Update app.py

app.py

@@ -1,92 +1,92 @@
-import gradio as gr
-
-import cv2
-from mtcnn.mtcnn import MTCNN
-import tensorflow as tf
-import tensorflow_addons
-import numpy as np
-
-import os
-import zipfile
-
-local_zip = "FINAL-EFFICIENTNETV2-B0.zip"
-zip_ref = zipfile.ZipFile(local_zip, 'r')
-zip_ref.extractall('FINAL-EFFICIENTNETV2-B0')
-zip_ref.close()
-
-model = tf.keras.models.load_model("FINAL-EFFICIENTNETV2-B0")
-
-detector = MTCNN()
-
-def deepfakespredict(input_img ):
-
-    labels = ['real', 'fake']
-    pred = [0, 0]
-    text =""
-    text2 =""
-    
-    face = detector.detect_faces(input_img)
-
-    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 may be REAL or FAKE."
-            
-    else:
-        text = "Face is not detected in the image."
-    
-    text2 = "REAL: " + str(np.round(pred[0]*100, 2)) + "%, FAKE: " + str(np.round(pred[1]*100, 2)) + "%"
-
-    return input_img, text, text2, {labels[i]: float(pred[i]) for i in range(2)}
-
-
-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.  \
-            This demo and model represent the Final Year Project titled \"Achieving Face Swapped Deepfakes Detection Using EfficientNetV2\" by a CS undergraduate Lee Sheng Yeh.  \
-            The examples were extracted from Celeb-DF(V2)(Li et al, 2020) and FaceForensics++(Rossler et al., 2019). Full reference details is available in \"references.txt.\" \
-            The examples are used under fair use to demo the working of the model only. If any copyright is infringed, please contact the researcher via this email: tp054565@mail.apu.edu.my, the researcher will immediately take down the examples used.\
-            "
-            
-examples = [
-                ['Fake-1.png'],
-                ['Fake-2.png'],
-                ['Fake-3.png'],
-                ['Fake-4.png'],
-                ['Fake-5.png'],
-  
-                ['Real-1.png'],
-                ['Real-2.png'],
-                ['Real-3.png'],
-                ['Real-4.png'],
-                ['Real-5.png']
-                
-           ]
-           
-             
-gr.Interface(deepfakespredict,
-                     inputs = ["image"],
-                     outputs=[gr.outputs.Image(type="pil", label="Detected face"), 
-                         "text", 
-                         "text", 
-                         gr.outputs.Label(num_top_classes=None, type="auto", label="Confidence")],                     
-                     title=title,
-                     description=description,
-                     examples = examples, 
-                     examples_per_page = 5
+import gradio as gr
+
+import cv2
+from mtcnn.mtcnn import MTCNN
+import tensorflow as tf
+import tensorflow_addons
+import numpy as np
+
+import os
+import zipfile
+
+local_zip = "FINAL-EFFICIENTNETV2-B0.zip"
+zip_ref = zipfile.ZipFile(local_zip, 'r')
+zip_ref.extractall('FINAL-EFFICIENTNETV2-B0')
+zip_ref.close()
+
+model = tf.keras.models.load_model("FINAL-EFFICIENTNETV2-B0")
+
+detector = MTCNN()
+
+def deepfakespredict(input_img ):
+
+    labels = ['real', 'fake']
+    pred = [0, 0]
+    text =""
+    text2 =""
+    
+    face = detector.detect_faces(input_img)
+
+    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 may be REAL or FAKE."
+            
+    else:
+        text = "Face is not detected in the image."
+    
+    text2 = "REAL: " + str(np.round(pred[0]*100, 2)) + "%, FAKE: " + str(np.round(pred[1]*100, 2)) + "%"
+
+    return input_img, text, text2, {labels[i]: float(pred[i]) for i in range(2)}
+
+
+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.  \
+            This demo and model represent the Final Year Project titled \"Achieving Face Swapped Deepfakes Detection Using EfficientNetV2\" by a CS undergraduate Lee Sheng Yeh.  \
+            The examples were extracted from Celeb-DF(V2)(Li et al, 2020) and FaceForensics++(Rossler et al., 2019). Full reference detail is available in \"references.txt.\" \
+            The examples are used under fair use to demo the working of the model only. If any copyright is infringed, please contact the researcher via this email: tp054565@mail.apu.edu.my.\
+            "
+            
+examples = [
+                ['Fake-1.png'],
+                ['Fake-2.png'],
+                ['Fake-3.png'],
+                ['Fake-4.png'],
+                ['Fake-5.png'],
+  
+                ['Real-1.png'],
+                ['Real-2.png'],
+                ['Real-3.png'],
+                ['Real-4.png'],
+                ['Real-5.png']
+                
+           ]
+           
+             
+gr.Interface(deepfakespredict,
+                     inputs = ["image"],
+                     outputs=[gr.outputs.Image(type="pil", label="Detected face"), 
+                         "text", 
+                         "text", 
+                         gr.outputs.Label(num_top_classes=None, type="auto", label="Confidence")],                     
+                     title=title,
+                     description=description,
+                     examples = examples, 
+                     examples_per_page = 5
                      ).launch()