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import gradio as gr
import numpy as np
import tensorflow as tf
import keras
from huggingface_hub import from_pretrained_keras
from PIL import Image
import io
import gc
# Load MIRNet model from Hugging Face
model = from_pretrained_keras("keras-io/lowlight-enhance-mirnet", compile=False)
# TensorFlow graph mode for performance
@tf.function
def enhance_image(img, passes):
for _ in tf.range(passes):
img = model(img)
return img
# Inference function
def process_image(input_img: Image.Image, passes: int):
try:
# Convert to RGB, Resize, Normalize
input_img = input_img.convert("RGB").resize((256, 256), Image.LANCZOS)
img_array = keras.preprocessing.image.img_to_array(input_img).astype("float32") / 255.0
img_array = np.expand_dims(img_array, axis=0)
# Enhance
output = enhance_image(tf.convert_to_tensor(img_array), passes)
enhanced_img = (output[0].numpy() * 255.0).clip(0, 255).astype('uint8')
result_img = Image.fromarray(enhanced_img, "RGB")
# Return both images for preview
return result_img
finally:
# Memory cleanup
del img_array, output, enhanced_img
gc.collect()
# Gradio Interface
title = "🌃 Low-Light Image Enhancer"
description = """
Boost visibility of dark images using deep learning (MIRNet)<br>
Built for Bharatiya Antariksh Hackathon 2025 🚀 – Team <strong>CodeKarma</strong>
"""
demo = gr.Interface(
fn=process_image,
inputs=[
gr.Image(type="pil", label="📷 Upload Low-Light Image (JPG/PNG)"),
gr.Slider(minimum=1, maximum=3, value=1, step=1, label="🔁 Enhancement Passes")
],
outputs=[
gr.Image(type="pil", label="✨ Enhanced Image")
],
title=title,
description=description,
allow_flagging="never",
theme="soft",
)
if __name__ == "__main__":
demo.launch()
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