Update app.py

app.py

@@ -15,10 +15,15 @@ from torch_mtcnn import show_bboxes
 def pipeline(img):
     bounding_boxes, landmarks = detect_faces(img)
     bb = [bounding_boxes[0,0], bounding_boxes[0,1], bounding_boxes[0,2], bounding_boxes[0,3]]
+    
+    if len(bb) == 0:
+        raise Exception("Didn't face any faces, try another image!")
+    if len(bb) > 1:
+        raise Exception("Found more than one face, try a profile picture with only one person in it!")
+    
     img_cropped = img.crop(bb)
-
+    
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
     model_fair_7 = torchvision.models.resnet34(pretrained=True)
     model_fair_7.fc = nn.Linear(model_fair_7.fc.in_features, 18)
     model_fair_7.load_state_dict(torch.load('res34_fair_align_multi_7_20190809.pt', map_location=torch.device('cpu')))
@@ -80,12 +85,15 @@ def pipeline(img):
     result.loc[result['age_preds_fair'] == 6, 'age'] = '50-59'
     result.loc[result['age_preds_fair'] == 7, 'age'] = '60-69'
     result.loc[result['age_preds_fair'] == 8, 'age'] = '70+'
-
-    return [result['gender'][0],result['age'][0]]   
+  
+    return "A " + result['gender'][0] + " in the age range of " + result['age'][0]
     
 def predict(image):
-  predictions = pipeline(image)
-  return "A " + predictions[0] + " in the age range of " + predictions[1]
+  try :
+      predictions = pipeline(image)
+  except Exception as e:
+      predictions = e
+  return predictions
 
 gr.Interface(
     predict,