Create app.py

app.py

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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'")