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import streamlit as st
from keras.models import load_model
import cv2
import numpy as np
from keras.preprocessing.image import img_to_array
def preprocess_image(img, target_size): # Reuse your function with `img` as input
image = cv2.resize(img, target_size)
image = img_to_array(image)
image = image.astype('float32') / 255.0
image = np.expand_dims(image, axis=0)
return image
# Load the model (outside the main app loop for efficiency)
model_path = 'C:\\Users\\Istifadeci\Desktop\\Deepfake_konfrans\\xception_deepfake_image_main.h5'
model = load_model(model_path)
target_size = (224, 224) # Your model's input size
st.title("Deepfake Detection App")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Convert the file to an OpenCV image
file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
opencv_image = cv2.imdecode(file_bytes, 1)
# Display the image
st.image(opencv_image, channels="BGR") # OpenCV uses BGR format
# Preprocess the image
preprocessed_image = preprocess_image(opencv_image, target_size)
# Make a prediction
prediction = model.predict(preprocessed_image)
if prediction[0] > 0.1:
st.error("The image is predicted as 'FAKE'")
else:
st.success("The image is predicted as 'REAL'")
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