Create app.py

app.py

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+# streamlit_app.py
+import streamlit as st
+import numpy as np
+import tensorflow as tf
+import cv2
+import os
+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
+
+st.set_page_config(page_title="Deepfake Image Verifier", layout="centered")
+st.title("Deepfake Image Verifier")
+st.markdown("Upload a face image to classify it as Real or Fake using Xception and EfficientNet.")
+
+# Load models from HF Hub once
+@st.cache_resource
+def load_models():
+    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)
+    return xcp_model, eff_model
+
+xcp_model, eff_model = load_models()
+
+# Prediction function
+def predict_deepfake(image_np):
+    xcp_img = cv2.resize(image_np, (299, 299))
+    eff_img = cv2.resize(image_np, (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
+
+# File uploader
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file:
+    # Read and preprocess
+    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    st.image(image_rgb, caption="Uploaded Image", use_column_width=True)
+    label = predict_deepfake(image_rgb)
+    st.success(f"Prediction: {label}")