Spaces:
Runtime error
Runtime error
File size: 1,279 Bytes
2387f46 5f0082d 83dae79 5f0082d 0014786 95c923f 7a29178 0014786 2387f46 9e0b2ba b1159c0 7a29178 afa9797 7a29178 1a9817c 7a29178 afa9797 1a9817c 77bbe85 1a9817c 7a29178 afa9797 7a29178 afa9797 7a29178 afa9797 7a29178 afa9797 b2e78ff 9e0b2ba 8861ba7 181183d 9e0b2ba |
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 |
from tensorflow import keras
from keras.models import load_model
from PIL import Image, ImageOps
import numpy as np
import gradio as gr
# Load the model
model = load_model('keras_model.h5')
def greet(name):
return "Hello " + prediction + "!!"
def predict(image):
# 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)
# Replace this with the path to your image
#resize the image to a 224x224 with the same strategy as in TM2:
#resizing the image to be at least 224x224 and then cropping from the center
size = (224, 224)
image = gr.inputs.Image()
image = ImageOps.fit(image, size, 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) / 127.0) - 1
# Load the image into the array
data[0] = normalized_image_array
# run the inference
prediction = model.predict(data)
gr.print(prediction)
return prediction
iface = gr.Interface(fn=predict, inputs="image", outputs="text")
iface.launch()
|