Update app.py

app.py

@@ -14,11 +14,15 @@ import zipfile
 import logging
 
 # unzip vegetable images
-with zipfile.ZipFile("Vegetable Images.zip", 'r') as zip_ref:
-    zip_ref.extractall('.')
-    logging.debug('unzipped images')
+def unzip_images():
+    with zipfile.ZipFile("Vegetable Images.zip", 'r') as zip_ref:
+        zip_ref.extractall('.')
+        logging.debug('unzipped images')
+
+if not os.path.exists('Vegetable Images/'):
+    unzip_images()
+
 
-@st.cache_resource
 class ImageVectorizer:
     '''
     Get vector representation of an image using VGG19 model fine tuned on vegetable images for classification
@@ -43,6 +47,7 @@ class ImageVectorizer:
         test_image = np.array([test_image])
         return model(test_image).numpy()[0]
 
+
 @st.cache_resource
 def get_milvus_collection():
     uri = os.environ.get("URI")
@@ -102,14 +107,18 @@ def find_similar_images(img_path: str, top_n: int=15):
 
 def delete_file(path_: str):
     if os.path.exists(path_):
-        rmtree(path=path_, ignore_errors=True)
+        os.remove(path_)
 
 
-def process_input_image(img_url):
+@st.cache_resource
+def get_upload_path():
     upload_file_path = os.path.join('.', 'uploads')
-    os.makedirs(upload_file_path, exist_ok=True)
     upload_filename = "input.jpg"
     upload_file_path = os.path.join(upload_file_path, upload_filename)
+    return upload_file_path
+
+def process_input_image(img_url):
+    upload_file_path = get_upload_path()
     headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36'}
     r = get(img_url, headers=headers)
     with open(upload_file_path, "wb") as file: