Updated app

app.py

@@ -26,28 +26,29 @@ warnings.filterwarnings("ignore")
 
 device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
 
-# If required, create a face detection pipeline using MTCNN:
-mtcnn = MTCNN(
-    image_size=160, margin=40, min_face_size=20,
-    thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True,
-    device=device
-)
-
-mtcnn2 = MTCNN(
-    image_size=160, margin=40, min_face_size=20,
-    thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=False,
-    device=device
-)
-
-# Create an inception resnet (in eval mode):
-resnet = InceptionResnetV1(pretrained='vggface2').eval().to(device)
-
-# Define the transformation to preprocess the images
-preprocess = transforms.Compose([
-    transforms.Resize((160, 160)),
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-])
+with st.spinner('Loading the models...'):
+    # If required, create a face detection pipeline using MTCNN:
+    mtcnn = MTCNN(
+        image_size=160, margin=40, min_face_size=20,
+        thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True,
+        device=device
+    )
+
+    mtcnn2 = MTCNN(
+        image_size=160, margin=40, min_face_size=20,
+        thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=False,
+        device=device
+    )
+
+    # Create an inception resnet (in eval mode):
+    resnet = InceptionResnetV1(pretrained='vggface2').eval().to(device)
+
+    # Define the transformation to preprocess the images
+    preprocess = transforms.Compose([
+        transforms.Resize((160, 160)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+    ])
 
 def extract_features(img):
     img = img.convert('RGB')
@@ -66,7 +67,10 @@ def extract_features(img):
 with open("models/svr.p", "rb") as f:
     lr = pickle.load(f)
 
-img_file_buffer = st.camera_input("Take a picture")
+st.markdown("<center><h1>Know Your BMI</h1></center>", unsafe_allow_html=True)
+st.caption("<center>Click a photo and the underlying Machine Learning model will predict your BMI</center>", unsafe_allow_html=True)
+
+img_file_buffer = st.camera_input("Click a photo and the underlying Machine Learning model will predict your BMI", label_visibility="hidden")
 
 if img_file_buffer is not None:
     # To read image file buffer as a PIL Image:
@@ -76,5 +80,5 @@ if img_file_buffer is not None:
     st.image(detected_image, caption="Detected Face")
     
     embeddings = extract_features(img)
-    bmi = round(lr.predict([embeddings])[0], 2) - 4 # Because the model is consistently over predicting the BMI values
+    bmi = round(lr.predict([embeddings])[0], 2) - 4
     st.write(f"Your BMI is {bmi}")