Update app.py

app.py

@@ -0,0 +1,47 @@
+import cv2
+import numpy as np 
+import gradio as gr
+from tensorflow.keras.utils import img_to_array
+from tensorflow.keras.models import load_model
+import os
+
+model = load_model(r'deepfake_detection_model.h5')
+
+def predict_image(img):
+    
+    x = img_to_array(img)
+
+    x = cv2.resize(x, (256, 256), interpolation=cv2.INTER_AREA)
+    
+    x /= 255.0
+
+    x = np.expand_dims(x, axis=0)
+
+    prediction = np.argmax(model.predict(x), axis=1)
+    
+    if prediction == 0:
+        return 'Fake Image'
+    else:
+        return 'Real Image'
+
+
+# Define the Gradio Interface with the desired title and description
+
+description_html = """
+<p>Upload a face image to check if it's real or morphed with deepfake</p>
+"""
+
+# Define example images and their true labels for users to choose from
+
+custom_css = """
+div {background-color: whitesmoke;}
+"""
+
+gr.Interface(
+    fn=predict_image,
+    inputs='image',
+    outputs='text',
+    title="Deepfake Image Detection",
+    description=description_html,
+    allow_flagging='never'
+).launch()