refactor: replace Python ML service runtime with Rust

apps/ml-service/.env.example

@@ -1,4 +1,4 @@
-MODEL_PATH=../../Machine_Learning/best_model/best_model.keras
+MODEL_PATH=../../Machine_Learning/model/model.onnx
 MODEL_INPUT_SIZE=224
 ML_SERVICE_HOST=0.0.0.0
 ML_SERVICE_PORT=8001

apps/ml-service/main.py

@@ -1,56 +0,0 @@
-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:
-        import logging
-
-        logging.exception("Prediction failed")
-        raise HTTPException(status_code=500, detail="Prediction failed") from exc
-
-    return PredictionResponse(
-        label=label,
-        confidence=confidence,
-        probabilities=probabilities,
-    )

apps/ml-service/model.py

@@ -1,72 +0,0 @@
-from io import BytesIO
-import logging
-import os
-from pathlib import Path
-
-import numpy as np
-from PIL import Image, UnidentifiedImageError
-import tensorflow as tf
-
-
-LABELS = ["Bercak Daun", "Daun Sehat", "Karat Daun", "Hawar Daun"]
-SERVICE_NAME = "zeavis-ml-service"
-SERVICE_VERSION = "0.1.0"
-
-
-class ImageDecodeError(ValueError):
-    pass
-
-
-class ModelService:
-    def __init__(self) -> None:
-        self.input_size = int(os.getenv("MODEL_INPUT_SIZE", "224"))
-        self.model_path = self._resolve_model_path(os.getenv("MODEL_PATH", "../../Machine_Learning/best_model/best_model.keras"))
-        self.model: tf.keras.Model | None = None
-        self.load_error: str | None = None
-
-    def _resolve_model_path(self, model_path: str) -> Path:
-        path = Path(model_path)
-        if path.is_absolute():
-            return path
-        return (Path(__file__).resolve().parent / path).resolve()
-
-    @property
-    def model_loaded(self) -> bool:
-        return self.model is not None
-
-    def load(self) -> None:
-        try:
-            self.model = tf.keras.models.load_model(self.model_path, compile=False)
-            self.load_error = None
-        except Exception as exc:
-            self.model = None
-            self.load_error = str(exc)
-            logging.exception("Failed to load ML model from %s", self.model_path)
-
-    def preprocess(self, image_bytes: bytes) -> np.ndarray:
-        try:
-            image = Image.open(BytesIO(image_bytes)).convert("RGB")
-        except (UnidentifiedImageError, OSError) as exc:
-            raise ImageDecodeError("Uploaded file is not a valid image") from exc
-
-        image = image.resize((self.input_size, self.input_size))
-        image_array = np.asarray(image, dtype=np.float32)
-        return np.expand_dims(image_array, axis=0)
-
-    def predict(self, image_bytes: bytes) -> tuple[str, float, dict[str, float]]:
-        if self.model is None:
-            raise RuntimeError("Model is not loaded")
-
-        batch = self.preprocess(image_bytes)
-        raw_predictions = self.model.predict(batch, verbose=0)[0]
-        probabilities_array = np.asarray(raw_predictions, dtype=np.float32)
-        top_index = int(np.argmax(probabilities_array))
-        probabilities = {
-            label: float(probabilities_array[index])
-            for index, label in enumerate(LABELS)
-        }
-
-        return LABELS[top_index], float(probabilities_array[top_index]), probabilities
-
-
-model_service = ModelService()

apps/ml-service/moon.yml

@@ -1,10 +1,18 @@
 tasks:
   dev:
-    command: .venv/bin/uvicorn main:app --host 0.0.0.0 --port 8001
+    command: cargo run
   typecheck:
-    command: python -m py_compile main.py model.py schemas.py
+    command: cargo check
     inputs:
-      - main.py
-      - model.py
-      - schemas.py
-      - requirements.txt
+      - Cargo.toml
+      - src/**/*.rs
+  test:
+    command: cargo test
+    inputs:
+      - Cargo.toml
+      - src/**/*.rs
+  build:
+    command: cargo build --release
+    inputs:
+      - Cargo.toml
+      - src/**/*.rs

apps/ml-service/requirements.txt

@@ -1,5 +0,0 @@
-fastapi>=0.115.0
-uvicorn[standard]>=0.32.0
-tensorflow>=2.13.0
-pillow>=10.0.0
-python-multipart>=0.0.9

apps/ml-service/schemas.py

@@ -1,21 +0,0 @@
-from pydantic import BaseModel
-
-
-class HealthResponse(BaseModel):
-    status: str
-    model_loaded: bool
-
-
-class MetadataResponse(BaseModel):
-    service_name: str
-    service_version: str
-    model_path: str
-    model_loaded: bool
-    input_size: int
-    labels: list[str]
-
-
-class PredictionResponse(BaseModel):
-    label: str
-    confidence: float
-    probabilities: dict[str, float]

apps/ml-service/test_model.py

@@ -1,15 +0,0 @@
-import unittest
-
-from model import LABELS
-
-
-class ModelServiceTests(unittest.TestCase):
-    def test_labels_match_training_class_order_with_display_names(self) -> None:
-        self.assertEqual(
-            LABELS,
-            ["Bercak Daun", "Daun Sehat", "Karat Daun", "Hawar Daun"],
-        )
-
-
-if __name__ == "__main__":
-    unittest.main()