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import tensorflow as tf
import numpy as np

Example video features: quality, topic, length
video_features = np.array([[0.7, 0.2, 0.1]])

Example user engagement: likes, comments, shares
user_engagement = np.array([[10, 5, 8]])

Combine video features and engagement data
input_data = np.concatenate([video_features, user_engagement], axis=1)

Simple feedforward neural network model
inputs = tf.keras.Input(shape=(6,))
x = tf.keras.layers.Dense(16, activation='relu')(inputs)
x = tf.keras.layers.Dense(8, activation='relu')(x)
output = tf.keras.layers.Dense(1, activation='sigmoid')(x)

model = tf.keras.Model(inputs=inputs, outputs=output)
model.compile(optimizer='adam', loss='binary_crossentropy')

Labels: 1 means viral, 0 means not viral (for training)
labels = np.array([1])

Train the model
model.fit(input_data, labels, epochs=100, verbose=0)

Predict virality score
prediction = model.predict(input_data)
print(f"Predicted virality score: {prediction[0][0]:.2f}")