Update app.py

app.py

@@ -1,84 +1,88 @@
-import gradio as gr
-import numpy as np
-import cv2
-import tensorflow as tf
-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
-
-# Load models from Hugging Face model repo
-xcp_model = load_model("https://huggingface.co/Zeyadd-Mostaffa/deepfake-image-detector/resolve/main/xception_model.h5")
-eff_model = load_model("https://huggingface.co/Zeyadd-Mostaffa/deepfake-image-detector/resolve/main/efficientnet_model.h5")
-
-# Face detection
-def detect_face_opencv(pil_image):
-    cv_img = np.array(pil_image.convert("RGB"))
-    cv_img = cv_img[:, :, ::-1]  # RGB to BGR
-    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
-    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.crop((x, y, x+w, y+h))
-
-# Grad-CAM
-def grad_cam(model, img, size, preprocess_func):
-    img_resized = img.resize(size)
-    x = img_to_array(img_resized)
-    x = np.expand_dims(x, axis=0)
-    x = preprocess_func(x)
-    x_tensor = tf.convert_to_tensor(x)
-
-    grad_model = Model([model.inputs], [model.layers[-3].output, model.output])
-    with tf.GradientTape() as tape:
-        conv_outputs, predictions = grad_model(x_tensor)
-        loss = predictions[:, 0]
-    grads = tape.gradient(loss, conv_outputs)[0]
-    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 = np.uint8(255 * heatmap)
-    heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
-
-    img_np = np.array(img_resized)
-    if img_np.shape[-1] == 4:
-        img_np = img_np[:, :, :3]
-    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):
-    img = img.resize(size)
-    arr = img_to_array(img)
-    arr = np.expand_dims(arr, axis=0)
-    return preprocess_func(arr)
-
-# Prediction logic
-def predict(image):
-    face = detect_face_opencv(image)
-
-    xcp_input = preprocess(face, (299, 299), xcp_pre)
-    eff_input = preprocess(face, (224, 224), eff_pre)
-
-    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"
-
-    cam_img = grad_cam(xcp_model, face, (299, 299), xcp_pre)
-
-    return f"{label} ({ensemble_prob:.2%} confidence)", cam_img
-
-# Gradio interface
-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."
-).launch()
+import gradio as gr
+import numpy as np
+import cv2
+import tensorflow as tf
+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
+
+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
+def detect_face_opencv(pil_image):
+    cv_img = np.array(pil_image.convert("RGB"))
+    cv_img = cv_img[:, :, ::-1]  # RGB to BGR
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+    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.crop((x, y, x+w, y+h))
+
+# Grad-CAM
+def grad_cam(model, img, size, preprocess_func):
+    img_resized = img.resize(size)
+    x = img_to_array(img_resized)
+    x = np.expand_dims(x, axis=0)
+    x = preprocess_func(x)
+    x_tensor = tf.convert_to_tensor(x)
+
+    grad_model = Model([model.inputs], [model.layers[-3].output, model.output])
+    with tf.GradientTape() as tape:
+        conv_outputs, predictions = grad_model(x_tensor)
+        loss = predictions[:, 0]
+    grads = tape.gradient(loss, conv_outputs)[0]
+    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 = np.uint8(255 * heatmap)
+    heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
+
+    img_np = np.array(img_resized)
+    if img_np.shape[-1] == 4:
+        img_np = img_np[:, :, :3]
+    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):
+    img = img.resize(size)
+    arr = img_to_array(img)
+    arr = np.expand_dims(arr, axis=0)
+    return preprocess_func(arr)
+
+# Prediction logic
+def predict(image):
+    face = detect_face_opencv(image)
+
+    xcp_input = preprocess(face, (299, 299), xcp_pre)
+    eff_input = preprocess(face, (224, 224), eff_pre)
+
+    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"
+
+    cam_img = grad_cam(xcp_model, face, (299, 299), xcp_pre)
+
+    return f"{label} ({ensemble_prob:.2%} confidence)", cam_img
+
+# Gradio interface
+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."
+).launch()