app.py

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()