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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 repo
Navigate inside this repo
### Install requirements using `pip install -r requirements.txt`?????????
Add your OpenAI Key to `.env` file and save the file.
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
``` bash
docker build -t llm-app .
```
Test the Docker Image Locally (Optional)
``` 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.
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