Upload app.py

app.py

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+import gradio as gr
+
+import cv2
+from mtcnn.mtcnn import MTCNN
+import tensorflow as tf
+import tensorflow_addons
+import numpy as np
+
+
+detector = MTCNN()
+
+model = tf.keras.models.load_model("FINAL-EFFICIENTNETV2-B0")
+
+
+def deepfakespredict(input_img):
+
+    face = detector.detect_faces(input_img)
+
+    text =""
+
+    if len(face) > 0:
+        x, y, width, height = face[0]['box']
+        x2, y2 = x + width, y + height
+
+        cv2.rectangle(input_img, (x, y), (x2, y2), (0, 255, 0), 2)
+
+        face_image = input_img[y:y2, x:x2]
+        face_image2 = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB)
+        face_image3 = cv2.resize(face_image2, (224, 224))
+        face_image4 = face_image3/255
+
+        pred = model.predict(np.expand_dims(face_image4, axis=0))[0]
+
+        if pred[1] >= 0.6:
+            text = "The image is fake."
+        elif pred[0] >= 0.6:
+            text = "The image is real."
+        else:
+            text = "The image might be real or fake."
+
+        # if pred[1] >= 0.5:
+        #     text = "The image is fake."
+        # else:
+        #     text = "The image is real."
+
+    else:
+        text = "Face is not detected in the image."
+
+    return pred, text, input_img
+
+
+title="EfficientNetV2 Deepfakes Image Detector"
+description="This is a demo implementation of EfficientNetV2 Deepfakes Image Detector. To use it, simply upload your image, or click one of the examples to load them."
+examples = [
+            ['fake-86.jpg'],
+            ['fake-239.jpg'],
+            ['fake-254.jpg'],
+            ['fake-1266.jpg'],
+            ['fake-2225.jpg']
+            ]
+demo = gr.Interface(deepfakespredict,
+                     inputs = ["image"],
+                     outputs=["text","text","image"],
+                     title=title,
+                     description=description,
+                     examples=examples
+                     )
+demo.launch()