Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras import layers, models
+from tensorflow.keras.applications import Xception
+import cv2
+import numpy as np
+
+def build_deepfake_detection_model():
+    cnn_base = Xception(weights='imagenet', include_top=False, input_shape=(128, 128, 3))
+    cnn_base.trainable = True
+    for layer in cnn_base.layers[:-50]:
+        layer.trainable = False
+    input_layer = layers.Input(shape=(1, 128, 128, 3))
+    x = layers.TimeDistributed(cnn_base)(input_layer)
+    x = layers.TimeDistributed(layers.GlobalAveragePooling2D())(x)
+    x = layers.BatchNormalization()(x)
+    x = layers.Dropout(0.5)(x)
+    x = layers.LSTM(128)(x)
+    x = layers.Dropout(0.5)(x)
+    output = layers.Dense(1, activation='sigmoid')(x)
+    model = models.Model(inputs=input_layer, outputs=output)
+    return model
+
+# Load the model (you'll need to upload your model weights to Hugging Face)
+model = build_deepfake_detection_model()
+model.load_weights('dfdc_cnn_lstm_model_finetuned.keras')
+
+def process_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    frames = []
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        frame = cv2.resize(frame, (128, 128))
+        frame = frame.astype('float32') / 255.0
+        frames.append(frame)
+    cap.release()
+    return np.array(frames)
+
+def predict_deepfake(video):
+    frames = process_video(video)
+    predictions = []
+    for frame in frames:
+        frame = np.expand_dims(frame, axis=0)  # Add batch dimension
+        frame = np.expand_dims(frame, axis=0)  # Add time dimension
+        prediction = model.predict(frame)
+        predictions.append(prediction[0][0])
+    
+    avg_prediction = np.mean(predictions)
+    result = "Real" if avg_prediction > 0.5 else "Fake"
+    confidence = avg_prediction if result == "Real" else 1 - avg_prediction
+    
+    return f"{result} with {confidence:.2%} confidence"
+
+iface = gr.Interface(
+    fn=predict_deepfake,
+    inputs=gr.Video(),
+    outputs="text",
+    title="Deepfake Detection",
+    description="Upload a video to check if it's a deepfake or not."
+)
+
+iface.launch()