| """ |
| HuggingFace Inference Endpoint Handler for LearningStudio Callout Detection. |
| |
| This wrapper provides an EMCO-compatible API format for LearningStudio integration, |
| calling the AWS Lambda-based callout detection pipeline via API Gateway. |
| """ |
|
|
| from typing import Dict, Any |
| from inference import inference |
|
|
|
|
| class EndpointHandler: |
| """ |
| HuggingFace Inference Endpoint Handler. |
| |
| This class provides the interface expected by HuggingFace Inference Endpoints. |
| It wraps the callout detection pipeline and transforms outputs to EMCO format. |
| """ |
|
|
| def __init__(self, path: str = ""): |
| """ |
| Initialize the endpoint handler. |
| |
| Args: |
| path: Model path (unused for this wrapper, but required by HF interface) |
| """ |
| |
| pass |
|
|
| def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]: |
| """ |
| Process an inference request. |
| |
| Args: |
| data: Request data with format: |
| { |
| "inputs": "image_url_or_base64", |
| "parameters": {...} # Optional parameters |
| } |
| |
| Returns: |
| EMCO-compatible response: |
| { |
| "predictions": [ |
| { |
| "id": 1, |
| "label": "callout", |
| "class_id": 0, |
| "confidence": 0.95, |
| "bbox": {"x1": 100, "y1": 200, "x2": 300, "y2": 400} |
| }, |
| ... |
| ], |
| "total_detections": N, |
| "image": "base64_encoded_image" |
| } |
| """ |
| |
| inputs = data.get("inputs") |
| if inputs is None: |
| return { |
| "error": "Missing 'inputs' field", |
| "predictions": [], |
| "total_detections": 0, |
| "image": "" |
| } |
|
|
| |
| parameters = data.get("parameters", {}) |
|
|
| |
| result = inference(inputs, parameters) |
|
|
| return result |
|
|
