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app and requirements

app.py

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+import streamlit as st
+import numpy as np
+from tensorflow.keras.models import load_model
+from PIL import Image
+
+# Model Load
+model = load_model("xception_deepfake_image.h5")
+
+# Title
+st.title("DeepFake Image Detector")
+
+# Upload Image
+uploaded_file = st.file_uploader("Upload an image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    # Preprocessing
+    image = image.resize((256, 256))
+    image = np.array(image) / 255.0
+    image = np.expand_dims(image, axis=0)
+
+    # Prediction
+    prediction = model.predict(image)
+    result = "FAKE" if prediction > 0.5 else "REAL"
+    
+    st.write(f"**Prediction:** {result}")

requirements.txt

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+streamlit
+torch
+torchvision
+numpy
+pillow