app.py

import tensorflow as tf
import gradio as gr

import numpy as np
from keras.models import load_model
from gradio.components import Image
from model import get_model


def autocontrast(tensor, cutoff=0):
    tensor = tf.cast(tensor, dtype=tf.float32)
    min_val = tf.reduce_min(tensor)
    max_val = tf.reduce_max(tensor)
    range_val = max_val - min_val
    adjusted_tensor = tf.clip_by_value(tf.cast(tf.round((tensor - min_val - cutoff) * (255 / (range_val - 2 * cutoff))), tf.uint8), 0, 255)
    return adjusted_tensor

def read_image(image):
    image = autocontrast(image)
    image.set_shape([None, None, 3])
    image = tf.cast(image, dtype=tf.float32) / 255
    return image




model = get_model()
model.load_weights("./model.h5")

def enhance_image(input_image):
    # Process the input image using the loaded model
    image = read_image(input_image)
    image = np.expand_dims(image, axis=0)
    output_image = model.predict(image)
    generated_image = np.squeeze(output_image, axis=0)
    generated_image = tf.keras.preprocessing.image.array_to_img(generated_image)
    # Return the output image
    return generated_image

inputs = Image()
outputs = Image()
app = gr.Interface(enhance_image, inputs, outputs)
app.launch()