Update main.py

main.py

@@ -41,19 +41,20 @@ async def predict_api(
         # last_seen: Union[UploadFile, None] = File(None),
         last_seen: Optional[str] = Form(None),
 ):
+    # parameters
     total_time = time.time()
     start_time = time.time()
+    most_close = 0
+    out_img = None
+    diff_value = 0.5
+
+    # read image and predict
     image = read_image_file(await file.read())
-    print("Read image", time.time() - start_time)
-    start_time = time.time()
     results = model.predict(image, show=False)[0]
-    print("Model predict", time.time() - start_time)
     masks, boxes = results.masks, results.boxes
     area_image = image.width * image.height
-    most_close = 0
-    out_img = None
-    diff_value = 0.5
-    start_time = time.time()
+    
+    # select and crop face image
     if boxes is not None:
         for xyxy, conf, cls in zip(boxes.xyxy, boxes.conf, boxes.cls):
             if int(cls) != 0:
@@ -64,7 +65,8 @@ async def predict_api(
                 out_img = image.crop(tuple(box)).resize((64, 64))
                 most_close = area_rate
     print("Get face time", time.time() - start_time)
-    
+
+    # check with previous image if have
     start_time = time.time()
     if last_seen is not None:
         if type(last_seen) == str:
@@ -74,7 +76,8 @@ async def predict_api(
         if out_img is not None:
             diff_value = dist.euclidean(get_hist(out_img), get_hist(last_seen))
     print("Hist time", time.time() - start_time)
-    
+
+    # return results 
     start_time = time.time()
     print(f"Distance: {most_close}. Different value: {diff_value}")
     if most_close >= area_thres and diff_value >= 0.5: