| from typing import Any, Dict, List |
| import base64 |
| import io |
| import tempfile |
| from PIL import Image |
| import logging |
|
|
| def process_image(img): |
| crash_img = img.crop((0,0, 200, 200)) |
|
|
| if crash_img: |
| img_io = io.BytesIO() |
| crash_img.save(img_io, "PNG") |
| img_io.seek(0) |
| |
| return {"data": base64.b64encode(img_io.read()).decode("utf-8"), "mime_type": "image/png"} |
| else: |
| return {"error": "No crash diagram detected"} |
|
|
|
|
| class EndpointHandler: |
| def __init__(self, path: str = ""): |
| """Initialize the endpoint handler. |
| |
| Args: |
| path: Path to the model artifacts |
| """ |
| logging.warning("initialized") |
| pass |
| |
| def __call__(self, data: Any) -> List[List[Dict[str, str]]]: |
| logging.warning("inside __call__") |
| logging.warning(f"data keys {data.keys()}") |
| inputs = data.pop("inputs", data) |
| logging.warning(f"inputs keys {inputs.keys()}") |
| imagedata = inputs.pop("imagedata", inputs) |
| if isinstance(imagedata, str): |
| logging.warning("decoding pdfdata") |
| image_bytes = base64.b64decode(imagedata) |
| img = Image.open(io.BytesIO(image_bytes)) |
| logging.warning(f"image {str(img)}") |
| return process_image(img) |
