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app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.preprocessing import image
+from PIL import Image
+import cv2
+import traceback
+
+# Load the trained model
+try:
+    model = tf.keras.models.load_model("./model/deepfake_mobilenet_model.h5")
+except Exception as e:
+    print(f"Error loading model. Make sure the path is correct. Error: {e}")
+    inputs = tf.keras.Input(shape=(224, 224, 3))
+    outputs = tf.keras.layers.Dense(1, activation="sigmoid")(tf.keras.layers.GlobalAveragePooling2D()(inputs))
+    model = tf.keras.Model(inputs, outputs)
+
+# ==============================================================================
+# --- Grad-CAM Heatmap Generation Functions ---
+# ==============================================================================
+
+def get_last_conv_layer_name(model):
+    for layer in reversed(model.layers):
+        if len(layer.output.shape) == 4:
+            return layer.name
+    raise ValueError("Could not find a conv layer in the model")
+
+def make_gradcam_heatmap(img_array, model, last_conv_layer_name):
+    grad_model = tf.keras.models.Model(
+        model.inputs, [model.get_layer(last_conv_layer_name).output, model.output]
+    )
+    with tf.GradientTape() as tape:
+        last_conv_layer_output, preds = grad_model([img_array])
+        class_channel = preds[0][:, 0]
+    grads = tape.gradient(class_channel, last_conv_layer_output)
+    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
+    last_conv_layer_output = last_conv_layer_output[0]
+    heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]
+    heatmap = tf.squeeze(heatmap)
+    heatmap = tf.maximum(heatmap, 0) / (tf.math.reduce_max(heatmap) + 1e-8)
+    return heatmap.numpy()
+
+def superimpose_gradcam(original_img_pil, heatmap):
+    original_img = np.array(original_img_pil)
+    heatmap = cv2.resize(heatmap, (original_img.shape[1], original_img.shape[0]))
+    heatmap = np.uint8(255 * heatmap)
+    heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
+    superimposed_img = cv2.addWeighted(original_img, 0.6, heatmap, 0.4, 0)
+    return Image.fromarray(superimposed_img)
+
+# ==============================================================================
+# --- Main Prediction Function ---
+# ==============================================================================
+
+last_conv_layer_name = get_last_conv_layer_name(model)
+
+def predict_and_visualize(img):
+    try:
+        img_resized = img.resize((224, 224))
+        img_array = image.img_to_array(img_resized) / 255.0
+        img_array_expanded = np.expand_dims(img_array, axis=0)
+        
+        prediction = model.predict(img_array_expanded, verbose=0)[0][0]
+        real_conf = float(prediction)
+        fake_conf = float(1 - prediction)
+        labels = {"Real Image": real_conf, "Fake Image": fake_conf}
+        
+        heatmap = make_gradcam_heatmap(img_array_expanded, model, last_conv_layer_name)
+        superimposed_image = superimpose_gradcam(img, heatmap)
+        
+        return labels, superimposed_image
+
+    except Exception as e:
+        print("--- GRADIO APP ERROR ---")
+        traceback.print_exc()
+        print("------------------------")
+        error_msg = f"Error: {e}"
+        return {error_msg: 0.0}, None
+
+# ==============================================================================
+# --- Gradio Interface with Improved Design ---
+# ==============================================================================
+
+gr.Interface(
+    fn=predict_and_visualize,
+    inputs=gr.Image(type="pil", label="📷 Upload a Face Image"),
+    outputs=[
+        gr.Label(num_top_classes=2, label="🧠 Model Prediction"),
+        gr.Image(label="🔥 Grad-CAM Heatmap Overlay")
+    ],
+    title="✨ Deepfake Image Detector with Visual Explanation ✨",
+    description="""
+    **Detect whether an uploaded image is Real or AI-Generated (Deepfake).**  
+    The confidence bars show the model's certainty for both **Real** and **Fake** categories.  
+
+    Below, the **Grad-CAM heatmap** highlights the regions the model focused on (red = most important).  
+
+    ⚡ **Instructions:**  
+    1. Upload a face image (JPEG/PNG).  
+    2. Wait a few seconds for the prediction and heatmap.  
+    3. Observe the confidence bars and heatmap for model explanation.
+    """,
+    examples=[
+        ["sample_images/real1.jpg"],
+        ["sample_images/fake1.jpg"]
+    ],
+    theme="default"  # you can later try 'soft', 'grass', or 'peach'
+).launch()

deepfake_mobilenet_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f6530c0a9c0d2400a25c467e7234636a245adedc770e5db9a5efa92c18fe517d
+size 11540600

requirements.txt

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+gradio
+tensorflow
+pillow
+matplotlib
+numpy