> ## Documentation Index
> Fetch the complete documentation index at: https://docs.inferless.com/llms.txt
> Use this file to discover all available pages before exploring further.

# InferlessPythonModel Class

The class called 'InferlessPythonModel' is the entrypoint for you ML code. This class has three methods: 'initialize', 'infer', and 'finalize'. Let's go through each method and explain their purpose, signature, and return types.

**'initialize'** method:

* Purpose: This method is responsible for initializing the model and setting up the necessary components.
* Signature: The method takes in one parameter, self, which refers to the instance of the class. It doesn't have any other parameters.
* Return type: This method doesn't return anything (None).

**'infer'** method:

* Purpose: This method performs the inference process using the initialized model. It takes in an input dictionary containing a "prompt" key, and it generates an image based on the provided prompt.
* Signature: The method takes in two parameters: self (referring to the instance of the class) and inputs (a dictionary containing the input data).
* Return type: The method returns a dictionary with a single key-value pairs.

**'finalize'** method:

* Purpose: This method is responsible for cleaning up and finalizing the model. It sets the pipe attribute to None.
* Signature: The method takes in one parameter, self, which refers to the instance of the class. It doesn't have any other parameters.
* Return type: This method doesn't return anything (None).

### Example

```python theme={null}
from diffusers import StableDiffusionPipeline
import torch
from io import BytesIO
import base64

class InferlessPythonModel:
    def initialize(self):
        self.pipe = StableDiffusionPipeline.from_pretrained(
            "stabilityai/stable-diffusion-2-1",
            use_safetensors=True,
            torch_dtype=torch.float16,
            device_map='auto'
        )

    def infer(self, inputs):
        prompt = inputs["prompt"]
        image = self.pipe(prompt).images[0]
        buff = BytesIO()
        image.save(buff, format="JPEG")
        img_str = base64.b64encode(buff.getvalue()).decode()
        return { "generated_image_base64" : img_str }
        
    def finalize(self):
        self.pipe = None

```