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import gradio as gr
import numpy as np
import tensorflow as tf
import PIL
import os
'''
def sepia(input_img):
sepia_filter = np.array([[.393, .769, .189],
[.349, .686, .168],
[.272, .534, .131]])
sepia_img = input_img.dot(sepia_filter.T)
sepia_img /= sepia_img.max()
return sepia_img
'''
def normalize_img(img):
img = tf.cast(img, dtype=tf.float32)
# Map values in the range [-1, 1]
return (img / 127.5) - 1.0
def predict_and_save(img, generator_model):
img = normalize_img(img)
prediction = generator_model(img, training=False)[0].numpy()
prediction = (prediction * 127.5 + 127.5).astype(np.uint8)
im = PIL.Image.fromarray(prediction)
return im
def run(image_path):
model = tf.keras.models.load_model('pretrained')
'''
img = tf.keras.preprocessing.image.load_img(
image_path, target_size=(256, 256)
)
img_array = tf.keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0)
'''
#predict_and_save(img_array, model)
img_array = tf.expand_dims(image_path, 0)
im = predict_and_save(img_array, model)
return im
iface = gr.Interface(run, gr.inputs.Image(shape=(256, 256)), "image")
iface.launch(share = True) |