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import gradio as gr
from keras.models import load_model # TensorFlow is required for Keras to work
from PIL import Image, ImageOps # Install pillow instead of PIL
import numpy as np
model = load_model("keras_model.h5", compile=False)
class_names = open("labels.txt", "r").readlines()
def pred(img):
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
image = img
size = (224, 224)
image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
image_array = np.asarray(image)
normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
data[0] = normalized_image_array
prediction = model.predict(data)
index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = prediction[0][index]
return class_name[2:], confidence_score
imatge_entrada = gr.Image(type='pil')
etiqueta = gr.Textbox(label='Això és...')
percentatge = gr.Textbox(label='Probabilitat:')
demo = gr.Interface(
fn=pred,
inputs=imatge_entrada,
outputs=[etiqueta, percentatge],
allow_flagging="never",
css="footer {visibility: hidden}",
theme=gr.themes.Soft(),
examples=["1024_2000.jpeg"])
demo.launch(debug=True) |