Update app.py

app.py

@@ -8,7 +8,6 @@ from skimage.feature import hog
 import joblib
 import numpy as np
 
-# Preprocessing for Siamese Model
 def preprocess_image_siamese(img):
     transform = transforms.Compose([
         transforms.Resize((224, 224)),
@@ -17,18 +16,15 @@ def preprocess_image_siamese(img):
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
     return transform(img)
 
-# Preprocessing for SVM model (converting to grayscale)
 def preprocess_image_svm(img):
     img = cv2.resize(img, (224, 224))
     img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
     return img
 
-# Extract HOG Features
 def extract_hog_features(img):
     hog_features = hog(img, orientations=9, pixels_per_cell=(16, 16), cells_per_block=(4, 4))
     return hog_features
 
-# Detect faces using MTCNN
 def get_face(img):
     detector = MTCNN()
     faces = detector.detect_faces(img)
@@ -39,7 +35,6 @@ def get_face(img):
         return img[y1:y2, x1:x2]
     return None
 
-# Function to verify face (either HOG-SVM or Siamese model)
 def verify(image, model, person):
     
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_image:
@@ -51,7 +46,7 @@ def verify(image, model, person):
     face = get_face(image)
 
     if face is not None:
-        if model_type == "HOG-SVM":
+        if model == "HOG-SVM":
             with open(f'./svm_{lower(person)}.pkl', 'rb') as f:
                 svm = joblib.load(f)
             with open(f'./pca_{lower(person)}.pkl', 'rb') as f:
@@ -70,9 +65,8 @@ def verify(image, model, person):
             else:
                 st.write("Not Match")
     else:
-        st.write("Face not detected in one or both images")
+        st.write("Face not detected")
 
-# Main function to handle Streamlit interaction
 def main():
     st.title("Real-time Face Verification App")