__":
     ducky()

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name='rubber-ducky',
-    version='1.0.1',
+    version='1.1.0',
     description='AI Companion for Pair Programming',
     long_description=long_description,
     long_description_content_type='text/markdown',
@@ -16,6 +16,7 @@
     packages=find_packages(),
     install_requires=[
         'langchain',
+        'termcolor'
     ],
     entry_points={
         'console_scripts': [