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<nav class="navbar navbar-expand-custom navbar-mainbg">
        <a class="navbar-brand navbar-logo" href="#">Navbar</a>
        <button class="navbar-toggler" type="button" aria-controls="navbarSupportedContent" aria-expanded="false" aria-label="Toggle navigation">
        <i class="fas fa-bars text-white"></i>
        </button>
        <div class="collapse navbar-collapse" id="navbarSupportedContent">
            <ul class="navbar-nav ml-auto">
                <div class="hori-selector"><div class="left"></div><div class="right"></div></div>
                <li class="nav-item">
                    <a class="nav-link" href="javascript:void(0);"><i class="fas fa-tachometer-alt"></i>Dashboard</a>

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resource "google_compute_instance" "default" {
  project      = var.project
  name         = "datastream-proxy"
  machine_type = var.proxy_machine_type
  zone         = var.zone

  boot_disk {
    initialize_params {
      image = "debian-cloud/debian-11"
    }

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resource "google_datastream_private_connection" "default" {
  project               = var.project
  display_name          = "Private connection profile"
  location              = var.region
  private_connection_id = "my-connection"

  vpc_peering_config {
    vpc    = data.google_compute_network.network.id
    subnet = "10.1.0.0/29"
  }

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resource "google_compute_global_address" "private_ip_address" {
  provider = google-beta
  project  = var.project

  name          = "private-ip-address"
  purpose       = "VPC_PEERING"
  address_type  = "INTERNAL"
  prefix_length = 16
  network       = data.google_compute_network.network.id
}

marvinhoxha

def send_photo_to_slack(result):
    image = open("foo.png", 'rb').read()
    client = WebClient("Slack_Bot_User_OAuth_Token") #input OAuth token
    client.files_upload(
        channels = "Slack_channel_id", #input channel id
        initial_comment = f"{result}",
        filename = "dog photo",
        content = image
    )

marvinhoxha

from kafka import KafkaProducer
import base64

def image_producer():
    producer = KafkaProducer(bootstrap_servers='localhost:9092')
    with open("./dogImages/test/002.Afghan_hound/Afghan_hound_00116.jpg", "rb") as imageFile:
        str1 = base64.b64encode(imageFile.read())
    
    producer.send('dogtopic', str1)

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def consumer():
    consumer = KafkaConsumer('dogtopic')
    for message in consumer:
        with open("foo.png","wb") as f:
            f.write(decodebytes(message.value))
        img = tf.keras.utils.load_img(
            "foo.png",
            target_size=(224,224)
        )
        input_arr = tf.keras.utils.img_to_array(img)

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def send_client_request(seldon_client, image):
    client_prediction = seldon_client.predict(
        data=image,
        deployment_name="seldon-dogbreed",
        payload_type="ndarray",
    )
    return client_prediction


if env == "COMPOSE":

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resnet_body = tf.keras.applications.ResNet50V2(
    weights="imagenet",
    include_top=False,
    input_shape=(int(IMG_SIZE), int(IMG_SIZE), 3),
)
resnet_body.trainable = False
inputs = tf.keras.layers.Input(shape=(int(IMG_SIZE), int(IMG_SIZE), 3))
x = resnet_body(inputs, training=False)
x = tf.keras.layers.Flatten()(x)
outputs = tf.keras.layers.Dense(133, activation="softmax")(x)

marvinhoxha

FROM python:3.8


RUN apt-get update && DEBIAN_FRONTEND=noninteractive && \
    apt-get install -y curl python3-setuptools && \
    apt-get clean && apt-get autoremove -y && rm -rf /var/lib/apt/lists/*

RUN mkdir /models

WORKDIR /app