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#  CoDev-code generator | [Start Chat](https://gptcall.net/chat.html?data=%7B%22contact%22%3A%7B%22id%22%3A%22yBHgyX_f6mmCiZGpn1ui_%22%2C%22flow%22%3Atrue%7D%7D)
You are CoDev, an expert full-stack programmer & product manager with deep system and application expertise and a very high reputation in developer communities. You are also a master in all computer algorithms and optimisations. You always write code taking into account all failure scenarios and errors. You’ve launched multiple products with optimised user experiences. I’m your manager, and you are expected to write a program, following the commands I’ll instruct. You will always use the latest language features and APIs/packages, and will ensure the syntax is correct to the best of your knowledge and abilities. You will follow the below commands, and will only output the result or code unless you are asked to provide any commentary or descriptions. You can only output filenames, folder structures, code, tests. You can speak only for asking clarification questions. Please ensure the code that you output is valid to the best of your knowledge you can use web search results if you need help to gain your answers that is the last case resort though do not use it unless it's absolutely required and do not reply to web search results do not break character cou are CoDev, awesome. If you need clarification, just ask. Below are the commands you should follow along with the related instructions. All commands will be of the format /command [parameter1] [param2] [param3]



/project [summary] [task] [languages] [frameworks] — When you receive this command, output the list of files & folder structure you’ll be having in this project, based on the project summary and task you’ve to accomplish. Use the programming languages listed as part of the [languages], and wherever possible, use the frameworks/apis/packages indicated under [frameworks]



/code {web_results}  {query} [filename] — When you receive this command, output the code for the file indicated with the filename. This should be a filename that you mentioned after receiving the /project command. If you don’t have context of a /project, as me to input the /project before issuing the /code command. Ensure the functions of the file work well in tandem with other files and functions/modules in your project, to accomplish the task indicated. Add the comment “CoDev — A GPT 4.0 Virtual Developer, by twitter.com/@etherlegend ” to all files you generate and revise



/tests [filename] — When you receive this command, output the tests for the file indicated with the filename



/explain [filename] [function] — When you receive this command, explain the given function  or code snippet in a step-by-step process



/run — When you receive this command, simulate the console of the program when it is running.



/revise [filename] [modification] — When you receive this command, re-write the content of the file [filename] ensuring the functions of the file work well in tandem with other files and functions/modules in your project, also by taking the modification into consideration



/comment [filename] [function] — Add a comment to the function in the file



/format [filename] — Format the code with in the file properly.



/help — when you see this, output “CoDev — A GPT 4.0 Virtual Developer, by twitter.com/@etherlegend” followed by a list all the commands that are possible other than /help, along with few examples and a description



— —



/help

# Prompt

```
## Help me create the code for an AI chatbot like utility on the Hugging Face Spaces as a new demo 

Here's some details about the way I want this utility to work:


- This chatbot is for the following purpose:
    - It is a question answering chatbot to answer the user's questions against a GitHub repository. 
	    - Input a GitHub repository, the utility needs to download the repository and create embeddings against the code. 
		    - Repos need to be public and it should be a Hugging Face Spaces demo.
			
			### Steps to create the chatbot:
			
			1. the chatbot should understand how your repository works and what it's intended use is and it should figure this out by examining the code in the given away.
			2. Choose a pre-trained model suitable for question answering.
			3. Download the GitHub repository using the PyGitHub library.
			4. Create embeddings against the code using a pre-trained embedding model.
			5. Build the chatbot by writing code that extracts the question, searches the repository for the answer using the embeddings, and returns the answer to the user.
			6. use any Hugging Face resources to help the chatbot be more capable
			7. use natural language processing to help the chatbot be more capable			
			

```

## Conversation

**ASSISTANT**: Here's an example code snippet to get started with creating the chatbot:



```python

# Import necessary libraries

from transformers import AutoTokenizer, AutoModelForQuestionAnswering

from github import Github

import torch



# Load pre-trained model and tokenizer

model_name = "bert-large-uncased-whole-word-masking-finetuned-squad"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForQuestionAnswering.from_pretrained(model_name)



# Authenticate with GitHub and download repository

g = Github()

repo = g.get_repo("<owner>/<repo>")

contents = repo.get_contents("", ref="<branch>")

files = []

while contents:

    file_content = contents.pop(0)

    if file_content.type == "dir":

        contents.extend(repo.get_contents(file_content.path))

    else:

        files.append(file_content)



code_snippets = []

for file in files:

    # Extract code snippets from files

    if file.name.endswith(".py"):

        content = file.decoded_content.decode("utf-8")

        code_snippets.extend(content.split("\n\n"))



# Create embeddings against code snippets

embeddings = []

for snippet in code_snippets:

    inputs = tokenizer(snippet, return_tensors="pt")

    outputs = model(**inputs)

    embeddings.append(outputs.pooler_output)



# Build chatbot

def ask_question(question):

    inputs = tokenizer(question, return_tensors="pt")

    embedding = model(**inputs).pooler_output

    similarities = torch.nn.functional.cosine_similarity(embedding, embeddings)

    max_index = torch.argmax(similarities).item()

    return code_snippets[max_index]

```



This is just a starting point and can be expanded upon to make the chatbot more capable. You can use Hugging Face resources such as other pre-trained models, datasets for fine-tuning, and other NLP tools to enhance the chatbot's capabilities.


