--- title: lv-recipe-chatbot emoji: 🫑 colorFrom: green colorTo: indigo sdk: gradio sdk_version: 3.23.0 app_file: app.py pinned: false license: unknown --- # lv-recipe-chatbot This file will become your README and also the index of your documentation. ## Install ``` sh pip install lv_recipe_chatbot ``` ## How to use ``` python from dotenv import load_dotenv load_dotenv() app.launch_demo() ``` Running on local URL: http://127.0.0.1:7860 To create a public link, set `share=True` in `launch()`. or ``` sh python3 app.py ``` ## Dev quick-start `git clone` the repo ``` sh cd lv-recipe-chatbot ``` Make sure to use the version of python specified in `py_version.txt` Create a virtual environment. ``` sh python3 -m venv env ``` Activate the env and install dependencies. ``` sh source env/bin/activate pip install -r requirements.txt pip install -r requirements/dev.txt ``` To make the Jupyter environment, git friendly: `nbdev_install_hooks` If you want to render documentation locally, you will want to [install Quarto](https://nbdev.fast.ai/tutorials/tutorial.html#install-quarto). `nbdev_install_quarto` Put API secrets in .env ``` sh cp .env.example .env ``` Edit .env with your secret key(s). Only `OPEN_AI_KEY` is required. Then start the Gradio demo from within the virtual environment. ``` sh python3 app.py ``` Preview documentation ``` sh nbdev_preview ``` ## Dependencies If a new dependency for development is helpful for developers, add it to `dev.txt`. If it is a dependency for the app that is imported in source code, add it to `core.txt`. Then run: ``` sh pipreqs --force ``` This will update our `requirements.txt` to include the dependency as it should be pinned in the environment. ## Development [quick nbdev tutorial](https://nbdev.fast.ai/tutorials) Make changes in `/nbs`. Update the package files with `nbdev_export` then reimport with `pip install -e '.[dev]'` Preview doc `nbdev_preview` Build docs, test and update README `nbdev_prepare` ## Useful links - [Task Matrix (Formerly Visual ChatGPT)](https://github.com/microsoft/TaskMatrix) - [LangChain](https://python.langchain.com/en/latest/index.html) - [LLM Prompt Engineering](https://www.promptingguide.ai) - [OpenAI best practices for prompts](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api)