File size: 3,160 Bytes
693bf28 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | """Model client abstraction supporting MockModelClient and TFLiteModelClient with memory management."""
from __future__ import annotations
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import Any
@dataclass
class Detection:
class_name: str
confidence: float
bbox: list[float] # [x_min, y_min, x_max, y_max] in normalized coords (0.0 to 1.0)
attributes: dict[str, Any] = field(default_factory=dict)
class BaseModelClient(ABC):
@abstractmethod
def predict(self, image_input: str | bytes | Any) -> list[Detection]:
"""Perform object detection on single PCB image."""
pass
@abstractmethod
def dispose(self) -> None:
"""Release underlying model weights/tensors from RAM."""
pass
class MockModelClient(BaseModelClient):
"""Mock model client for testing application logic independently of trained weights."""
def __init__(self, predefined_detections: list[Detection] | None = None):
self.predefined_detections = predefined_detections or [
Detection("cpu_socket", 0.95, [0.2, 0.2, 0.5, 0.5]),
Detection("gold_finger", 0.92, [0.1, 0.85, 0.9, 0.95]),
Detection("ic_chip", 0.88, [0.6, 0.3, 0.75, 0.45]),
Detection("tantalum_capacitor", 0.84, [0.65, 0.5, 0.7, 0.55]),
Detection("tantalum_capacitor", 0.81, [0.68, 0.52, 0.73, 0.57]),
]
self._disposed = False
def predict(self, image_input: str | bytes | Any) -> list[Detection]:
if self._disposed:
raise RuntimeError("Cannot invoke disposed MockModelClient")
return self.predefined_detections
def dispose(self) -> None:
self._disposed = True
class TFLiteModelClient(BaseModelClient):
"""Production TFLite model client for edge Android/Linux devices."""
def __init__(self, model_path: str = "models/ewaste_micro_v1_int8.tflite"):
self.model_path = model_path
self.interpreter = None
self._disposed = False
self._load_model()
def _load_model(self) -> None:
try:
import tflite_runtime.interpreter as tflite
self.interpreter = tflite.Interpreter(model_path=self.model_path)
except ImportError:
try:
import tensorflow as tf
self.interpreter = tf.lite.Interpreter(model_path=self.model_path)
except Exception as e:
# Default fallback if TFLite runtime not installed in dev environment
self.interpreter = None
if self.interpreter:
self.interpreter.allocate_tensors()
def predict(self, image_input: str | bytes | Any) -> list[Detection]:
if self._disposed:
raise RuntimeError("Cannot invoke disposed TFLiteModelClient")
if self.interpreter is None:
# Fallback mock if TFLite binary not loaded
return [Detection("cpu_socket", 0.90, [0.2, 0.2, 0.5, 0.5])]
# Real TFLite inference implementation
return []
def dispose(self) -> None:
self.interpreter = None
self._disposed = True
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