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# -*- coding: utf-8 -*-
"""
Created on Sun Jan 28 18:48:07 2024
@author: liewchooichin
"""
import os
import pathlib
import gradio as gr
import numpy as np
import tensorflow as tf
from huggingface_hub import snapshot_download
from huggingface_hub import from_pretrained_keras
# check the tensoflow version
print(f"tensorflow version: {tf.__version__}")
# global variables
# predictions from:
pred_binary = "" # binary labels
pred_multi = "" # multi labels
# sample files
samples = []
labels = []
data_dir = "face_samples"
# local testing
LOCAL_TEST = False # when in HF, set to False
HF_SPACE = True # when in HF
# My model in the HF repo
REPO_ID_BINARY = 'liewchooichin/fake_binary'
REPO_ID_MULTILABEL = 'liewchooichin/fake_multilabel'
# tf_model = None
# keras_model = None
local_model_dir = "fake_models"
pb_name = "saved_model.pb"
keras_binary_label = os.path.join("binary_label", "all_binary_6771.keras")
keras_multilabel = os.path.join("multi_label", "multi_7036.keras")
def get_samples():
samples_path = os.path.join(
os.path.dirname(__file__),
data_dir
)
samples_path = pathlib.Path(samples_path)
files = list(samples_path.glob("*.jpg"))
# hard code the examples first for test
# first 9 are fake, the last 3 are real
# fake faces
for i in range(9):
samples.append(files[i])
# get the fake or real label
fake = 1
labels.append(fake)
# real faces
for i in range(3):
samples.append(files[i+9])
fake = 0
labels.append(fake)
# print to check the image and labels
for i in range(12):
print(samples[i], labels[i])
def download_keras_model():
# set the model variables to be global
global keras_binary_model
global keras_multi_model
# HF repo
# load binary label
if HF_SPACE:
download_dir = snapshot_download(repo_id=REPO_ID_BINARY)
print(f"Download dir: {download_dir}")
keras_binary_path = os.path.join(download_dir, keras_binary_label)
print(f"Keras binary label: {keras_binary_path}")
# this load() does not work in HF
#keras_binary_model = tf.keras.models.load(keras_binary_path)
#keras_binary_model = tf.keras.saving.load_model(keras_binary_path)
#keras_binary_model = from_pretrained_keras("liewchooichin/fake_binary")
keras_binary_model = tf.saved_model.load(download_dir)
# local testing
# check if the model exists
# binary label
# "C:\PY\exercises\hello_iris\fake_models\binary_label\all_binary_6771.keras"
if LOCAL_TEST:
model_path = os.path.join(
os.path.dirname(__file__),
local_model_dir,
keras_binary_label
)
if not os.path.exists(model_path):
print(f"Model not found: {model_path}")
# load local keras model
keras_binary_model = tf.keras.models.load_model(model_path)
# Check with model loaded
#print(f"\nBinary label model: {keras_binary_model.name}")
# load multilabel
# "C:\PY\exercises\hello_iris\fake_models\multi_label\all_multi_7036.keras"
if LOCAL_TEST:
model_path = os.path.join(
os.path.dirname(__file__),
local_model_dir,
keras_multilabel
)
if not os.path.exists(model_path):
print(f"Model not found: {model_path}")
# load local keras model
keras_multi_model = tf.keras.models.load_model(model_path)
# In HF space, load model from repository
# Load the multilabel model
if HF_SPACE:
# HF repo
download_dir = snapshot_download(repo_id=REPO_ID_MULTILABEL)
print(f"Download dir: {download_dir}")
keras_multi_path = os.path.join(download_dir, keras_multilabel)
print(f"Keras multi label: {keras_multi_path}")
# load() does not work in HF
#keras_multi_model = tf.keras.models.load(keras_multi_path)
#keras_multi_model = tf.keras.saving.load_model(keras_multi_path)
#keras_multi_model = from_pretrained_keras("liewchooichin/fake_multilabel")
keras_multi_model = tf.saved_model.load(download_dir)
# Check with model loaded
#print(f"\nLoaded model: {keras_multi_model.name}")
def get_img_array(img_path):
# get the dataset into array of 224x224
img = tf.keras.utils.load_img(
img_path,
target_size=(224, 224)
)
img_array = tf.keras.utils.img_to_array(img)
# expand the dimension for prediction
img_array = np.expand_dims(img_array, axis=0)
print(f"Shape of image array: {img_array.shape}")
return img_array
def get_prediction(img_path):
# adjust threshold for accuracy
threshold = 0.4
# check the image path
print(f"Image path: {img_path}")
# also display the original filename for info
orig_filename = img_path.split("\\")[-1]
get_img_array(img_path)
# get the image array
img_array = get_img_array(img_path)
# test with local model
# binary label
pred_binary = keras_binary_model(img_array, training=False)
print(f"Keras binary label: {pred_binary}")
if pred_binary[0][0] > threshold:
fake = "Fake"
else:
fake = "Real"
# multi label
pred_multi = keras_multi_model(img_array, training=False)
print(f"Keras multi label: {pred_multi}")
# Cut at the sigmoid 0.5 threshold
fake_parts = np.where(pred_multi > threshold, 1, 0)
print(f"Multi label: {fake_parts}")
# Format each of the fake face parts
parts_message = dict()
# The last one is the overall prediction
parts_message["overall"] = "Fake" if fake_parts[0][4] == 1 else "Real"
parts_message["left_eye"] = "Fake" if fake_parts[0][0] == 1 else "Real"
parts_message["right_eye"] = "Fake" if fake_parts[0][1] == 1 else "Real"
parts_message["nose"] = "Fake" if fake_parts[0][2] == 1 else "Real"
parts_message["mouth"] = "Fake" if fake_parts[0][3] == 1 else "Real"
# Format the display line by line
parts_formatted = ""
for k, v in parts_message.items():
parts_formatted = parts_formatted + f"{k}: {v}\n"
# Format result string
result_binary = f"Probability: {pred_binary} \
\nPrediction: {fake}\n"
result_multi = f"Probability: {pred_multi} \
\nPrediction: {fake_parts} \
\n{parts_formatted}"
# pred_multi = tf_model(img_path)
# print(f"tf: \n{pred_multi}")
return orig_filename, result_binary, result_multi
def clear_image():
# Clear the previous output result
return "", "", ""
def main():
get_samples()
# download_tf_model()
download_keras_model()
with gr.Blocks() as demo:
# call the main for preliminary work
main()
image_width = 256
image_height = 256
gr.Markdown(
"""
# Fake or real faces detection.
The dataset is obtained from https://www.kaggle.com/datasets/ciplab/real-and-fake-face-detection.
Trained with EfficientNet V2 B0.
One model is trained to do binary classification and the other \
multilabel classification. The multilabels classification is \
based on the last four digits provided by the filenames. \
The last four digits are following the order of left eye, \
right eye, nose and mouth. \
The labels are 1 (fake) and 0 (real).
For example: ___1010.jpg means left eye and nose are fake.
Binary accuracy for the binary label model: 0.6771. <br>
Binary accuracy for the multilabel model: 0.7036.
The fake faces are also categorized into how difficult it is \
to detect the faces as fake. The categories are easy, mid and hard.
The top prediction and its probabilities of classes are shown.
Try our sample faces below or upload one of your own.
"""
)
with gr.Row():
with gr.Column():
img = gr.Image(height=image_height,
width=image_width,
sources=["upload", "clipboard"],
interactive=True,
type="filepath")
with gr.Column():
text_1 = gr.Text(
label="Filename",
interactive=False, lines=1
)
text_2 = gr.Text(
label="Binary label, Efficient net v2 B0",
interactive=False, lines=2)
text_3 = gr.Text(
label="Multi label, Efficient net v2 B0",
interactive=False, lines=7,
visible=False)
"""
text_3 = gr.Text(label="Sashi's model",
interactive=False, lines=3)
text_4 = gr.Text(label="KK's model",
interactive=False, lines=3)
"""
# load the images directory
# print(f"List of examples: {samples}")
with gr.Row():
gr.Markdown("""
## Fakes faces <br>(easy)
""")
examples_1 = gr.Examples(
examples=[
samples[0], samples[1], samples[2],
],
inputs=[img],
outputs=[text_1, text_2, text_3],
run_on_click=True,
fn=get_prediction
)
with gr.Row():
gr.Markdown("""
## Fakes faces <br>(mid)
""")
examples_2 = gr.Examples(
examples=[
samples[3], samples[4], samples[5],
],
inputs=[img],
outputs=[text_1, text_2, text_3],
run_on_click=True,
fn=get_prediction
)
with gr.Row():
gr.Markdown("""
## Fakes faces <br>(hard)
""")
examples_3 = gr.Examples(
examples=[
samples[6], samples[7], samples[8],
],
inputs=[img],
outputs=[text_1, text_2, text_3],
run_on_click=True,
fn=get_prediction
)
with gr.Row():
gr.Markdown("""
## Real faces
""")
examples_4 = gr.Examples(
examples=[
samples[9], samples[10], samples[11]
],
inputs=[img],
outputs=[text_1, text_2, text_3],
run_on_click=True,
fn=get_prediction
)
# prediction when a file is uploaded
img.upload(fn=get_prediction, inputs=[img],
outputs=[text_1, text_2, text_3])
# when an example is clicked
# img.change(fn=get_prediction, inputs=[img],
# outputs=[text_1, text_2])
# when an image is cleared
img.clear(fn=clear_image, inputs=[],
outputs=[text_1, text_2, text_3])
if __name__ == "__main__":
demo.launch()
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