Clean code

app/main.py

@@ -24,9 +24,7 @@ def process_image():
     )
     if uploaded_file:
         placeholder: st.delta_generator.DeltaGenerator = st.empty()
-        placeholder.info(
-            "The image is being processed. It may take some time. Wait, please..."
-        )
+        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)

app/model.py

@@ -9,23 +9,24 @@ def create_model() -> keras.Model:
     return from_pretrained_keras("keras-io/lowlight-enhance-mirnet")
 
 
-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]
+def run_model(image_bytes: io.BytesIO, model: keras.Model) -> Image.Image:
+    image: Image.Image = Image.open(image_bytes)
+    width: int
+    height: int
+    width, height = image.size
     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)
+    image_array: np.ndarray = keras.utils.img_to_array(image)
+    image_array: np.ndarray = image_array.astype("float32") / 255.0
+    image_array: np.ndarray = np.expand_dims(image_array, axis=0)
+    output: np.ndarray = model.predict(image_array)
+    output_image_array: np.ndarray = output[0] * 255.0
+    output_image_array: np.ndarray = output_image_array.clip(0, 255)
+    output_image_array: np.ndarray = output_image_array.reshape(
+        (np.shape(output_image_array)[0], np.shape(output_image_array)[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_array: np.ndarray = np.uint32(output_image_array)
+    output_image_array: np.ndarray = output_image_array.astype(np.uint8)
+    output_image: Image.Image = Image.fromarray(output_image_array)
     output_image: Image.Image = output_image.resize((width, height))
     output_image.save("user_data/output.jpg")
     return output_image