diff --git a/apps/ml-service/main.py b/apps/ml-service/main.py
new file mode 100644
index 0000000..ad45bcf
--- /dev/null
+++ b/apps/ml-service/main.py
@@ -0,0 +1,53 @@
+from fastapi import FastAPI, File, HTTPException, UploadFile
+
+from model import ImageDecodeError, LABELS, SERVICE_NAME, SERVICE_VERSION, model_service
+from schemas import HealthResponse, MetadataResponse, PredictionResponse
+
+
+app = FastAPI(title="ZeaVis ML Service", version=SERVICE_VERSION)
+
+
+@app.on_event("startup")
+def load_model() -> None:
+    model_service.load()
+
+
+@app.get("/health", response_model=HealthResponse)
+def health() -> HealthResponse:
+    return HealthResponse(status="ok", model_loaded=model_service.model_loaded)
+
+
+@app.get("/metadata", response_model=MetadataResponse)
+def metadata() -> MetadataResponse:
+    return MetadataResponse(
+        service_name=SERVICE_NAME,
+        service_version=SERVICE_VERSION,
+        model_path=str(model_service.model_path),
+        model_loaded=model_service.model_loaded,
+        input_size=model_service.input_size,
+        labels=LABELS,
+    )
+
+
+@app.post("/predict", response_model=PredictionResponse)
+async def predict(file: UploadFile = File(...)) -> PredictionResponse:
+    if file.content_type is None or not file.content_type.startswith("image/"):
+        raise HTTPException(status_code=400, detail="Uploaded file must be an image")
+
+    if not model_service.model_loaded:
+        raise HTTPException(status_code=503, detail="Model is not loaded")
+
+    image_bytes = await file.read()
+
+    try:
+        label, confidence, probabilities = model_service.predict(image_bytes)
+    except ImageDecodeError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+    except Exception as exc:
+        raise HTTPException(status_code=500, detail="Prediction failed") from exc
+
+    return PredictionResponse(
+        label=label,
+        confidence=confidence,
+        probabilities=probabilities,
+    )