Create app.py

app.py

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+import numpy as np
+import streamlit as st
+#import gradio as gr
+from PIL import Image
+import tensorflow as tf
+import keras
+from huggingface_hub import from_pretrained_keras
+#dfvds
+
+model = from_pretrained_keras("hbpkillerX/low_light_img_enhancer", compile=False)
+
+#model= tf.saved_model.load("hbpkillerX/low_light_img_enhancer/")
+
+def autocontrast(tensor, cutoff=0):
+    tensor = tf.cast(tensor, dtype=tf.float32)
+    min_val = tf.reduce_min(tensor)
+    max_val = tf.reduce_max(tensor)
+    range_val = max_val - min_val
+    adjusted_tensor = tf.clip_by_value(tf.cast(tf.round((tensor - min_val - cutoff) * (255 / (range_val - 2 * cutoff))), tf.uint8), 0, 255)
+    return adjusted_tensor
+
+
+
+def infer(original_image):
+    image = keras.utils.img_to_array(original_image)
+    image = autocontrast(image)
+    image = image.astype("float32") / 255.0
+    image = np.expand_dims(image, axis=0)
+    output = model.predict(image)
+    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)
+    return output_image
+
+iface = st.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", shape=(960, 640))],
+    outputs="image",
+    cache_examples=True,
+)