Update app.py

app.py

@@ -7,10 +7,10 @@ import pickle
 from insightface.app import FaceAnalysis
 from concurrent.futures import ThreadPoolExecutor
 
-# Initialize your models only once
-app = FaceAnalysis(name='buffalo_l')
-app.prepare(ctx_id=0, det_size=(640, 640))
-swapper = insightface.model_zoo.get_model(os.path.join(os.getcwd(), 'inswapper_128.onnx'), download=False)
+# Initialize your models to None
+app = None
+swapper = None
+data_images = None
 
 # Load pickle file once and keep it in memory
 @st.cache_data
@@ -18,7 +18,21 @@ def load_data_images():
     with open(os.path.join(os.getcwd(), 'data_images.pkl'), 'rb') as file:
         return pickle.load(file)
 
-data_images = load_data_images()
+# Lazy initialization of data_images
+def get_data_images():
+    global data_images
+    if data_images is None:
+        data_images = load_data_images()
+    return data_images
+
+# Lazy initialization for models
+def prepare_models():
+    global app, swapper
+    if app is None:
+        app = FaceAnalysis(name='buffalo_l')
+        app.prepare(ctx_id=0, det_size=(640, 640))
+    if swapper is None:
+        swapper = insightface.model_zoo.get_model(os.path.join(os.getcwd(), 'inswapper_128.onnx'), download=False)
 
 def face_swapper(image_background, image_customer):
     face_customer = app.get(image_customer)[0]
@@ -30,7 +44,8 @@ def face_swapper(image_background, image_customer):
     return image_background
 
 def process(image):
-    images_background_encoding, images_background_contents = data_images['encodings'], data_images['content']
+    prepare_models()  # Prepare models only when required
+    images_background_encoding, images_background_contents = get_data_images()['encodings'], get_data_images()['content']
     image_loaded = face_recognition.load_image_file(image)
     face_encoding = face_recognition.face_encodings(image_loaded)[0]
     face_distances = face_recognition.face_distance(images_background_encoding, face_encoding)
@@ -58,14 +73,15 @@ else:
 
 if uploaded_file is not None:
     bytes_data = uploaded_file.getvalue()
-    if option=='File':
+    if option == 'File':
         st.image(uploaded_file)
-        
+
     if st.button('Process'):
-        with ThreadPoolExecutor() as executor:
-            future = executor.submit(process, uploaded_file)
-            image_output = future.result()
-            st.image(image_output)
+        with st.spinner('Processing...'):
+            with ThreadPoolExecutor() as executor:
+                future = executor.submit(process, uploaded_file)
+                image_output = future.result()
+                st.image(image_output)
 
 if image_output is not None:
     image_output_to_download = cv2.cvtColor(image_output, cv2.COLOR_BGR2RGB)