Output restoration

app.py

@@ -10,7 +10,7 @@ from fer import FER  # Facial emotion recognition
 from concurrent.futures import ThreadPoolExecutor
 
 # -----------------------------
-# Global Asynchronous Executor & Caches
+# Asynchronous Processing Setup
 # -----------------------------
 executor = ThreadPoolExecutor(max_workers=4)
 latest_results = {
@@ -27,22 +27,24 @@ futures = {
 }
 
 def async_analyze(key, func, image):
-    """
-    Runs the heavy detection function 'func' in a background thread.
-    Returns the last computed result (if available) so that the output
-    FPS remains high even if the detection lags.
-    """
-    if futures[key] is None or futures[key].done():
-        futures[key] = executor.submit(func, image)
-    if futures[key].done():
+    # If a background task is done, update our cache.
+    if futures[key] is not None and futures[key].done():
         latest_results[key] = futures[key].result()
-    # Return latest result if available; otherwise, compute synchronously
-    return latest_results.get(key, func(image))
+        futures[key] = None
+    # If we already have a cached result, return it immediately and schedule a new update if none is running.
+    if latest_results[key] is not None:
+        if futures[key] is None:
+            futures[key] = executor.submit(func, image)
+        return latest_results[key]
+    # Otherwise, compute synchronously (blocking) to initialize the cache.
+    result = func(image)
+    latest_results[key] = result
+    futures[key] = executor.submit(func, image)
+    return result
 
 # -----------------------------
 # Initialize Models and Helpers
 # -----------------------------
-
 # MediaPipe Pose for posture analysis
 mp_pose = mp.solutions.pose
 pose = mp_pose.Pose()
@@ -65,9 +67,7 @@ emotion_detector = FER(mtcnn=True)
 # -----------------------------
 # Heavy (Synchronous) Analysis Functions
 # -----------------------------
-
 def _analyze_posture(image):
-    # Convert from PIL (RGB) to OpenCV BGR format
     frame = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
     output_frame = frame.copy()
     frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
@@ -132,18 +132,18 @@ def _analyze_faces(image):
     return annotated_image, f"Face Detection: {face_result}"
 
 # -----------------------------
-# Asynchronous (Fast) Analysis Functions
+# Asynchronous Wrappers for Each Analysis
 # -----------------------------
-def analyze_posture(image):
+def analyze_posture_async(image):
     return async_analyze("posture", _analyze_posture, image)
 
-def analyze_emotion(image):
+def analyze_emotion_async(image):
     return async_analyze("emotion", _analyze_emotion, image)
 
-def analyze_objects(image):
+def analyze_objects_async(image):
     return async_analyze("objects", _analyze_objects, image)
 
-def analyze_faces(image):
+def analyze_faces_async(image):
     return async_analyze("faces", _analyze_faces, image)
 
 # -----------------------------
@@ -183,7 +183,7 @@ body {
 # Create Individual Interfaces for Each Analysis
 # -----------------------------
 posture_interface = gr.Interface(
-    fn=analyze_posture,
+    fn=analyze_posture_async,
     inputs=gr.Image(sources=["webcam"], streaming=True, label="Capture Your Posture"),
     outputs=[gr.Image(type="numpy", label="Annotated Output"), gr.Textbox(label="Posture Analysis")],
     title="Posture Analysis",
@@ -192,7 +192,7 @@ posture_interface = gr.Interface(
 )
 
 emotion_interface = gr.Interface(
-    fn=analyze_emotion,
+    fn=analyze_emotion_async,
     inputs=gr.Image(sources=["webcam"], streaming=True, label="Capture Your Face"),
     outputs=[gr.Image(type="numpy", label="Annotated Output"), gr.Textbox(label="Emotion Analysis")],
     title="Emotion Analysis",
@@ -201,7 +201,7 @@ emotion_interface = gr.Interface(
 )
 
 objects_interface = gr.Interface(
-    fn=analyze_objects,
+    fn=analyze_objects_async,
     inputs=gr.Image(sources=["webcam"], streaming=True, label="Capture the Scene"),
     outputs=[gr.Image(type="numpy", label="Annotated Output"), gr.Textbox(label="Object Detection")],
     title="Object Detection",
@@ -210,7 +210,7 @@ objects_interface = gr.Interface(
 )
 
 faces_interface = gr.Interface(
-    fn=analyze_faces,
+    fn=analyze_faces_async,
     inputs=gr.Image(sources=["webcam"], streaming=True, label="Capture Your Face"),
     outputs=[gr.Image(type="numpy", label="Annotated Output"), gr.Textbox(label="Face Detection")],
     title="Face Detection",