Update ai_training.py

Author: ProjectZeroDays

src/ai/ai_training/ai_training.py

@@ -1 +1,66 @@
+import logging
+import tensorflow as tf
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
 
+class AITraining:
+    def __init__(self, model, data, labels):
+        self.model = model
+        self.data = data
+        self.labels = labels
+        self.logger = logging.getLogger(__name__)
+
+    def train_model(self, epochs=10, batch_size=32):
+        self.logger.info("Starting AI model training")
+        X_train, X_val, y_train, y_val = train_test_split(self.data, self.labels, test_size=0.2, random_state=42)
+        self.model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_val, y_val))
+        self.logger.info("AI model training completed")
+
+    def evaluate_model(self):
+        self.logger.info("Evaluating AI model performance")
+        X_train, X_val, y_train, y_val = train_test_split(self.data, self.labels, test_size=0.2, random_state=42)
+        y_pred = self.model.predict(X_val)
+        y_pred = (y_pred > 0.5).astype(int)
+        accuracy = accuracy_score(y_val, y_pred)
+        precision = precision_score(y_val, y_pred, average='weighted', zero_division=0)
+        recall = recall_score(y_val, y_pred, average='weighted', zero_division=0)
+        f1 = f1_score(y_val, y_pred, average='weighted', zero_division=0)
+        self.logger.info(f"Model Evaluation: Accuracy={accuracy:.4f}, Precision={precision:.4f}, Recall={recall:.4f}, F1-Score={f1:.4f}")
+        return accuracy, precision, recall, f1
+
+    def integrate_vLLM_models(self, vllm_model_paths):
+        self.logger.info("Integrating vLLM models")
+        self.vllm_models = []
+        for model_path in vllm_model_paths:
+            try:
+                vllm_model = tf.keras.models.load_model(model_path)
+                self.vllm_models.append(vllm_model)
+                self.logger.info(f"vLLM model loaded from {model_path}")
+            except Exception as e:
+                self.logger.error(f"Error loading vLLM model from {model_path}: {e}")
+
+    def build_custom_dashboard(self):
+        self.logger.info("Building custom dashboard for monitoring and training vLLM models")
+        # Placeholder for custom dashboard implementation
+        dashboard = {
+            "vLLM_models": [model.summary() for model in self.vllm_models],
+            "training_data": self.data,
+            "labels": self.labels
+        }
+        self.logger.info("Custom dashboard built successfully")
+        return dashboard
+
+    def monitor_vLLM_models(self):
+        self.logger.info("Monitoring vLLM models")
+        # Placeholder for monitoring implementation
+        monitoring_data = {
+            "vLLM_models": [model.evaluate(self.data, self.labels) for model in self.vllm_models]
+        }
+        self.logger.info("vLLM models monitoring data collected successfully")
+        return monitoring_data
+
+    def manually_train_vLLM_models(self, additional_data, additional_labels):
+        self.logger.info("Manually training vLLM models")
+        for model in self.vllm_models:
+            model.fit(additional_data, additional_labels, epochs=10, batch_size=32)
+        self.logger.info("Manual training of vLLM models completed")