Add async processing

Improve performance

Improve face detection parameters

src/classification.py

@@ -9,11 +9,10 @@ import tempfile
 class Classification:
     def __init__(self, model_choice):
         self.model_choice = model_choice
-        self.classes_path = "src/model_data/cls_classes.txt"
+        self.classes_path = "src/cls_classes.txt"
         self.input_shape = (224, 224)
         self.alpha = 0.25
 
-        # Download the model from Hugging Face
         cache_dir = os.path.join(tempfile.gettempdir(), "hf_cache")
         os.makedirs(cache_dir, exist_ok=True)
         self.model_path = hf_hub_download(
@@ -22,10 +21,8 @@ class Classification:
             cache_dir=cache_dir
         )
 
-        # Load class names
+        # Load class names and model
         self.class_names, self.num_classes = get_classes(self.classes_path)
-
-        # Load model
         self.load_model()
 
     def load_model(self):

src/model_data/mobilenet_2_5_224_tf_no_top.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:dbdb03ee2a22fd895301636cd328b234bb3a9952358f436d82f46b81e0d5b0bf
-size 2108140

src/model_data/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bfe5187d0a272bed55ba430631598124cff8e880b98d38c9e56c8d66032abdc1
-size 58889256

src/streamlit_app.py

@@ -82,7 +82,7 @@ if __name__ == '__main__':
             faces = face_cascade.detectMultiScale(
                 img_gray,
                 scaleFactor=1.1,
-                minNeighbors=1,
+                minNeighbors=3,
                 minSize=(30, 30)
             )
             return img, img_gray, faces
@@ -198,6 +198,7 @@ if __name__ == '__main__':
         video_processor_factory=EmotionRecognitionProcessor,
         rtc_configuration=RTC_CONFIGURATION,
         media_stream_constraints={"video": True, "audio": False},
+        async_processing=True,
     )
 
     history = list(shared_emotion_history)