Create app.py

app.py

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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()