code2prompt.py

import modal
import os

stub = modal.Stub("smol-codetoprompt-v1")
generatedDir = "generated"
openai_image = modal.Image.debian_slim().pip_install("openai")



def read_file(filename):
    with open(filename, 'r') as file:
        return file.read()

def walk_directory(directory):
    image_extensions = ['.png', '.jpg', '.jpeg', '.gif', '.bmp', '.svg', '.ico', '.tif', '.tiff']
    code_contents = {}
    for root, dirs, files in os.walk(directory):
        for file in files:
            if not any(file.endswith(ext) for ext in image_extensions):
                try:
                    relative_filepath = os.path.relpath(os.path.join(root, file), directory)
                    code_contents[relative_filepath] = read_file(os.path.join(root, file))
                except Exception as e:
                    code_contents[relative_filepath] = f"Error reading file {file}: {str(e)}"
    return code_contents



@stub.local_entrypoint()
def main(prompt=None, directory="generated", model="gpt-3.5-turbo"):
  code_contents = walk_directory(directory)

  # Now, `code_contents` is a dictionary that contains the content of all your non-image files
  # You can send this to OpenAI's text-davinci-003 for help

  context = "\n".join(f"{path}:\n{contents}" for path, contents in code_contents.items())
  system = "You are an AI debugger who is trying to fully describe a program, in order for another AI program to reconstruct every file, data structure, function and functionality. The user has provided you with the following files and their contents:"
  prompt = "My files are as follows: " + context + "\n\n" + (("Take special note of the following: " + prompt) if prompt else "")
  prompt += "\n\nDescribe the program in markdown using specific language that will help another AI program reconstruct the given program in as high fidelity as possible."
  res = generate_response.call(system, prompt, model)
  # print res in teal
  print("\033[96m" + res + "\033[0m")


@stub.function(
    image=openai_image,
    secret=modal.Secret.from_dotenv(),
    retries=modal.Retries(
        max_retries=3,
        backoff_coefficient=2.0,
        initial_delay=1.0,
    ),
    concurrency_limit=5,
    timeout=120,
)
def generate_response(system_prompt, user_prompt, model="gpt-3.5-turbo", *args):
    import openai

    # Set up your OpenAI API credentials
    openai.api_key = os.environ["OPENAI_API_KEY"]

    messages = []
    messages.append({"role": "system", "content": system_prompt})
    messages.append({"role": "user", "content": user_prompt})
    # loop thru each arg and add it to messages alternating role between "assistant" and "user"
    role = "assistant"
    for value in args:
        messages.append({"role": role, "content": value})
        role = "user" if role == "assistant" else "assistant"

    params = {
        'model': model,
        "messages": messages,
        "max_tokens": 2500,
        "temperature": 0,
    }

    # Send the API request
    response = openai.ChatCompletion.create(**params)

    # Get the reply from the API response
    reply = response.choices[0]["message"]["content"]
    return reply