adding exception and logging making code more readable

Age and Sex Prediction/Web App/app.py

@@ -4,6 +4,7 @@
 import cv2
 import tensorflow as tf
 import sys
+import logging
 
 sys.stdout.reconfigure(encoding='utf-8')
 
@@ -16,7 +17,13 @@
 
 # loading the model
 # model is saved in static folder
-model = tf.keras.models.load_model('static/age_and_gender_prediction_model.h5')
+# Load the model
+try:
+    model = tf.keras.models.load_model('static/age_and_gender_prediction_model.h5')
+    logging.info("Model loaded successfully.")
+except Exception as e:
+    logging.error("Error loading model: %s", e)
+    model = None
 
 # gender mapping
 # if gender prediction = 0, the predition is MALE
@@ -28,39 +35,44 @@
 # This function finds the face area using
 # a haar cascade designed by OpenCV to detect the frontal face
 def extract_face():
-
-    img = cv2.imread('static/uploaded_img.jpg') # reading uploaded image
-    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # converted to grayscale
-
-    # OpenCV's CascadeClassifier to load a pre-trained Haar cascade for detecting frontal faces
-    haar_cascade = cv2.CascadeClassifier('static/haarcascade_frontalface_default.xml')
-    faces_rect = haar_cascade.detectMultiScale(gray_img, scaleFactor=1.1, minNeighbors=9)
-    extracted_faces = []
-
-    if len(faces_rect) == 0:
-        cv2.imwrite('static/extracted.jpg', img) # if no coordinates are detected, its only face image
-    else:
+    try:
+        img = cv2.imread('static/uploaded_img.jpg') # reading uploaded image
+        gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # converted to grayscale
+
+        # OpenCV's CascadeClassifier to load a pre-trained Haar cascade for detecting frontal faces
+        haar_cascade = cv2.CascadeClassifier('static/haarcascade_frontalface_default.xml')
+        faces_rect = haar_cascade.detectMultiScale(gray_img, scaleFactor=1.1, minNeighbors=9)
         extracted_faces = []
-        for (x, y, w, h) in faces_rect:
-            face = img[y:y+h, x:x+w]
-            extracted_faces.append(face)
-        concatenated_faces = cv2.hconcat(extracted_faces)
-        cv2.imwrite('static/extracted.jpg', concatenated_faces) # face extracted image - saved as extracted.jpg
 
-    if len(faces_rect) != 0:
-        for (x, y, w, h) in faces_rect:
-            cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), thickness=2)
-        cv2.imwrite('static/uploaded_img.jpg', img) # original uploaded image where face is marked
+        if len(faces_rect) == 0:
+            cv2.imwrite('static/extracted.jpg', img) # if no coordinates are detected, its only face image
+        else:
+            extracted_faces = []
+            for (x, y, w, h) in faces_rect:
+                face = img[y:y+h, x:x+w]
+                extracted_faces.append(face)
+            concatenated_faces = cv2.hconcat(extracted_faces)
+            cv2.imwrite('static/extracted.jpg', concatenated_faces) # face extracted image - saved as extracted.jpg
+
+        if len(faces_rect) != 0:
+            for (x, y, w, h) in faces_rect:
+                cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), thickness=2)
+            cv2.imwrite('static/uploaded_img.jpg', img) # original uploaded image where face is marked
+    except Exception as e:
+        logging.error("Error occured while extracting face",e)
 
 # Function to extract features from the image
 # This is the pre-process technique
 # which was applied on original dataset before training
 def extract_features(images):
-    img = cv2.imread(images, cv2.IMREAD_GRAYSCALE)  # Read image in grayscale
-    img = cv2.resize(img, (128, 128))  # Resize image
-    img = np.array(img)  # Convert image to numpy array
-    features = img.reshape(1, 128, 128, 1)  # Reshape to match input shape
-    return features
+    try:
+        img = cv2.imread(images, cv2.IMREAD_GRAYSCALE)  # Read image in grayscale
+        img = cv2.resize(img, (128, 128))  # Resize image
+        img = np.array(img)  # Convert image to numpy array
+        features = img.reshape(1, 128, 128, 1)  # Reshape to match input shape
+        return features
+    except Exception as e:
+        logging.error("error occured during extracting feature",e)
 
 # Function to predict gender and age
 def predict_result():