Upload 3 files

app.py

@@ -0,0 +1,50 @@
+import numpy as np
+import cv2
+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>This model was trained by Rudolf Enyimba in partial fulfillment of the requirements 
+of Solent University for the degree of MSc Artificial Intelligence and Data Science</p>
+<p>This model was trained to detect deepfake images.</p>
+<p>The model achieved an accuracy of <strong>91%</strong> on the test set.</p>
+<p>Upload a face image or pick from the samples below to test model accuracy</p>
+"""
+
+# Define example images and their true labels for users to choose from
+example_data = ['AI POPE.jpg']
+
+
+gr.Interface(
+    fn=predict_image,
+    inputs="image",
+    outputs=gr.Label(num_top_classes=15,min_width=360),
+    title="Deepfake Image Detection(CNN)",
+    description=description_html,
+    allow_flagging='never',
+    examples=example_data 
+).launch()

deepfake_detection_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7fcb48499437d1b8bc52250e1d03e25dcfc8ba19ac025cee6ad46c71e00f791c
+size 314652832