Adding app.pyy

app.py

@@ -0,0 +1,44 @@
+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("Harveenchadha/low-light-image-enhancement", compile=False)
+#examples = ['examples/179.png', 'examples/493.png', 'examples/780.png']
+
+
+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)
+    # print(len(output))
+    # print([len(a) for a in output])
+    output_image = output[0] * 255.0
+    output_image = output_image.clip(0, 255)
+    output_image = output_image.reshape(
+        (np.shape(output_image)[0], np.shape(output_image)[1], 3)
+    )
+    output_image = np.uint32(output_image)
+    
+    # output_image = tf.cast((output[0, :, :, :] * 255), dtype=np.uint8)
+    
+    
+    # #output_image = Image.fromarray(output_image.numpy())
+    # output_image = output_image.numpy()
+    # print(output_image.shape())
+    return output_image
+    
+
+iface = gr.Interface(
+    fn=infer,
+    title="Low Light Image Enhancement",
+    description = "Keras Implementation of MIRNet model for light up the dark image 🌆🎆",
+    inputs=[gr.inputs.Image(label="image", type="pil")],
+    outputs=[gr.outputs.Image(label="image", type="numpy")],
+    #examples=examples,
+    article = "Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the keras example from <a href=\"https://keras.io/examples/vision/mirnet/\">Soumik Rakshit</a>",
+    ).launch(debug=True)