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import streamlit as st
import numpy as np
from PIL import Image, ImageOps
import tensorflow as tf
from tensorflow import keras
import io
# ----------------------------
# Load the model
# ----------------------------
@st.cache_resource
def load_model():
model = keras.models.load_model(
"src/naxi_lowlight.keras",
custom_objects={
"charbonnier_loss": lambda y_true, y_pred: tf.reduce_mean(tf.sqrt(tf.square(y_true - y_pred) + 1e-6)),
"psnr_metric": lambda y_true, y_pred: tf.image.psnr(y_pred, y_true, max_val=1.0)
}
)
return model
model = load_model()
# ----------------------------
# Inference function
# ----------------------------
def enhance_image_pil(pil_img):
image = keras.utils.img_to_array(pil_img).astype("float32") / 255.0
image = np.expand_dims(image, axis=0)
output = model.predict(image)[0]
output = np.clip(output * 255.0, 0, 255).astype(np.uint8)
return Image.fromarray(output)
# ----------------------------
# Streamlit UI
# ----------------------------
st.set_page_config(page_title="NaxiLowLight Enhancement", layout="centered")
st.title("🌙 NaxiLowLight: Low-Light Image Enhancer")
st.write("Upload a low-light image to enhance it using a deep learning model.")
uploaded_file = st.file_uploader("Choose a low-light image", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Load image
image = Image.open(uploaded_file).convert("RGB")
# Enhance
st.write("✨ Enhancing image...")
enhanced_image = enhance_image_pil(image)
autocontrast_image = ImageOps.autocontrast(image)
# Show results
st.write("📷 **Comparison:**")
col1, col2, col3 = st.columns(3)
with col1:
st.image(image, caption="Original", use_column_width=True)
with col2:
st.image(autocontrast_image, caption="Autocontrast", use_column_width=True)
with col3:
st.image(enhanced_image, caption="NaxiLowLight Enhanced", use_column_width=True)
# Download
buf = io.BytesIO()
enhanced_image.save(buf, format="PNG")
byte_im = buf.getvalue()
st.download_button(
label="⬇️ Download Enhanced Image",
data=byte_im,
file_name="enhanced_image.png",
mime="image/png"
)
else:
st.info("Upload a PNG or JPG image to get started.")
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