Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,60 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+import tensorflow as tf
+from tensorflow import keras
+import numpy as np
+from PIL import Image
+import io
+import os
+
+from huggingface_hub import hf_hub_download
+
+# Set page configuration
+st.set_page_config(page_title="NaxiLowLight Enhancer", layout="centered")
+
+# Title and description
+st.title("🌙 NaxiLowLight - Low-Light Image Enhancer")
+st.write("Upload a low-light image and enhance it using a model from Hugging Face Hub.")
+
+# -------------------------------
+# 🔽 Step 1: Download model from HF
+# -------------------------------
+@st.cache_resource
+def load_model_from_huggingface():
+    repo_id = "NEXAS/low_light_enhance"
+    filename = "model.h5"
+    model_path = hf_hub_download(repo_id=repo_id, filename=filename)
+    return keras.models.load_model(model_path, compile=False)
+
+# Load model
+model = load_model_from_huggingface()
+
+# -------------------------------
+# 🧠 Enhance function
+# -------------------------------
+def enhance_image(image: Image.Image) -> Image.Image:
+    image_resized = image.resize((256, 256))  # Resize for speed
+    img_array = keras.utils.img_to_array(image_resized) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    output = model.predict(img_array)[0]
+    output = np.clip(output * 255.0, 0, 255).astype(np.uint8)
+    return Image.fromarray(output)
+
+# -------------------------------
+# 📤 Upload & Enhance UI
+# -------------------------------
+uploaded_file = st.file_uploader("📤 Upload a low-light image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="🖼️ Original Image", use_column_width=True)
+
+    if st.button("✨ Enhance Image"):
+        with st.spinner("Enhancing image..."):
+            enhanced = enhance_image(image)
+
+        st.image(enhanced, caption="🚀 Enhanced by NaxiLowLight", use_column_width=True)
+
+        # Download
+        img_bytes = io.BytesIO()
+        enhanced.save(img_bytes, format="PNG")
+        st.download_button("💾 Download Enhanced Image", data=img_bytes.getvalue(), file_name="enhanced.png", mime="image/png")