Update README.md

README.md

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 # Beyond-ChatGPT
-Chainlit App using Python streaming for Level 0 MLOps
-
-LLM Application with Chainlit, Docker, and Huggingface Spaces
-In this guide, we'll walk you through the steps to create a Language Learning Model (LLM) application using Chainlit, then containerize it using Docker, and finally deploy it on Huggingface Spaces.
-
-Prerequisites
-A GitHub account
-Docker installed on your local machine
-A Huggingface Spaces account
-
-
-### Building our App
-Clone [this](https://github.com/AI-Maker-Space/Beyond-ChatGPT/tree/main) repo.
-
-``` bash
-git clone https://github.com/AI-Maker-Space/Beyond-ChatGPT.git
-```
-
-Navigate inside this repo
-```
-cd Beyond-ChatGPT
-```
-
-Install the packages required for this python envirnoment in `requirements.txt`.
-```
-pip install -r requirements.txt
-```
-
-Open your `.env` file. Replace the `###` in your `.env` file with your OpenAI Key and save the file.
-```
-OPENAI_API_KEY=sk-###
-```
-
-Let's try deploying it locally. Make sure you're in the python environment where you installed Chainlit and OpenAI.
-
-Run the app using Chainlit
-
-```
-chainlit run app.py -w
-```
-
-Great work! Let's see if we can interact with our chatbot.
-
-Time to throw it into a docker container a prepare it for shipping
-
-Build the Docker image. We'll tag our image as `llm-app` using the `-t` parameter. The `.` at the end means we want all of the files in our current directory to be added to our image.
-``` bash
-docker build -t llm-app .
-```
-
-Run and test the Docker image locally using the `run` command. The `-p`parameter connects our **host port #** to the left of the `:` to our **container port #** on the right.
-``` bash
-docker run -p 7860:7860 llm-app
-```
-
-Visit http://localhost:7860 in your browser to see if the app runs correctly.
-
-Great! Time to ship!
-
-### Deploy to Huggingface Spaces
-
-Make sure you're logged into Huggingface Spaces CLI
-
-``` bash
-huggingface-cli login
-```
-
-Follow the prompts to authenticate.
-
-
-Deploy to Huggingface Spaces
-
-
-Create a new Huggingface Space
-
-- Owner: Your username
-- Space Name: `llm-app`
-- License: `Openrail`
-- Select the Space SDK: `Docker`
-- Docker Template: `Blank`
-- Space Hardware: `CPU basic - 2 vCPU - 16 GB - Free`
-- Repo type: `Public`
-
-
-
-Deploying on Huggingface Spaces using a custom Docker image involves using their web interface. Go to Huggingface Spaces and create a new space, then set it up to use your Docker image from the Huggingface Container Registry.
-
-Access the Application
-
-Once deployed, access your app at:
-
-ruby
-Copy code
-https://huggingface.co/spaces/your-username/llm-app
-Conclusion
-You've successfully created an LLM application with Chainlit, containerized it with Docker, and deployed it on Huggingface Spaces. Visit the link to interact with your deployed application.
+Please refer to the [LLMOps Dev Environment](https://github.com/AI-Maker-Space/LLMOps-Dev-101/)https://github.com/AI-Maker-Space/LLMOps-Dev-101/ for instructions.