Asep Haryana SaputraandClaude Opus 4.7
ed8cecc3f5
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
from io import BytesIO
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import logging
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import os
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from pathlib import Path
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import numpy as np
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from PIL import Image, UnidentifiedImageError
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import tensorflow as tf
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LABELS = ["Bercak Daun", "Daun Sehat", "Karat Daun", "Hawar Daun"]
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SERVICE_NAME = "zeavis-ml-service"
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SERVICE_VERSION = "0.1.0"
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class ImageDecodeError(ValueError):
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pass
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class ModelService:
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def __init__(self) -> None:
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self.input_size = int(os.getenv("MODEL_INPUT_SIZE", "224"))
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self.model_path = self._resolve_model_path(os.getenv("MODEL_PATH", "../../Machine_Learning/best_model/best_model.keras"))
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self.model: tf.keras.Model | None = None
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self.load_error: str | None = None
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def _resolve_model_path(self, model_path: str) -> Path:
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path = Path(model_path)
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if path.is_absolute():
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return path
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return (Path(__file__).resolve().parent / path).resolve()
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@property
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def model_loaded(self) -> bool:
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return self.model is not None
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def load(self) -> None:
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try:
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self.model = tf.keras.models.load_model(self.model_path, compile=False)
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self.load_error = None
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except Exception as exc:
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self.model = None
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self.load_error = str(exc)
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logging.exception("Failed to load ML model from %s", self.model_path)
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def preprocess(self, image_bytes: bytes) -> np.ndarray:
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try:
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image = Image.open(BytesIO(image_bytes)).convert("RGB")
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except (UnidentifiedImageError, OSError) as exc:
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raise ImageDecodeError("Uploaded file is not a valid image") from exc
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image = image.resize((self.input_size, self.input_size))
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image_array = np.asarray(image, dtype=np.float32)
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return np.expand_dims(image_array, axis=0)
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def predict(self, image_bytes: bytes) -> tuple[str, float, dict[str, float]]:
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if self.model is None:
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raise RuntimeError("Model is not loaded")
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batch = self.preprocess(image_bytes)
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raw_predictions = self.model.predict(batch, verbose=0)[0]
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probabilities_array = np.asarray(raw_predictions, dtype=np.float32)
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top_index = int(np.argmax(probabilities_array))
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probabilities = {
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label: float(probabilities_array[index])
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for index, label in enumerate(LABELS)
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}
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return LABELS[top_index], float(probabilities_array[top_index]), probabilities
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model_service = ModelService()
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