Update app.py

app.py

@@ -8,21 +8,26 @@ import tensorflow as tf
 
 from typing import Annotated
 from fastapi import FastAPI, File, UploadFile
-# uvicorn main:app --reload
 
 
 def load_model() -> tf.keras.Model:
-    path = huggingface_hub.hf_hub_download('public-data/DeepDanbooru',
-                                           'model-resnet_custom_v3.h5')
-    model = tf.keras.models.load_model(path)
-    return model
+    path = huggingface_hub.hf_hub_download(
+        'public-data/DeepDanbooru',
+        'model-resnet_custom_v3.h5'
+    )
+
+    return tf.keras.models.load_model(path)
 
 
 def load_labels() -> list[str]:
-    path = huggingface_hub.hf_hub_download('public-data/DeepDanbooru',
-                                           'tags.txt')
+    path = huggingface_hub.hf_hub_download(
+        'public-data/DeepDanbooru',
+        'tags.txt'
+    )
+
     with open(path) as f:
         labels = [line.strip() for line in f.readlines()]
+
     return labels
 
 
@@ -31,30 +36,36 @@ labels = load_labels()
 app = FastAPI()
 
 
-def predict(image: PIL.Image.Image,
-            score_threshold: float) -> dict[str, float]:
+def predict(
+    image: PIL.Image.Image,
+    score_threshold: float
+) -> dict[str, float]:
     _, height, width, _ = model.input_shape
     image = np.asarray(image)
-    image = tf.image.resize(image,
-                            size=(height, width),
-                            method=tf.image.ResizeMethod.AREA,
-                            preserve_aspect_ratio=True)
+    image = tf.image.resize(
+        image,
+        size=(height, width),
+        method=tf.image.ResizeMethod.AREA,
+        preserve_aspect_ratio=True
+    )
     image = image.numpy()
     image = dd.image.transform_and_pad_image(image, width, height)
     image = image / 255.
     probs = model.predict(image[None, ...])[0]
     probs = probs.astype(float)
     res = dict()
+
     for prob, label in zip(probs.tolist(), labels):
         if prob < score_threshold:
             continue
         res[label] = prob
+
     return res
 
 
 @app.get("/")
 async def root():
-    return {"message": "Hello World"}
+    return {"message": "Application Has Been Running!!"}
 
 
 @app.post("/upload/")