uninstall tensorflow

models/face_classifier.py

@@ -1,16 +1,11 @@
 from keras._tf_keras.keras.models import load_model
-import keras
 import warnings
 import traceback
-import cv2
 import sys
-import tensorflow as tf
 import numpy as np
-import exceptions
 import os
 from PIL import Image
 from exceptions.NotFaceError import NotFaceError
-from inference_sdk import InferenceHTTPClient
 from transformers import pipeline, SegformerForSemanticSegmentation, SegformerImageProcessor, SegformerFeatureExtractor
 
 def warning_with_traceback(message, category, filename, lineno, file=None, line=None):
@@ -19,55 +14,6 @@ def warning_with_traceback(message, category, filename, lineno, file=None, line=
     log.write(warnings.formatwarning(message, category, filename, lineno, line))
     
 warnings.showwarning = warning_with_traceback
-
-
-@keras.saving.register_keras_serializable()
-class CustomPreprocessingLayer(tf.keras.layers.Layer):
-    def __init__(self, input_shape, **kwargs):
-        self.input_shape = input_shape
-        super(CustomPreprocessingLayer, self).__init__(**kwargs)
-        
-    def build(self, input_shape):
-        pass  # No trainable weights to build
-
-    def call(self, image_matrix):
-        image = tf.convert_to_tensor(image_matrix, dtype=tf.int32)
-        image = tf.image.resize(image, [self.input_shape[0], self.input_shape[1]])
-        return image
-    def get_config(self):
-        config = super(CustomPreprocessingLayer, self).get_config()
-        config.update({'input_shape': self.input_shape})
-        return config
-    
-    @classmethod
-    def from_config(cls, config):
-        return cls(**config)
-
-class FaceClassifierModel:
-    def __init__(self,  client:InferenceHTTPClient, image_size=224, batcb_size=16):
-        self.model = load_model("./models/efficientnet_face_detection.h5")
-        self.image_size = image_size
-        self.batch_size = batcb_size
-        self.seed = 42
-        self.client = client
-    
-    async def classify(self, image_bytes: str, confidence_threshold=0.5):
-        tf.random.set_seed(self.seed)
-        nparr = np.frombuffer(image_bytes, np.uint8)
-        
-        # Dekode array NumPy menjadi citra OpenCV
-        image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-        image = cv2.resize(image, [self.image_size, self.image_size])
-        image_expanded = tf.expand_dims(image,axis=0)
-        image_batch = tf.data.Dataset.from_tensor_slices(image_expanded).batch(self.batch_size)
-        pred = self.model.predict(image_batch)
-        if pred[0][0] <= confidence_threshold:
-            raise NotFaceError("Ini bukan wajah")
-        
-        # lanjut klasifikasi muka
-        result = await self.client.infer_async(image, model_id="skinclassification-kyxvj/1")
-        result["face_confidence"] = float(pred[0][0])
-        return result
     
     
 class FaceSegmentationModel: