Socius AI
Predictive Social Media Intelligence

# Socius AI Core Prediction Algorithm
import tensorflow as tf
from transformers import TFAutoModelForSequenceClassification

class SociusPredictor:
    def __init__(self):
        self.temporal_model = tf.keras.models.load_model('temporal_nn.h5')
        self.content_model = TFAutoModelForSequenceClassification.from_pretrained('socius-content-bert')
        self.optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)

    def predict_engagement(self, content, timing_features):
        """Predict engagement score for content at given time"""
        # Temporal features analysis
        time_score = self.temporal_model.predict(timing_features)

        # Content features analysis
        content_inputs = self._preprocess_content(content)
        content_score = self.content_model(content_inputs).logits

        # Combined prediction
        combined = 0.6 * content_score + 0.4 * time_score
        return combined * 100  # Convert to percentage

    def optimize_schedule(self, content_batch, time_window):
        """Find optimal posting time within window"""
        time_points = np.linspace(time_window[0], time_window[1], 1440)
        predictions = [self.predict_engagement(content_batch, t) for t in time_points]
        optimal_idx = np.argmax(predictions)
        return time_points[optimal_idx], predictions[optimal_idx]

    def _preprocess_content(self, content):
        # NLP preprocessing pipeline
        return tokenizer(content, return_tensors='tf', truncation=True, padding=True)

# Initialize predictor
predictor = SociusPredictor()

# Example prediction
content = "New AI breakthrough in social media optimization..."
timing_features = get_current_timing_features()
score = predictor.predict_engagement(content, timing_features)
print(f"Predicted engagement: {score:.1f}%")