Implement application

app/main.py

@@ -0,0 +1,42 @@
+import streamlit as st
+from tensorflow import keras
+from PIL import Image
+import io
+
+import model
+
+
+def configure():
+    st.set_page_config(page_title="Low-light image enhancement")
+    if "model" not in st.session_state:
+        st.session_state["model"]: keras.Model = model.create_model()
+
+
+def describe_service():
+    st.title("Low-light image enhancement")
+    st.subheader("Just upload your low-light image and get the processed one!")
+
+
+def process_image():
+    uploaded_file: io.BytesIO = st.file_uploader(
+        label="Choose a file (you can upload new files without refreshing the page)",
+        type=["png", "jpg", "jpeg"],
+    )
+    if uploaded_file:
+        placeholder: st.delta_generator.DeltaGenerator = st.empty()
+        placeholder.info(
+            "The image is being processed. It may take some time. Wait, please..."
+        )
+        image: Image.Image = model.run_model(uploaded_file, st.session_state["model"])
+        placeholder.empty()
+        placeholder.image(image)
+
+
+def main():
+    describe_service()
+    process_image()
+
+
+if __name__ == "__main__":
+    configure()
+    main()

app/model.py

@@ -0,0 +1,30 @@
+from tensorflow import keras
+from PIL import Image
+import numpy as np
+import io
+
+
+def create_model() -> keras.Model:
+    return keras.models.load_model("model")
+
+
+def run_model(image: io.BytesIO, model: keras.Model) -> Image.Image:
+    image: Image.Image = Image.open(image)
+    width: int = image.size[0]
+    height: int = image.size[1]
+    image: Image.Image = image.resize((960, 640))
+    image: np.ndarray = keras.utils.img_to_array(image)
+    image: np.ndarray = image.astype("float32") / 255.0
+    image: np.ndarray = np.expand_dims(image, axis=0)
+    output: np.ndarray = model.predict(image)
+    output_image: np.ndarray = output[0] * 255.0
+    output_image: np.ndarray = output_image.clip(0, 255)
+    output_image: np.ndarray = output_image.reshape(
+        (np.shape(output_image)[0], np.shape(output_image)[1], 3)
+    )
+    output_image: np.ndarray = np.uint32(output_image)
+    output_image: np.ndarray = output_image.astype(np.uint8)
+    output_image: Image.Image = Image.fromarray(output_image)
+    output_image: Image.Image = output_image.resize((width, height))
+    output_image.save("user_data/output.jpg")
+    return output_image