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README.md

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 title: BeyondChatGPT Demo
 emoji: 📉
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 ### That's it for now!  And so it begins.... :)
-=======
-# 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.
-
-It works! Let's ship it!
-
-
-### Deploy to Huggingface Spaces
-
-Login to Huggingface Spaces CLI
-
-``` bash
-huggingface-cli login
-```
-
-Follow the prompts to authenticate.
-
-
-
-Push Docker Image to Huggingface Container Registry
-
-```
-docker tag llm-app:latest huggingface/your-username/llm-app:latest
-docker push huggingface/your-username/llm-app:latest
-```
-
-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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