classifier.py

from taipy.gui import Gui
from tensorflow.keras import models
from PIL import Image
import numpy as np

class_names = {
    0: 'airplane',
    1: 'automobile',
    2: 'bird',
    3: 'cat',
    4: 'deer',
    5: 'dog',
    6: 'frog',
    7: 'horse',
    8: 'ship',
    9: 'truck',
}

model = models.load_model('model.keras') 

def predict_image(model, path_to_img):
    img = Image.open(path_to_img)
    img = img.convert('RGB')
    img = img.resize((32,32))
    #Normalise images
    data = np.asarray(img)
    data = data / 255
    probs = model.predict(np.array([data])[:1]) # Trick neural network to proccess a single image
 
    top_prob = probs.max()
    top_pred = class_names[np.argmax(probs)]

    return top_prob, top_pred

content =''
logo_path = 'logo.png'
img_path= 'placeholder_image.png'
prob = 0
pred = ''

index = '''
<|text-center|
<|{logo_path}|image|width=25vw|>

<|{content}|file_selector|extensions=.png|>
select an image from your file system

<|{pred}|>

<|{img_path}|image|>

<|{prob}|indicator|value={prob}|min=0|max=100|width=25vw|>
>
'''

def on_change(state, var_name,var_val):
    if var_name == 'content':
        top_prob, top_pred = predict_image(model, var_val)
        state.prob = round(top_prob * 100)
        state.pred = 'This is a ' + top_pred
        state.img_path = var_val
    #print(var_name, var_val) DEBUG

app = Gui(page=index)

if __name__ == '__main__':
    app.run(use_reloader= True) #Argument so we dont have rerun app from terminal