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@dbaslan dbaslan released this 10 Mar 20:18
· 2 commits to main since this release
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Learn about vigilant mode.

This is the definitive release for Code Snippet Generator.

Usage Instructions

The user interface is simple and straightforward. Type in your prompt in the text box, then click the "Generate Code Snippet" button. Once the model generates and displays your code, you will have the option to provide feedback in a new text box. You may rewrite and submit feedback as many times as you like, and the model will rewrite its output as per your instructions.

Deployment Instructions

1. Run locally

  1. Install Python 3.10
  2. Download the source code.
  3. Create a .env file in the same directory as main.py
  4. Put your OpenAI API key within the .env file, like so:

Screenshot3

  1. Open the terminal and go to the project directory.
  2. Install dependencies by executing the following command in the terminal:
pip install -r requirements. txt
  1. Run the application by executing the following command in the terminal:
uvicorn main:app --reload
  1. Use the application by going to 'localhost:8000' on your browser.

2. Deploy through Docker

  1. Install Docker
  2. Download the source code.
  3. Create a .env file in the same directory as main.py
  4. Put your OpenAI API key within the .env file
  5. Open the terminal and go to the project directory.
  6. Build Docker image by executing the following command in the terminal:
docker build -t csg .
  1. Deploy Docker image by executing the following command in the terminal:
docker run -d --name csgc -p 80:80 csg
  1. Use the application by going to 'localhost:80' on your browser.

Modification Instructions

Code Snippet Generator, as it is, makes use of OpenAI's GPT 3.5 Turbo Instruct model through their API. However, it is possible to pick a different model and change the parameters to one's liking. These settings can be found in main.py, specifically under the call_openai_api() function:

Screenshot2

Simply browse OpenAI's documentation to pick your ideal model, and replace the model parameter which is set to "gpt-3.5-turbo-instruct" by default. It is also possible to fine-tune the model's output by changing the max_tokens and temperature parameters. Experiment with the parameters until you find an ideal configuration.

Requirements and Versioning

The application was built with Python 3.10.9, and should be compatible with any version >= 3.10. Below is the full list of required packages, as listed in requirements.txt:

  • annotated-types==0.6.0
  • anyio==4.3.0
  • certifi==2024.2.2
  • click==8.1.7
  • colorama==0.4.6
  • distro==1.9.0
  • exceptiongroup==1.2.0
  • fastapi==0.110.0
  • greenlet==3.0.3
  • h11==0.14.0
  • httpcore==1.0.4
  • httpx==0.27.0
  • idna==3.6
  • Jinja2==3.1.3
  • MarkupSafe==2.1.5
  • openai==1.13.3
  • pydantic==2.6.3
  • pydantic_core==2.16.3
  • python-dotenv==1.0.1
  • python-multipart==0.0.9
  • sniffio==1.3.1
  • starlette==0.36.3
  • tqdm==4.66.2
  • typing_extensions==4.10.0
  • uvicorn==0.27.1

Full Changelog: https://github.com/dbaslan/CodeSnippetGenerator/commits/latest

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