Update streamlit_app.py

streamlit_app.py

@@ -1,19 +1,19 @@
-# 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
 
+# Set Streamlit page configuration
 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.title("🔍 Deepfake Image Verifier")
+st.markdown("Upload an image to classify it as **Real** or **Fake** using an ensemble of Xception and EfficientNet models.")
+
+# Load models only once and cache them
 @st.cache_resource
 def load_models():
     xcp_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector_final", filename="xception_model.h5")
@@ -25,7 +25,7 @@ def load_models():
 xcp_model, eff_model = load_models()
 
 # Prediction function
-def predict_deepfake(image_np):
+def predict(image_np):
     xcp_img = cv2.resize(image_np, (299, 299))
     eff_img = cv2.resize(image_np, (224, 224))
 
@@ -36,18 +36,23 @@ def predict_deepfake(image_np):
     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"
+    label = "🟢 Real" if avg_pred > 0.5 else "🔴 Fake"
     return label
 
-# File uploader
+# Upload image
 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}")
+    if image is None:
+        st.error("Failed to decode the image. Please try another file.")
+    else:
+        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        st.image(image_rgb, caption="Uploaded Image", use_column_width=True)
+
+        with st.spinner("Analyzing..."):
+            label = predict(image_rgb)
+
+        st.success(f"Prediction: **{label}**")