Spaces:
Sleeping
Sleeping
File size: 1,359 Bytes
e73e2a2 3a2cd79 e73e2a2 3a2cd79 e6fe7fe 3a2cd79 e73e2a2 eaa1a24 b26c2e5 e73e2a2 3a2cd79 b26c2e5 6ac5675 b26c2e5 6ac5675 b26c2e5 6ac5675 |
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 |
import os
import cv2
import numpy as np
import gradio as gr
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.applications.xception import preprocess_input as xcp_pre
from tensorflow.keras.applications.efficientnet import preprocess_input as eff_pre
from huggingface_hub import hf_hub_download
# Download and load models
xcp_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector", filename="xception_model.h5")
eff_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector", filename="efficientnet_model.h5")
xcp_model = load_model(xcp_path)
eff_model = load_model(eff_path)
def predict(image):
# Use the full image directly (no face extraction)
xcp_img = cv2.resize(image, (299, 299))
eff_img = cv2.resize(image, (224, 224))
xcp_tensor = xcp_pre(xcp_img.astype(np.float32))[np.newaxis, ...]
eff_tensor = eff_pre(eff_img.astype(np.float32))[np.newaxis, ...]
xcp_pred = xcp_model.predict(xcp_tensor, verbose=0).flatten()[0]
eff_pred = eff_model.predict(eff_tensor, verbose=0).flatten()[0]
avg_pred = (xcp_pred + eff_pred) / 2
label = "Real" if avg_pred > 0.5 else "Fake"
return label
iface = gr.Interface(
fn=predict,
inputs=gr.Image(type="filepath"),
outputs=gr.JSON(), # <-- very important
live=False
)
iface.launch() |