Update app.py

app.py

@@ -1,79 +1,79 @@
-import tensorflow.keras as K
-import os
-from tensorflow.keras import layers
-import tensorflow as tf
-import gradio as gr
-from extract_landmarks import get_data_for_test,extract_landmark,merge_video_prediction
-
-block_size = 60
-DROPOUT_RATE = 0.5
-RNN_UNIT = 64
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
-gpus = tf.config.list_physical_devices(device_type='GPU')
-for gpu in gpus:
-    tf.config.experimental.set_memory_growth(device=gpu, enable=True)
-device = "CPU" if len(gpus) == 0 else "GPU"
-
-
-def predict(video):
-    path = extract_landmark(video)
-    test_samples, test_samples_diff, _, _, test_sv, test_vc = get_data_for_test(path, 1, block_size)
-
-    model = K.Sequential([
-        layers.InputLayer(input_shape=(block_size, 136)),
-        layers.Dropout(0.25),
-        layers.Bidirectional(layers.GRU(RNN_UNIT)),
-        layers.Dropout(DROPOUT_RATE),
-        layers.Dense(64, activation='relu'),
-        layers.Dropout(DROPOUT_RATE),
-        layers.Dense(2, activation='softmax')
-    ])
-    model_diff = K.Sequential([
-        layers.InputLayer(input_shape=(block_size - 1, 136)),
-        layers.Dropout(0.25),
-        layers.Bidirectional(layers.GRU(RNN_UNIT)),
-        layers.Dropout(DROPOUT_RATE),
-        layers.Dense(64, activation='relu'),
-        layers.Dropout(DROPOUT_RATE),
-        layers.Dense(2, activation='softmax')
-    ])
-
-    lossFunction = K.losses.SparseCategoricalCrossentropy(from_logits=False)
-    optimizer = K.optimizers.Adam(learning_rate=0.001)
-    model.compile(optimizer=optimizer,
-                  loss=lossFunction,
-                  metrics=['accuracy'])
-    model_diff.compile(optimizer=optimizer,
-                  loss=lossFunction,
-                  metrics=['accuracy'])
-
-#----Using Deeperforensics 1.0 Parameters----#
-    model.load_weights('g1.h5')
-    model_diff.load_weights('g2.h5')
-
-    prediction = model.predict(test_samples)
-    prediction_diff = model_diff.predict(test_samples_diff)
-    mix_predict = []
-    for i in range(len(prediction)):
-        mix = prediction[i][1] + prediction_diff[i][1]
-        mix_predict.append(mix/2)
-
-    prediction_video = merge_video_prediction(mix_predict, test_sv, test_vc)
-
-    video_names = []
-    for key in test_vc.keys():
-        video_names.append(key)
-    for i, pd in enumerate(prediction_video):
-        if pd >= 0.5:
-            label = "Fake"
-        else:
-            label = "Real"
-    return label
-
-inputs = gr.inputs.Video()
-outputs = gr.outputs.Textbox()
-iface = gr.Interface(fn=predict, inputs=inputs, outputs=outputs,
-                     examples=[["sample_fake.mp4"],["sample_real.mp4"]])
+import tensorflow.keras as K
+import os
+from tensorflow.keras import layers
+import tensorflow as tf
+import gradio as gr
+from extract_landmarks import get_data_for_test,extract_landmark,merge_video_prediction
+
+block_size = 60
+DROPOUT_RATE = 0.5
+RNN_UNIT = 64
+os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+gpus = tf.config.list_physical_devices(device_type='GPU')
+for gpu in gpus:
+    tf.config.experimental.set_memory_growth(device=gpu, enable=True)
+device = "CPU" if len(gpus) == 0 else "GPU"
+print("using {}".format(device))
+
+def predict(video):
+    path = extract_landmark(video)
+    test_samples, test_samples_diff, _, _, test_sv, test_vc = get_data_for_test(path, 1, block_size)
+
+    model = K.Sequential([
+        layers.InputLayer(input_shape=(block_size, 136)),
+        layers.Dropout(0.25),
+        layers.Bidirectional(layers.GRU(RNN_UNIT)),
+        layers.Dropout(DROPOUT_RATE),
+        layers.Dense(64, activation='relu'),
+        layers.Dropout(DROPOUT_RATE),
+        layers.Dense(2, activation='softmax')
+    ])
+    model_diff = K.Sequential([
+        layers.InputLayer(input_shape=(block_size - 1, 136)),
+        layers.Dropout(0.25),
+        layers.Bidirectional(layers.GRU(RNN_UNIT)),
+        layers.Dropout(DROPOUT_RATE),
+        layers.Dense(64, activation='relu'),
+        layers.Dropout(DROPOUT_RATE),
+        layers.Dense(2, activation='softmax')
+    ])
+
+    lossFunction = K.losses.SparseCategoricalCrossentropy(from_logits=False)
+    optimizer = K.optimizers.Adam(learning_rate=0.001)
+    model.compile(optimizer=optimizer,
+                  loss=lossFunction,
+                  metrics=['accuracy'])
+    model_diff.compile(optimizer=optimizer,
+                  loss=lossFunction,
+                  metrics=['accuracy'])
+
+#----Using Deeperforensics 1.0 Parameters----#
+    model.load_weights('g1.h5')
+    model_diff.load_weights('g2.h5')
+
+    prediction = model.predict(test_samples)
+    prediction_diff = model_diff.predict(test_samples_diff)
+    mix_predict = []
+    for i in range(len(prediction)):
+        mix = prediction[i][1] + prediction_diff[i][1]
+        mix_predict.append(mix/2)
+
+    prediction_video = merge_video_prediction(mix_predict, test_sv, test_vc)
+
+    video_names = []
+    for key in test_vc.keys():
+        video_names.append(key)
+    for i, pd in enumerate(prediction_video):
+        if pd >= 0.5:
+            label = "Fake"
+        else:
+            label = "Real"
+    return label
+
+inputs = gr.inputs.Video()
+outputs = gr.outputs.Textbox()
+iface = gr.Interface(fn=predict, inputs=inputs, outputs=outputs,
+                     examples=[["sample_fake.mp4"],["sample_real.mp4"]])
 iface.launch()