Update app.py

app.py

@@ -1,10 +1,10 @@
-# Import libraries
 import cv2
 from ultralytics import YOLO
-from huggingface_hub import hf_hub_download  # For Hugging Face model download
+from huggingface_hub import hf_hub_download  
 import gradio as gr
+import spaces 
+
 
-# Define constants for ASL letters with color for bounding boxes
 ASL_COLORS = {
     0: (191, 100, 21),   # A
     1: (2, 62, 115),     # B
@@ -35,13 +35,12 @@ ASL_COLORS = {
 }
 BOX_PADDING = 2
 
-# Define the function to download the models dynamically
+
 def download_model(model_id):
-    # Use Hugging Face's hf_hub_download to download models
     model_path = hf_hub_download(repo_id="atalaydenknalbant/asl-yolo-models", filename=model_id, local_dir="./")
     return model_path
 
-# Function for detecting objects (ASL letters) in the image
+@spaces.GPU  
 def detect(image_path, model_id):
     """
     Output inference image with bounding boxes and ASL letter predictions.
@@ -59,8 +58,8 @@ def detect(image_path, model_id):
         return image
     
     # Predict on image
-    results = detection_model.predict(source=image, conf=0.2, iou=0.8)  # Predict on image
-    boxes = results[0].boxes  # Get bounding boxes
+    results = detection_model.predict(source=image, conf=0.2, iou=0.8) 
+    boxes = results[0].boxes  
 
     if len(boxes) == 0:
         return image
@@ -102,7 +101,7 @@ def detect(image_path, model_id):
 
     return image
 
-# Gradio interface
+
 model_filenames = [
     "yolov10s.pt",
     "yolov10x.pt",
@@ -112,10 +111,10 @@ model_filenames = [
     "yolov9s.pt"
 ]
 
-iface = gr.Interface(fn=detect, 
+asl_app = gr.Interface(fn=detect, 
                      inputs=[gr.Image(label="Upload ASL letter image", type="filepath"), 
                              gr.Dropdown(label="Model", choices=model_filenames, value=model_filenames[0])],
                      outputs="image")
 
 # Launch the interface
-iface.launch()
+asl_app.launch()