Create app.py

app.py

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+import face_recognition
+import numpy as np
+import gradio as gr
+
+def read_files(files):
+  images = []
+  encodings = []
+  no_face = []
+  more_face = []
+  for idx, image in enumerate(files):
+    image = face_recognition.load_image_file(image.name)
+    encoding = face_recognition.face_encodings(image)
+    if len(encoding)==1:
+      images.append(image)
+      encodings.append(encoding[0])
+    elif len(encoding)==0:
+      no_face.append(image)
+    else:
+      more_face.append(image)
+  return images, encodings, no_face, more_face
+
+def read(profile, files):
+  profile_encoding = face_recognition.face_encodings(profile)
+  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
+  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""")
+  with gr.Row():
+    with gr.Column():
+      gr.Markdown("""### Upload the profile picture here""")
+      profile = gr.Image(label="Profile picture")
+    with gr.Column():
+      gr.Markdown("""### Upload the screenshots here""")
+      files = gr.File(file_count="directory", label="Screenshots")
+  btn = gr.Button(label="Verify").style(full_width=True) #show_progress=True
+  with gr.Row():
+    with gr.Column():
+      gr.Markdown("""### Report""")
+      text = gr.Textbox(show_label=False).style(container=False)
+      label = gr.Label(num_top_classes=1, show_label=False)
+    with gr.Tab("Matched images"):
+      gallery1 = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(columns=[5], height=3)
+    with gr.Tab("Not matched"):
+      gallery2 = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(columns=[5], height=3)
+    with gr.Tab("No faces detected"):
+      gallery3 = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(columns=[5], height=3)
+    with gr.Tab("More than one face detected"):
+      gallery4 = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(columns=[5], height=3)
+
+  btn.click(read, [profile, files], [label, text, gallery1, gallery2, gallery3, gallery4], show_progress=True, scroll_to_output=True)
+
+demo.launch()