import numpy as np
import gradio as gr
from PIL import Image
import tensorflow as tf
from tensorflow import keras
from huggingface_hub import from_pretrained_keras

model = from_pretrained_keras("keras-io/low-light-image-enhancement", compile=False)
examples = ['got2.png', 'gotj.png', 'goti.png']

def get_enhanced_image(data, output):
    r1 = output[:, :, :, :3]
    r2 = output[:, :, :, 3:6]
    r3 = output[:, :, :, 6:9]
    r4 = output[:, :, :, 9:12]
    r5 = output[:, :, :, 12:15]
    r6 = output[:, :, :, 15:18]
    r7 = output[:, :, :, 18:21]
    r8 = output[:, :, :, 21:24]
    x = data + r1 * (tf.square(data) - data)
    x = x + r2 * (tf.square(x) - x)
    x = x + r3 * (tf.square(x) - x)
    enhanced_image = x + r4 * (tf.square(x) - x)
    x = enhanced_image + r5 * (tf.square(enhanced_image) - enhanced_image)
    x = x + r6 * (tf.square(x) - x)
    x = x + r7 * (tf.square(x) - x)
    enhanced_image = x + r8 * (tf.square(x) - x)
    return enhanced_image

def infer(original_image):
    image = keras.preprocessing.image.img_to_array(original_image)
    image = image.astype("float32") / 255.0
    image = np.expand_dims(image, axis=0)
    output = model.predict(image)
    output = get_enhanced_image(image, output)
    output_image = tf.cast(output[0, :, :, :] * 255, dtype=tf.uint8).numpy()
    return Image.fromarray(output_image)

iface = gr.Interface(
    fn=infer,
    title="Zero-DCE for Low-Light Image Enhancement",
    description="Implementing Zero-Reference Deep Curve Estimation for low-light image enhancement.",
    inputs=gr.Image(label="Original Image", type="pil"),
    outputs=gr.Image(label="Enhanced Image"),
    examples=examples,
    article="**Original Author**: [Soumik Rakshit](https://github.com/soumik12345) <br>**HF Contribution**: [Harveen Singh Chadha](https://github.com/harveenchadha)<br>",
).launch(debug=True, enable_queue=False, cache_examples=True)