hotfix: snafu from renaming ml_backends to processing_services

docker-compose.ci.yml

@@ -8,7 +8,7 @@ services:
       - postgres
       - redis
       - minio-init
-      - processing_service
+      - ml_backend
     env_file:
       - ./.envs/.ci/.django
       - ./.envs/.ci/.postgres
@@ -43,10 +43,8 @@ services:
       - ./compose/local/minio/init.sh:/etc/minio/init.sh
     entrypoint: /etc/minio/init.sh
 
-  processing_service:
+  ml_backend:
     build:
       context: ./processing_services/example
     volumes:
-      - ./processing_services/example/:/app:processing_service
-    ports:
-      - "2000:2000"
+      - ./processing_services/example/:/app

docker-compose.yml

@@ -23,6 +23,7 @@ services:
       - postgres
       - redis
       - minio-init
+      - ml_backend
     volumes:
       - .:/app:z
     env_file:
@@ -141,8 +142,12 @@ services:
 
   ml_backend:
     build:
-      context: ./ml_backends/example
+      context: ./processing_services/example
     volumes:
-      - ./ml_backends/example/:/app
+      - ./processing_services/example/:/app
     ports:
       - "2005:2000"
+    networks:
+      default:
+          aliases:
+            - processing_service

processing_services/example/api/api.py

@@ -0,0 +1,110 @@
+"""
+Fast API interface for processing images through the localization and classification pipelines.
+"""
+
+import logging
+import time
+
+import fastapi
+
+from .pipelines import ConstantPipeline, Pipeline, RandomPipeline
+from .schemas import (
+    AlgorithmConfigResponse,
+    PipelineRequest,
+    PipelineResultsResponse,
+    ProcessingServiceInfoResponse,
+    SourceImage,
+    SourceImageResponse,
+)
+
+logger = logging.getLogger(__name__)
+
+app = fastapi.FastAPI()
+
+
+pipelines: list[type[Pipeline]] = [RandomPipeline, ConstantPipeline]
+pipeline_choices: dict[str, type[Pipeline]] = {pipeline.config.slug: pipeline for pipeline in pipelines}
+algorithm_choices: dict[str, AlgorithmConfigResponse] = {
+    algorithm.key: algorithm for pipeline in pipelines for algorithm in pipeline.config.algorithms
+}
+
+
+@app.get("/")
+async def root():
+    return fastapi.responses.RedirectResponse("/docs")
+
+
+@app.get("/info", tags=["services"])
+async def info() -> ProcessingServiceInfoResponse:
+    info = ProcessingServiceInfoResponse(
+        name="ML Backend Template",
+        description=(
+            "A template for an inference API that allows the user to run different sequences of machine learning "
+            "models and processing methods on images for the Antenna platform."
+        ),
+        pipelines=[pipeline.config for pipeline in pipelines],
+        # algorithms=list(algorithm_choices.values()),
+    )
+    return info
+
+
+# Check if the server is online
+@app.get("/livez", tags=["health checks"])
+async def livez():
+    return fastapi.responses.JSONResponse(status_code=200, content={"status": True})
+
+
+# Check if the pipelines are ready to process data
+@app.get("/readyz", tags=["health checks"])
+async def readyz():
+    """
+    Check if the server is ready to process data.
+
+    Returns a list of pipeline slugs that are online and ready to process data.
+    @TODO may need to simplify this to just return True/False. Pipeline algorithms will likely be loaded into memory
+    on-demand when the pipeline is selected.
+    """
+    if pipeline_choices:
+        return fastapi.responses.JSONResponse(status_code=200, content={"status": list(pipeline_choices.keys())})
+    else:
+        return fastapi.responses.JSONResponse(status_code=503, content={"status": []})
+
+
+@app.post("/process", tags=["services"])
+async def process(data: PipelineRequest) -> PipelineResultsResponse:
+    pipeline_slug = data.pipeline
+
+    source_images = [SourceImage(**image.model_dump()) for image in data.source_images]
+    source_image_results = [SourceImageResponse(**image.model_dump()) for image in data.source_images]
+
+    start_time = time.time()
+
+    try:
+        Pipeline = pipeline_choices[pipeline_slug]
+    except KeyError:
+        raise fastapi.HTTPException(status_code=422, detail=f"Invalid pipeline choice: {pipeline_slug}")
+
+    pipeline = Pipeline(source_images=source_images)
+    try:
+        results = pipeline.run()
+    except Exception as e:
+        logger.error(f"Error running pipeline: {e}")
+        raise fastapi.HTTPException(status_code=422, detail=f"{e}")
+
+    end_time = time.time()
+    seconds_elapsed = float(end_time - start_time)
+
+    response = PipelineResultsResponse(
+        pipeline=pipeline_slug,
+        algorithms={algorithm.key: algorithm for algorithm in pipeline.config.algorithms},
+        source_images=source_image_results,
+        detections=results,
+        total_time=seconds_elapsed,
+    )
+    return response
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=2000)