Update app.py

app.py

@@ -24,23 +24,38 @@ if file_name is not None:
 
     # capture image data for face analysis
     image_data = np.array(image)
-    # capture predictions from deepface emotion model
-    predictions = DeepFace.analyze(image_data, actions=['emotion'])
-    # ensure only the main prediction object is processed,
-    if len(predictions) > 1:
-        # when more than one face is detected by the backend,
-        faces = [(face, face['region']['w'] * face['region']['h']) for face in predictions]
-        # by using the predictions connected to the largest bounding box
-        new_predictions = sorted(faces, key=lambda x: x[1], reverse=True)[0][0]
-        emotion_dict = new_predictions['emotion']
-    else:
-        emotion_dict = predictions['emotion']
-    # capture desired prediction data
-    emotions = list(emotion_dict.keys())
-    probabilities = list(emotion_dict.values())
-
-    # display in the right column...
-    col2.header("Emotion Probabilities")
-    # ...each emotion category and its probability
-    for i in range(len(emotions)):
-        col2.subheader(f"{emotions[i]}: {probabilities[i]:.2f}%")
+    # define a list of backends in case face cannot be detected 
+    backends = ['opencv', 'mtcnn', 'retinaface', 'mediapipe', 'ssd']
+    # attempt tracker
+    attempt = 0
+
+    # retry loop
+    while True:
+        try:
+            # capture predictions from deepface emotion model
+            predictions = DeepFace.analyze(image_data, actions=['emotion'], detector_backend=backends[attempt])  
+            # ensure only the main prediction object is processed,
+            if len(predictions) > 1:
+                # when more than one face is detected by the backend,
+                faces = [(face, face['region']['w'] * face['region']['h']) for face in predictions]
+                # by using the predictions connected to the largest bounding box
+                new_predictions = sorted(faces, key=lambda x: x[1], reverse=True)[0][0]
+                emotion_dict = new_predictions['emotion']
+            else:
+                emotion_dict = predictions['emotion']
+            # capture desired prediction data
+            emotions = list(emotion_dict.keys())
+            probabilities = list(emotion_dict.values())
+            # display in the right column...
+            col2.header("Emotion Probabilities")
+            # ...each emotion category and its probability
+            for i in range(len(emotions)):
+                col2.subheader(f"{emotions[i]}: {probabilities[i]:.2f}%")
+            break
+        except Exception as e:
+            # if the analysis fails to detect a face, try a different backend
+            attempt += 1
+            if attempt < len(backends):
+                print(f"Retrying with backend `{backends[attempt]}` due to error: {str(e)}")
+            else:
+                print(f"Failed to analyze image after attempting all detector backends available. Please upload a new image.")