Update app.py

app.py

@@ -8,7 +8,7 @@ def read_files(files):
   no_face = []
   more_face = []
   for idx, image in enumerate(files):
-    image = face_recognition.load_image_file(image.name) #.split("/")[-1]
+    image = face_recognition.load_image_file(image.name)
     encoding = face_recognition.face_encodings(image)
     if len(encoding)==1:
       images.append(image)
@@ -21,31 +21,34 @@ def read_files(files):
 
 def read(profile, files):
   profile_encoding = face_recognition.face_encodings(profile)
-  images, encodings, no_face, more_face = read_files(files)
-  face_distances = []
-  true_images = []
-  false_images = []
-  for index in range(len(images)):
-    results = face_recognition.compare_faces(encodings[index], profile_encoding)
-    if results[0] == True:
-      face_distance = face_recognition.face_distance(profile_encoding, encodings[index])
-      face_distances.append(face_distance)
-      true_images.append(images[index])
-    else:
-      false_images.append(images[index])
-  score = len(face_distances)/(len(images)+len(no_face)+len(more_face))
-  text = ""
-  vals, counts = np.unique(face_distances, return_counts=True)
+  if len(profile_encoding)==1:
+    images, encodings, no_face, more_face = read_files(files)
+    face_distances = []
+    true_images = []
+    false_images = []
+    for index in range(len(images)):
+      results = face_recognition.compare_faces(encodings[index], profile_encoding)
+      if results[0] == True:
+        face_distance = face_recognition.face_distance(profile_encoding, encodings[index])
+        face_distances.append(face_distance)
+        true_images.append(images[index])
+      else:
+        false_images.append(images[index])
+    score = len(face_distances)/(len(images)+len(no_face)+len(more_face))
+    text = ""
+    vals, counts = np.unique(face_distances, return_counts=True)
 
-  if (np.std(face_distances)<0.01) or max(counts)>((len(images)+len(no_face)+len(more_face))/2):
-    text += "Most of the images look similar.\n\n"
-  if len(false_images)>0:
-    text += str(len(false_images)) + " of the images do not match with the profile picture.\n"
-  if len(no_face)>0:
-    text += "No faces were detected in " + str(len(no_face)) + " images.\n" 
-  if len(more_face)>0:
-    text += "More than one face were detected in " + str(len(more_face)) + " images." 
-  return {"Percentage of matched images":score}, text, true_images, false_images, no_face, more_face
+    if (np.std(face_distances)<0.01) or max(counts)>((len(images)+len(no_face)+len(more_face))/2):
+      text += "Most of the images look similar.\n\n"
+    if len(false_images)>0:
+      text += str(len(false_images)) + " of the images do not match with the profile picture.\n"
+    if len(no_face)>0:
+      text += "No faces were detected in " + str(len(no_face)) + " images.\n" 
+    if len(more_face)>0:
+      text += "More than one face were detected in " + str(len(more_face)) + " images." 
+    return {"Percentage of matched images":score}, text, true_images, false_images, no_face, more_face
+  else:
+    return {"Percentage of matched images":0}, "No faces or more than one faces are detected in the profile picture", [], [], [], []
 
 with gr.Blocks() as demo:
   gr.Markdown("""# Face Verification System""")