new

app.py

@@ -13,6 +13,9 @@ import torch
 import json
 import requests
 
+# --- Model cache variables ---
+distraction_model_cache = None
+
 def smooth_values(history, current_value, window_size=5):
     if current_value is not None:
         if isinstance(current_value, np.ndarray):
@@ -101,7 +104,7 @@ EYE_CLOSURE_THRESHOLD = 10
 HEAD_STABILITY_THRESHOLD = 0.05
 DISTRACTION_CONF_THRESHOLD = 0.1
 
-@spaces.GPU(duration=30)  # Set duration to 30 seconds for real-time processing
+@spaces.GPU(duration=60)  # Extended duration to 60 seconds for longer streaming
 def analyze_video(input_video):
     cap = cv2.VideoCapture(input_video)
     local_gaze_predictor = GazePredictor(GAZE_MODEL_PATH)
@@ -229,7 +232,7 @@ def analyze_video(input_video):
         out.release()
     return temp_path
 
-@spaces.GPU(duration=30)  # Set duration to 30 seconds for real-time processing
+@spaces.GPU(duration=60)  # Extended duration to 60 seconds for longer streaming
 def analyze_distraction_video(input_video):
     cap = cv2.VideoCapture(input_video)
     if not cap.isOpened():
@@ -243,8 +246,10 @@ def analyze_distraction_video(input_video):
 
     fps = cap.get(cv2.CAP_PROP_FPS) or 30
 
-    local_distraction_model = YOLO(DISTRACTION_MODEL_PATH)
-    local_distraction_model.to('cpu')
+    global distraction_model_cache
+    if distraction_model_cache is None:
+        distraction_model_cache = YOLO(DISTRACTION_MODEL_PATH)
+        distraction_model_cache.to('cpu')
 
     while True:
         ret, frame = cap.read()
@@ -252,7 +257,7 @@ def analyze_distraction_video(input_video):
             break
 
         try:
-            results = local_distraction_model(frame, conf=DISTRACTION_CONF_THRESHOLD, verbose=False)
+            results = distraction_model_cache(frame, conf=DISTRACTION_CONF_THRESHOLD, verbose=False)
 
             display_text = "safe driving"
             alarm_action = None
@@ -301,9 +306,10 @@ def analyze_distraction_video(input_video):
         out.release()
     return temp_path
 
-@spaces.GPU(duration=30)  # Set duration to 30 seconds for real-time processing
+@spaces.GPU(duration=60)  # Extended duration to 60 seconds for longer streaming
 def process_distraction_frame(frame):
     global stop_distraction_processing
+    global distraction_model_cache
     
     if stop_distraction_processing:
         return np.zeros((480, 640, 3), dtype=np.uint8)
@@ -311,12 +317,13 @@ def process_distraction_frame(frame):
     if frame is None:
         return np.zeros((480, 640, 3), dtype=np.uint8)
     
-    local_distraction_model = YOLO(DISTRACTION_MODEL_PATH)
-    local_distraction_model.to('cpu')
+    if distraction_model_cache is None:
+        distraction_model_cache = YOLO(DISTRACTION_MODEL_PATH)
+        distraction_model_cache.to('cpu')
 
     try:
         # Run distraction detection model
-        results = local_distraction_model(frame, conf=DISTRACTION_CONF_THRESHOLD, verbose=False)
+        results = distraction_model_cache(frame, conf=DISTRACTION_CONF_THRESHOLD, verbose=False)
         
         display_text = "safe driving"
         alarm_action = None
@@ -396,7 +403,7 @@ def terminate_distraction_stream():
     stop_distraction_processing = True
     return "Distraction Processing Terminated."
 
-@spaces.GPU(duration=30)  # Set duration to 30 seconds for real-time processing
+@spaces.GPU(duration=60)  # Extended duration to 60 seconds for longer streaming
 def process_gaze_frame(frame):
     global gaze_history, head_history, ear_history, stable_gaze_time, stable_head_time
     global eye_closed_time, blink_count, start_time, is_unconscious, frame_count_webcam, stop_gaze_processing