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main.py
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main.py
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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("baseline_charles.keras")
def predict_image(model, path_to_img):
img = Image.open(path_to_img)
img = img.convert("RGB")
img = img.resize((32, 32))
data = np.asarray(img)
data = data / 255
probs = model.predict(np.array([data])[:1])
top_prob = probs.max()
top_pred = class_names[np.argmax(probs)]
return top_prob, top_pred
content = ""
img_path = "placeholder_image.png"
prob = 0
pred = ""
index = """
<|text-center|
<|{"logo.png"}|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)
app = Gui(page=index)
if __name__ == "__main__":
app.run(use_reloader=True)