Spaces:
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Sleeping
| # 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 | |
| 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. | |