Update app.py

app.py

@@ -6,18 +6,16 @@ from tensorflow.keras.models import load_model, Model
 from tensorflow.keras.preprocessing.image import img_to_array
 from tensorflow.keras.applications.xception import preprocess_input as xcp_pre
 from tensorflow.keras.applications.efficientnet import preprocess_input as eff_pre
-from PIL import Image
-import matplotlib.pyplot as plt
-
 from huggingface_hub import hf_hub_download
+from PIL import Image
 
+# Load models from Hugging Face Hub
 xcp_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector", filename="xception_model.h5")
 eff_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector", filename="efficientnet_model.h5")
-
 xcp_model = load_model(xcp_path)
 eff_model = load_model(eff_path)
 
-# Face detection
+# Face detection using OpenCV
 def detect_face_opencv(pil_image):
     cv_img = np.array(pil_image.convert("RGB"))
     cv_img = cv_img[:, :, ::-1]  # RGB to BGR
@@ -25,11 +23,11 @@ def detect_face_opencv(pil_image):
     gray = cv2.cvtColor(cv_img, cv2.COLOR_BGR2GRAY)
     faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=4)
     if len(faces) == 0:
-        return pil_image  # fallback
-    (x, y, w, h) = max(faces, key=lambda b: b[2]*b[3])  # largest face
+        return pil_image  # fallback to original
+    (x, y, w, h) = max(faces, key=lambda b: b[2]*b[3])
     return pil_image.crop((x, y, x+w, y+h))
 
-# Grad-CAM
+# Grad-CAM visualization (Xception only)
 def grad_cam(model, img, size, preprocess_func):
     img_resized = img.resize(size)
     x = img_to_array(img_resized)
@@ -45,7 +43,7 @@ def grad_cam(model, img, size, preprocess_func):
     cam = np.mean(grads, axis=-1)
     cam = np.maximum(cam, 0)
     cam /= cam.max() if cam.max() != 0 else 1
-    heatmap = cv2.resize(cam.numpy(), (size[0], size[1]))
+    heatmap = cv2.resize(cam, size)
     heatmap = np.uint8(255 * heatmap)
     heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
 
@@ -55,14 +53,14 @@ def grad_cam(model, img, size, preprocess_func):
     superimposed = cv2.addWeighted(img_np, 0.6, heatmap, 0.4, 0)
     return Image.fromarray(cv2.cvtColor(superimposed, cv2.COLOR_BGR2RGB))
 
-# Preprocessing
-def preprocess(img, size, preprocess_func):
+# Preprocessing helper
+def preprocess(img, size, func):
     img = img.resize(size)
     arr = img_to_array(img)
     arr = np.expand_dims(arr, axis=0)
-    return preprocess_func(arr)
+    return func(arr)
 
-# Prediction logic
+# Prediction function
 def predict(image):
     face = detect_face_opencv(image)
 
@@ -72,17 +70,17 @@ def predict(image):
     xcp_pred = xcp_model.predict(xcp_input)[0][0]
     eff_pred = eff_model.predict(eff_input)[0][0]
     ensemble_prob = (xcp_pred + eff_pred) / 2
-    label = "REAL" if ensemble_prob > 0.5 else "FAKE"
 
+    label = "REAL" if ensemble_prob > 0.5 else "FAKE"
     cam_img = grad_cam(xcp_model, face, (299, 299), xcp_pre)
 
     return f"{label} ({ensemble_prob:.2%} confidence)", cam_img
 
-# Gradio interface
+# Gradio UI
 gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil"),
     outputs=["text", "image"],
     title="Deepfake Image Detector (with Grad-CAM)",
-    description="Upload an image. We detect the face, classify using an ensemble (Xception + EfficientNetB4), and explain the prediction with Grad-CAM."
+    description="Upload an image. We detect the face, classify using an ensemble (Xception + EfficientNetB4), and explain the prediction using Grad-CAM on Xception."
 ).launch()