Update app.py

app.py

@@ -37,18 +37,7 @@ MODEL_DIR = os.path.join(ROOT_DIR, "logs")
 # Local path to trained weights file
 COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
 os.system("pip install pycocotools==2.0.0")
-K.clear_session()
 
-if not os.path.exists(COCO_MODEL_PATH):
-    utils.download_trained_weights(COCO_MODEL_PATH)
-
-class InferenceConfig(coco.CocoConfig):
-    GPU_COUNT = 1
-    IMAGES_PER_GPU = 1
-config = InferenceConfig()
-
-model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)
-model.load_weights(COCO_MODEL_PATH, by_name=True)
 
  
 # Define a function to handle the GET request at `/generate`
@@ -63,7 +52,18 @@ def generate(path: str):
     can be found [here](<https://huggingface.co/philschmid/bart-large-cnn-samsum>).
     """
     # Use the pipeline to generate text from the given input text
+    K.clear_session()
+
+    if not os.path.exists(COCO_MODEL_PATH):
+        utils.download_trained_weights(COCO_MODEL_PATH)
+    
+    class InferenceConfig(coco.CocoConfig):
+        GPU_COUNT = 1
+        IMAGES_PER_GPU = 1
+    config = InferenceConfig()
     
+    model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)
+    model.load_weights(COCO_MODEL_PATH, by_name=True)
     r = requests.get(path, stream=True)
     img = Image.open(io.BytesIO(r.content)).convert('RGB') 
     open_cv_image = np.array(img)