Are this steps correct for abstract the layer of deployment for AI Tools. Anyone should be easily add a new model to the stack without thinking about deployments. We should be able to deploy AI Tools in such a way where each model (every model can be packaged as a container) should be independently scalable. As a user, I should be able to access APIs associated with any model. :

## 1. Adding a New Model to the AI Toolchain

1. First, create a new directory for your model within the src folder in the AI Toolchain repository and name it according to your model’s purpose. For example, if you’re adding a sentiment analysis model with a specific provider and mode, the folder should be located at src/sentiment_analysis/provider/mode/.

2. Within the new directory, add the following files:

- model.py: This file should contain the implementation of the model using a popular machine learning framework like TensorFlow, PyTorch, or scikit-learn. It should include methods for training, evaluating, and predicting.

- requirements.txt: List any Python dependencies required by your model.

3. Create a new Dockerfile in your model’s directory to containerize the model. The file should include instructions to install dependencies and copy relevant files to the container. Here’s an example:

   FROM python:3.9-slim

   WORKDIR /app

   COPY requirements.txt .

   RUN pip install --no-cache-dir -r requirements.txt

   COPY model.py .

   CMD [“python”, “model.py”]


4. Update the repository_data.json file to include an entry for your new model with the corresponding information.

## 2. Testing the Model Locally with Docker Compose

1. Add a new service entry for your model in the existing docker-compose.yml file by following this example:

   sentiment_analysis:
     build:
       context: ./src/sentiment_analysis/provider/mode/
       dockerfile: Dockerfile
     ports:
       - “8001:8000”
     environment:
       - PYTHONUNBUFFERED=1
       - PYTHONDONTWRITEBYTECODE=1


   Replace ./src/sentiment_analysis/provider/mode/ with the path to your model’s directory and adjust the port numbers accordingly.

2. Run docker-compose up to build and run the containers for the main app and your new model.

3. Test your model’s API locally using a tool like curl or Postman, connecting to http://localhost:8000/use_case/provider/mode.

## 3. Deploying with Docker Compose

When you’re ready to deploy your model, you can use the existing docker-compose.yml file to deploy the entire AI Toolchain stack, including your new model.

1. Push your model’s Docker image to a container registry:

   docker-compose build sentiment_analysis
   docker tag sentiment_analysis:latest your-container-registry-url/sentiment_analysis:latest
   docker push your-container-registry-url/sentiment_analysis:latest


2. Update the docker-compose.yml file in your deployment environment with the new service entries for your model, as was done for testing locally.

3. Run docker-compose up -d in your deployment environment to start all services, including your new model.

## 4. Accessing the Model’s APIs

Once the AI Toolchain stack is deployed using Docker Compose, users can access the APIs associated with each model by calling the main app’s API at http://your.deployment.url/use_case/provider/mode.

For example, if the AI Toolchain is deployed at http://your.deployment.url, and your new model is a sentiment analysis model at the endpoint /sentiment_analysis/provider/mode, users can access the API by sending requests to http://your.deployment.url/sentiment_analysis/provider/mode.

By following these steps, you can add new models to the AI Toolchain, test them locally, and deploy them using Docker Compose, allowing users to access the APIs associated with any model.