Spaces:
Runtime error
Runtime error
File size: 1,968 Bytes
9f9b2fc 853a9c8 9f9b2fc 853a9c8 9f9b2fc 604195d 9f9b2fc 604195d 9f9b2fc 604195d 9f9b2fc 604195d 9f9b2fc 604195d 9f9b2fc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
import gradio as gr
import tensorflow as tf
import numpy as np
from tensorflow.keras.models import load_model
import keras
# 1. Load your model
@keras.saving.register_keras_serializable()
def scaling(x, scale=255.0, **kwargs):
return x * scale
@keras.saving.register_keras_serializable()
def l2_normalize(x, axis=-1, epsilon=1e-10):
return tf.nn.l2_normalize(x, axis=axis, epsilon=epsilon)
# Pass the registered functions explicitly
custom_objects = {"scaling": scaling, "l2_normalize": l2_normalize}
# Load the model with custom_objects
model = load_model("facenet_real_fake_classifier_final.keras", custom_objects=custom_objects)
# 2. Define your inference function
def detect_forgery(image):
# Preprocess the image to match your model’s input requirements
img = tf.image.resize(image, (160, 160)) # Example size; adjust for your model
img = tf.expand_dims(img, axis=0)
img = img / 255.0 # Example normalization; adapt as needed
# Run inference
predictions = model.predict(img)[0]
pred_value = float(predictions) # or pred_value.item()
# Suppose predictions = [prob_real, prob_fake]
# Format output
# You can return a dictionary or a string. For example:
if pred_value >= 0.5:
# It's real, use p as the confidence
confidence = pred_value
label = "REAL"
else:
# It's fake, confidence is (1 - p)
confidence = 1.0 - pred_value
label = "FAKE"
return f"{label} with confidence {confidence:.2f}"
# 3. Build your Gradio interface
demo = gr.Interface(
fn=detect_forgery,
inputs=gr.Image(type="numpy"), # 'type="numpy"' gives a NumPy array
outputs="text",
title="Face Forgery Detector",
description="Upload a face image to check if it's likely forged or real."
)
# 4. Launch the app (Gradio handles the rest)
if __name__ == "__main__":
_ = model.predict(np.zeros((1, 160, 160, 3), dtype=np.float32))
demo.launch()
|