Create app.py

app.py

@@ -0,0 +1,57 @@
+import os
+import urllib.request
+
+if not os.path.exists("biden.jpg"):
+    urllib.request.urlretrieve("https://github.com/ageitgey/face_recognition/blob/master/examples/biden.jpg?raw=true")
+    urllib.request.urlretrieve("https://github.com/ageitgey/face_recognition/blob/master/examples/obama.jpg?raw=true")
+    urllib.request.urlretrieve("https://github.com/ageitgey/face_recognition/blob/master/examples/obama2.jpg?raw=true")
+
+import face_recognition
+
+# Often instead of just checking if two faces match or not (True or False), it's helpful to see how similar they are.
+# You can do that by using the face_distance function.
+
+# The model was trained in a way that faces with a distance of 0.6 or less should be a match. But if you want to
+# be more strict, you can look for a smaller face distance. For example, using a 0.55 cutoff would reduce false
+# positive matches at the risk of more false negatives.
+
+# Note: This isn't exactly the same as a "percent match". The scale isn't linear. But you can assume that images with a
+# smaller distance are more similar to each other than ones with a larger distance.
+
+# Load some images to compare against
+known_obama_image = face_recognition.load_image_file("obama.jpg")
+known_biden_image = face_recognition.load_image_file("biden.jpg")
+
+# Get the face encodings for the known images
+obama_face_encoding = face_recognition.face_encodings(known_obama_image)[0]
+biden_face_encoding = face_recognition.face_encodings(known_biden_image)[0]
+
+known_encodings = [
+    obama_face_encoding,
+    biden_face_encoding
+]
+
+# Load a test image and get encondings for it
+image_to_test = face_recognition.load_image_file("obama2.jpg")
+image_to_test_encoding = face_recognition.face_encodings(image_to_test)[0]
+
+# See how far apart the test image is from the known faces
+face_distances = face_recognition.face_distance(known_encodings, image_to_test_encoding)
+
+
+import gradio as gr
+
+def greet(name):
+    ret = ""
+    for i, face_distance in enumerate(face_distances):
+        ret += "The test image has a distance of {:.2} from known image #{}".format(face_distance, i)
+        ret += "\n"
+        ret += "- With a normal cutoff of 0.6, would the test image match the known image? {}".format(face_distance < 0.6)
+        ret += "\n"
+        ret += "- With a very strict cutoff of 0.5, would the test image match the known image? {}".format(face_distance < 0.5)
+        ret += "\n\n"
+
+    return ret
+
+iface = gr.Interface(fn=greet, inputs="image", outputs="text")
+iface.launch()