Update app.py

app.py

@@ -1,72 +1,72 @@
-import tensorflow as tf
-import cv2
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-import streamlit as st
-from PIL import Image
-
-st.image(r"face-mask.png", width = 150)
-st.write("# Mask detector App")
-
-# load model
-@st.cache_resource
-def cache_model(model_add):
-    model = tf.keras.models.load_model(model_add)
-    return model
-
-model = cache_model("mask_detector.keras")
-
-labels = ['WithMask', 'WithoutMask']
-def predict(img,model,labels = labels):
-  # read_image
-#   img = cv2.imread(img_add)
-  # resize image to 224, 224
-  img = cv2.resize(img, (224,224))
-  # st.write("resize_cv2", img.shape)
-  # convert to RGB
-  img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-  img_expand = tf.expand_dims(img_rgb, axis = 0)
-  y_pred_prob = model.predict(img_expand)
-  y_pred_prob_round = np.round(y_pred_prob[0][0], 3)
-  st.write(y_pred_prob[0][0])
-  y_pred = int(np.round(y_pred_prob_round))
-  label = labels[y_pred]
-  return (label, y_pred_prob_round)
-
-def face_detect(img, model, labels = labels):
-#   img = cv2.imread(img_add)
-  face_detector=cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
-  img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-  faces = face_detector.detectMultiScale(img_gray)
-  if len(faces) > 0:
-    for face in faces:
-      x,y,w,h = face
-      x_max = x + w
-      y_max = y + h
-      face_crp = img[y:y_max+1, x:x_max+1]
-      label, prob = predict(face_crp,model,labels = labels)
-      if label == labels[0]:
-        cv2.rectangle(img, (x,y), (x_max, y_max), (0,255,0), 3)
-        cv2.putText(img, f"{label} -: {1 - prob}%", (x,y-10), cv2.FONT_HERSHEY_SIMPLEX,
-                     1, (0,255,0), 2)
-      else:
-        cv2.rectangle(img, (x,y), (x_max, y_max), (255,0,0), 3)
-        cv2.putText(img, f"{label} -: {prob}%", (x,y-10), cv2.FONT_HERSHEY_SIMPLEX,
-                     1, (255,0,0), 2)
-    return img
-
-
-# File uploader
-uploaded_file = st.file_uploader("upload image", type=["jpg", "jpeg", "png"])
-submit_btn = st.button("Submit")
-
-if submit_btn:
-  if uploaded_file:
-    img_array = np.array(Image.open(uploaded_file))
-    # st.write(img_array.shape)
-    result_img = face_detect(img_array, model)
-    # result_img_rgb = cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)
-    st.image(result_img)
-  else:
+import tensorflow as tf
+import cv2
+import numpy as np
+# import matplotlib.pyplot as plt
+# import seaborn as sns
+import streamlit as st
+from PIL import Image
+
+st.image(r"face-mask.png", width = 150)
+st.write("# Mask detector App")
+
+# load model
+@st.cache_resource
+def cache_model(model_add):
+    model = tf.keras.models.load_model(model_add)
+    return model
+
+model = cache_model("mask_detector.keras")
+
+labels = ['WithMask', 'WithoutMask']
+def predict(img,model,labels = labels):
+  # read_image
+#   img = cv2.imread(img_add)
+  # resize image to 224, 224
+  img = cv2.resize(img, (224,224))
+  # st.write("resize_cv2", img.shape)
+  # convert to RGB
+  img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+  img_expand = tf.expand_dims(img_rgb, axis = 0)
+  y_pred_prob = model.predict(img_expand)
+  y_pred_prob_round = np.round(y_pred_prob[0][0], 3)
+  st.write(y_pred_prob[0][0])
+  y_pred = int(np.round(y_pred_prob_round))
+  label = labels[y_pred]
+  return (label, y_pred_prob_round)
+
+def face_detect(img, model, labels = labels):
+#   img = cv2.imread(img_add)
+  face_detector=cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
+  img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+  faces = face_detector.detectMultiScale(img_gray)
+  if len(faces) > 0:
+    for face in faces:
+      x,y,w,h = face
+      x_max = x + w
+      y_max = y + h
+      face_crp = img[y:y_max+1, x:x_max+1]
+      label, prob = predict(face_crp,model,labels = labels)
+      if label == labels[0]:
+        cv2.rectangle(img, (x,y), (x_max, y_max), (0,255,0), 3)
+        cv2.putText(img, f"{label} -: {prob}%", (x,y-10), cv2.FONT_HERSHEY_SIMPLEX,
+                     1, (0,255,0), 2)
+      else:
+        cv2.rectangle(img, (x,y), (x_max, y_max), (255,0,0), 3)
+        cv2.putText(img, f"{label} -: {1 - prob}%", (x,y-10), cv2.FONT_HERSHEY_SIMPLEX,
+                     1, (255,0,0), 2)
+    return img
+
+
+# File uploader
+uploaded_file = st.file_uploader("upload image", type=["jpg", "jpeg", "png"])
+submit_btn = st.button("Submit")
+
+if submit_btn:
+  if uploaded_file:
+    img_array = np.array(Image.open(uploaded_file))
+    # st.write(img_array.shape)
+    result_img = face_detect(img_array, model)
+    # result_img_rgb = cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)
+    st.image(result_img)
+  else:
     st.write("Please upload an image")