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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
# Load models from Hugging Face Hub
xcp_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector_final", filename="xception_model.h5")
eff_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector_final", filename="efficientnet_model.h5")
xcp_model = load_model(xcp_path)
eff_model = load_model(eff_path)
def predict(image_path): # receives file path (not array)
image = cv2.imread(image_path)
if image is None:
return "Invalid image"
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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", label="image_path"), # <- This must match backend call
outputs="text",
allow_flagging="never"
)
iface.launch()
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