File size: 1,487 Bytes
c4e3d46 504da7c c4e3d46 504da7c c4e3d46 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | 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
import gradio as gr
# Disable scientific notation for clarity
np.set_printoptions(suppress=True)
# Load the model
model = load_model("keras_model.h5", compile=False)
# Load the labels
class_names = open("labels.txt", "r").readlines()
# Create the array of the right shape to feed into the keras model
# The 'length' or number of images you can put into the array is
# determined by the first position in the shape tuple, in this case 1
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
def saluda(img):
# Replace this with the path to your image
image = img
# resizing the image to be at least 224x224 and then cropping from the center
size = (224, 224)
image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
# turn the image into a numpy array
image_array = np.asarray(image)
# Normalize the image
normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
# Load the image into the array
data[0] = normalized_image_array
# Predicts the model
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')
demo = gr.Interface(fn=saluda, inputs=imatge_entrada, outputs=["text", "text"])
demo.launch() |