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app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import cv2
+
+def preprocess_image(image):
+    image = cv2.resize(image, (224, 224))  # Resize to model input size
+    image = image / 255.0  # Normalize to [0,1] range
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    return image
+
+# Load trained model
+model = tf.keras.models.load_model("xception_deepfake_image.h5")
+
+def predict_deepfake(image):
+    image = preprocess_image(image)
+    prediction = model.predict(image)[0][0]  # Model outputs probability
+    label = "FAKE" if prediction > 0.5 else "REAL"
+    confidence = prediction if label == "FAKE" else 1 - prediction
+    return {"REAL": float(1 - prediction), "FAKE": float(prediction)}
+
+# Create Gradio Interface
+demo = gr.Interface(
+    fn=predict_deepfake,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=2),
+    title="DeepFake Image Detector",
+    description="Upload an image to check if it's REAL or FAKE",
+)
+
+demo.launch()