Build an Image Classifier#
In this tutorial, we will collaboratively build an Image Classifier that can detect wind turbines in images. As a team, we will support the following functionalities:
Uploading an image file
Using an example image file
Viewing the image file
Viewing the predicted result
We will be using a random classifier, but you can later replace it with your own custom classifier if you want.
Note
When we ask you to run the code in the sections below, you may either execute the code directly in the Panel docs via the green run button, in a cell in a notebook, or in a file app.py
that is served with panel serve app.py --dev
.
Install the Dependencies#
Please ensure that hvPlot is installed.
pip install hvplot panel
conda install -y -c conda-forge hvplot panel
Create the App#
Run the code below.
import random
from io import BytesIO
from time import sleep
import hvplot.pandas
import pandas as pd
import requests
from PIL import Image
import panel as pn
IMAGE_DIM = 350
pn.extension(design="material", sizing_mode="stretch_width")
## Transformations
@pn.cache
def get_pil_image(url):
response = requests.get(url)
return Image.open(BytesIO(response.content))
@pn.cache
def get_plot(label):
data = pd.Series(label).sort_values()
return data.hvplot.barh(title="Prediction", ylim=(0, 100)).opts(default_tools=[])
## Custom Components
def get_label_view(fn, image: Image):
return get_plot(fn(image))
def get_image_button(url, image_pane):
button = pn.widgets.Button(
width=100,
height=100,
stylesheets=[
f"button {{background-image: url({url});background-size: cover;}}"
],
)
pn.bind(handle_example_click, button, url, image_pane, watch=True)
return button
## Event Handlers
def handle_example_click(event, url, image_pane):
image_pane.object = get_pil_image(url)
def handle_file_upload(value, image_pane):
file = BytesIO(value)
image_pane.object = Image.open(file)
def image_classification_interface(fn, examples):
## State
image_view = pn.pane.Image(
get_pil_image(examples[0]),
height=IMAGE_DIM,
width=IMAGE_DIM,
fixed_aspect=False,
margin=0,
)
label_view = pn.pane.JSON()
## Inputs
file_input = pn.widgets.FileInput(
accept=".png,.jpeg",
)
pn.bind(handle_file_upload, file_input, image_view, watch=True)
file_input_component = pn.Column("### Upload Image", file_input)
examples_input_component = pn.Column(
"### Examples", pn.Row(*(get_image_button(url, image_view) for url in examples))
)
## Views
label_view = pn.Row(
pn.panel(
pn.bind(get_label_view, fn=fn, image=image_view.param.object),
defer_load=True,
loading_indicator=True,
height=IMAGE_DIM,
width=IMAGE_DIM,
)
)
## Layouts
input_component = pn.Column(
"# Input",
image_view,
file_input_component,
examples_input_component,
width=IMAGE_DIM,
margin=10,
)
output_component = pn.Column(
"# Output",
label_view,
width=IMAGE_DIM,
margin=10,
)
return pn.FlexBox(input_component, output_component)
def predict(image: Image):
# Replace with your own image classification model
sleep(1.5)
a = random.uniform(0, 100)
b = random.uniform(0, 100 - a)
c = 100 - a - b
return {
"Wind Turbine": a,
"Solar Panel": b,
"Battery Storage": c,
}
EXAMPLES = [
# Replace with your own examples
"https://assets.holoviz.org/panel/tutorials/wind_turbine.png",
"https://assets.holoviz.org/panel/tutorials/solar_panel.png",
"https://assets.holoviz.org/panel/tutorials/battery_storage.png",
]
image_classification_interface(fn=predict, examples=EXAMPLES).servable()