"""
Database-Driven AI Workflows

Implements AI model training, inference, and pipeline orchestration
using database as the data source via DataFlow integration.

Key Features:
- Train ML models directly from database tables
- Real-time inference with database context enrichment
- Automated pipeline orchestration
- Model versioning and metadata storage in database
"""

from dataclasses import dataclass
from typing import Any, List, Optional

from kaizen.integrations.dataflow.base import DataFlowAwareAgent
from kaizen.signatures.core import InputField, OutputField, Signature

# ============================================================================
# Signatures
# ============================================================================


class TrainingPipelineSignature(Signature):
    """Database-driven model training workflow."""

    training_data_sample: list = InputField(
        desc="Sample of training data from database (first 100 rows)"
    )
    table_schema: dict = InputField(
        desc="Database table schema with column names and types"
    )
    model_objective: str = InputField(
        desc="Description of what the model should predict or accomplish"
    )

    feature_columns: list = OutputField(
        desc="Columns to use as input features for the model"
    )
    target_column: str = OutputField(desc="Column to predict (target variable)")
    preprocessing_steps: list = OutputField(
        desc="Required data preprocessing steps (normalization, encoding, etc.)"
    )
    model_config: dict = OutputField(
        desc="Recommended model configuration (algorithm, hyperparameters)"
    )


class InferenceSignature(Signature):
    """Real-time inference with database integration."""

    input_data: dict = InputField(desc="Input data for making prediction")
    model_metadata: dict = InputField(
        desc="Model metadata retrieved from database (features, target, config)"
    )
    context_data: dict = InputField(
        desc="Additional context enrichment from database (history, related data)"
    )

    prediction: Any = OutputField(desc="Model prediction result")
    confidence: float = OutputField(desc="Prediction confidence score (0.0 to 1.0)")
    explanation: str = OutputField(desc="Human-readable explanation of the prediction")


class PipelineOrchestrationSignature(Signature):
    """Orchestrate complex data-AI pipelines."""

    pipeline_config: dict = InputField(
        desc="Pipeline configuration (name, objective, data sources)"
    )
    data_sources: list = InputField(desc="Available data source schemas from database")

    execution_plan: list = OutputField(desc="Ordered list of pipeline steps to execute")
    resource_requirements: dict = OutputField(
        desc="Estimated computational resources needed (memory, CPU, time)"
    )
    monitoring_metrics: list = OutputField(
        desc="Key metrics to track during pipeline execution"
    )


# ============================================================================
# Database-Driven AI Agents
# ============================================================================


class DBTrainingPipeline(DataFlowAwareAgent):
    """
    Database-driven AI model training workflow.

    Trains machine learning models using data directly from database tables.
    Automatically analyzes schema, recommends features, and stores model
    metadata back in the database for versioning and tracking.

    Example:
        >>> db = DataFlow("postgresql://localhost/mydb")
        >>> trainer = DBTrainingPipeline(config=config, db=db)
        >>> result = trainer.train_from_database(
        ...     table="customers",
        ...     model_objective="Predict customer churn"
        ... )
        >>> print(f"Model ID: {result['model_id']}, Accuracy: {result['accuracy']}")
    """

    def __init__(self, config, db: Optional[Any] = None):
        """
        Initialize training pipeline.

        Args:
            config: Agent configuration with LLM settings
            db: Optional DataFlow database instance
        """
        super().__init__(config, signature=TrainingPipelineSignature(), db=db)
        self.trained_models = {}

    def train_from_database(
        self,
        table: str,
        model_objective: str,
        training_filter: Optional[dict] = None,
        validation_split: float = 0.2,
    ) -> dict:
        """
        Train AI model using data from database.

        Args:
            table: Database table containing training data
            model_objective: Description of what model should predict
            training_filter: Optional filter to select training data subset
            validation_split: Fraction of data for validation (default 0.2)

        Returns:
            Dict with model_id, metrics, features, and metadata

        Example:
            >>> result = trainer.train_from_database(
            ...     table="customers",
            ...     model_objective="Predict churn based on purchase history",
            ...     training_filter={"active": True}
            ... )
        """
        # Fetch training data from database
        training_data = self.query_database(
            table=table,
            filter=training_filter or {},
            limit=10000,  # Configurable limit for training data
        )

        if not training_data:
            raise ValueError(f"No training data found in table '{table}'")

        # Get table schema for context
        schema = (
            self.db_connection.get_table_schema(table)
            if self.db_connection
            else {
                "columns": list(training_data[0].keys()) if training_data else [],
                "types": {},
            }
        )

        # Generate training plan via LLM
        training_plan = self.run(
            training_data_sample=training_data[:100],  # Sample for LLM analysis
            table_schema=schema,
            model_objective=model_objective,
        )

        # Extract features and target from data
        features = self._extract_features(
            training_data, training_plan["feature_columns"]
        )
        target = self._extract_target(training_data, training_plan["target_column"])

        # Train model (simplified - real implementation would use scikit-learn, etc.)
        model = self._train_model(features, target, training_plan["model_config"])

        # Store model metadata in database
        model_metadata = self._save_model_metadata(
            model=model, table=table, training_plan=training_plan
        )

        # Cache trained model
        self.trained_models[model_metadata["id"]] = model

        return {
            "model_id": model_metadata["id"],
            "accuracy": getattr(model, "accuracy", 0.0),
            "features": training_plan["feature_columns"],
            "metadata": model_metadata,
        }

    def _extract_features(
        self, data: List[dict], feature_columns: List[str]
    ) -> List[List[Any]]:
        """Extract feature columns from raw data."""
        features = []
        for record in data:
            feature_row = [record.get(col) for col in feature_columns]
            features.append(feature_row)
        return features

    def _extract_target(self, data: List[dict], target_column: str) -> List[Any]:
        """Extract target column from raw data."""
        return [record.get(target_column) for record in data]

    def _train_model(self, features: List[List[Any]], target: List[Any], config: dict):
        """
        Train model (simplified mock implementation).

        Real implementation would use scikit-learn, XGBoost, etc.
        """

        # Mock model for testing
        @dataclass
        class MockModel:
            features: List[List[Any]]
            target: List[Any]
            config: dict
            accuracy: float = 0.85

            def predict(self, X):
                # Simple mock prediction
                return target[: len(X)] if target else [None] * len(X)

        model = MockModel(features=features, target=target, config=config)
        return model

    def _save_model_metadata(self, model: Any, table: str, training_plan: dict) -> dict:
        """
        Save model metadata to database.

        Stores information about trained model including source table,
        features used, accuracy, and versioning information.
        """
        import hashlib
        import time

        # Generate model ID
        model_id = hashlib.md5(
            f"{table}_{training_plan['target_column']}_{time.time()}".encode()
        ).hexdigest()[:12]

        metadata = {
            "id": model_id,
            "source_table": table,
            "features": training_plan["feature_columns"],
            "target": training_plan["target_column"],
            "accuracy": getattr(model, "accuracy", None),
            "created_at": "2025-10-05",  # Would use datetime.now()
            "version": "1.0.0",
            "preprocessing_steps": training_plan.get("preprocessing_steps", []),
            "model_config": training_plan.get("model_config", {}),
        }

        # Insert into model_metadata table via DataFlow
        if self.db_connection:
            try:
                result = self.insert_database(table="model_metadata", data=metadata)
                # Update with database-generated ID if available
                if result and "id" in result:
                    metadata["id"] = result["id"]
            except Exception:
                # If model_metadata table doesn't exist, continue with generated ID
                pass

        return metadata


class InferencePipeline(DataFlowAwareAgent):
    """
    Real-time inference with database integration.

    Performs predictions using trained models with automatic database
    context enrichment (historical data, related entities) and stores
    inference results for tracking and auditing.

    Example:
        >>> pipeline = InferencePipeline(config=config, db=db)
        >>> prediction = pipeline.infer_with_db_context(
        ...     model_id="model-123",
        ...     input_data={"age": 35, "income": 75000},
        ...     store_result=True
        ... )
        >>> print(f"Prediction: {prediction['prediction']}, "
        ...       f"Confidence: {prediction['confidence']}")
    """

    def __init__(self, config, db: Optional[Any] = None):
        """
        Initialize inference pipeline.

        Args:
            config: Agent configuration with LLM settings
            db: Optional DataFlow database instance
        """
        super().__init__(config, signature=InferenceSignature(), db=db)

    def infer_with_db_context(
        self, model_id: str, input_data: dict, store_result: bool = True
    ) -> dict:
        """
        Run inference using model from database with context enrichment.

        Args:
            model_id: ID of trained model in database
            input_data: Input data for making prediction
            store_result: Whether to store inference result in database

        Returns:
            Dict with prediction, confidence, and explanation

        Example:
            >>> result = pipeline.infer_with_db_context(
            ...     model_id="abc123",
            ...     input_data={"user_id": 456, "action": "purchase"},
            ...     store_result=True
            ... )
        """
        # Fetch model metadata from database
        model_metadata_list = self.query_database(
            table="model_metadata", filter={"id": model_id}
        )

        if not model_metadata_list:
            # Fallback to mock metadata if table doesn't exist
            model_metadata = {
                "id": model_id,
                "features": list(input_data.keys()),
                "target": "prediction",
                "accuracy": 0.85,
            }
        else:
            model_metadata = model_metadata_list[0]

        # Enrich input with database context
        context_data = self._fetch_context_data(input_data, model_metadata)

        # Run inference via LLM
        result = self.run(
            input_data=input_data,
            model_metadata=model_metadata,
            context_data=context_data,
        )

        # Store inference result if requested
        if store_result and self.db_connection:
            self._store_inference_result(
                model_id=model_id, input_data=input_data, result=result
            )

        return result

    def _fetch_context_data(self, input_data: dict, model_metadata: dict) -> dict:
        """
        Fetch relevant context from database.

        Enriches input data with historical information, related entities,
        and other contextual data that may improve prediction accuracy.
        """
        context = {"historical_data": [], "related_entities": []}

        # Example: If input has user_id, fetch user history
        if "user_id" in input_data and self.db_connection:
            try:
                user_history = self.query_database(
                    table="user_history",
                    filter={"user_id": input_data["user_id"]},
                    limit=10,
                )
                context["historical_data"] = user_history
            except Exception:
                # Table might not exist, continue with empty context
                pass

        return context

    def _store_inference_result(self, model_id: str, input_data: dict, result: dict):
        """Store inference result in database for tracking and auditing."""
        inference_record = {
            "model_id": model_id,
            "input_data": str(input_data),
            "prediction": str(result.get("prediction")),
            "confidence": result.get("confidence", 0.0),
            "timestamp": "2025-10-05T00:00:00Z",  # Would use datetime.now()
        }

        try:
            self.insert_database(table="inference_results", data=inference_record)
        except Exception:
            # If table doesn't exist, skip storage
            pass


class PipelineOrchestrator(DataFlowAwareAgent):
    """
    Orchestrate automated data-AI pipelines.

    Creates and executes end-to-end workflows combining data extraction,
    transformation, model training, and deployment. Monitors performance
    and stores pipeline metrics in database.

    Example:
        >>> orchestrator = PipelineOrchestrator(config=config, db=db)
        >>> result = orchestrator.create_pipeline(
        ...     pipeline_name="churn_prediction",
        ...     data_sources=["customers", "transactions"],
        ...     objective="Predict customer churn"
        ... )
        >>> print(f"Pipeline: {result['pipeline_name']}, "
        ...       f"Steps: {result['steps_executed']}")
    """

    def __init__(self, config, db: Optional[Any] = None):
        """
        Initialize pipeline orchestrator.

        Args:
            config: Agent configuration with LLM settings
            db: Optional DataFlow database instance
        """
        super().__init__(config, signature=PipelineOrchestrationSignature(), db=db)

    def create_pipeline(
        self, pipeline_name: str, data_sources: List[str], objective: str
    ) -> dict:
        """
        Create and execute automated data-AI pipeline.

        Args:
            pipeline_name: Name for the pipeline
            data_sources: List of database tables to use as data sources
            objective: Description of what the pipeline should accomplish

        Returns:
            Dict with pipeline execution results and metrics

        Example:
            >>> result = orchestrator.create_pipeline(
            ...     pipeline_name="customer_analytics",
            ...     data_sources=["customers", "orders", "reviews"],
            ...     objective="Analyze customer behavior and predict LTV"
            ... )
        """
        # Analyze data sources - get schemas
        schemas = {}
        for table in data_sources:
            if self.db_connection:
                try:
                    schema = self.db_connection.get_table_schema(table)
                    schemas[table] = schema
                except Exception:
                    # If schema not available, create mock
                    schemas[table] = {"columns": [], "types": {}}
            else:
                schemas[table] = {"columns": [], "types": {}}

        # Generate pipeline plan via LLM
        pipeline_config = {
            "name": pipeline_name,
            "objective": objective,
            "data_sources": data_sources,
        }

        plan = self.run(
            pipeline_config=pipeline_config, data_sources=list(schemas.values())
        )

        # Execute pipeline steps
        results = self._execute_pipeline(plan["execution_plan"])

        # Collect and store metrics
        metrics = self._collect_metrics(results)
        self._store_pipeline_metrics(pipeline_name, metrics)

        return {
            "pipeline_name": pipeline_name,
            "steps_executed": len(plan["execution_plan"]),
            "results": results,
            "metrics": metrics,
        }

    def _execute_pipeline(self, execution_plan: List[dict]) -> dict:
        """
        Execute pipeline steps in order.

        Real implementation would orchestrate data extraction, transformation,
        model training, evaluation, and deployment steps.
        """
        results = {}

        for step in execution_plan:
            step_name = step.get("step", "unknown")

            # Mock execution for different step types
            if step_name == "extract":
                results[step_name] = {
                    "rows": 1000,
                    "source": step.get("source", "unknown"),
                }
            elif step_name == "transform":
                results[step_name] = {
                    "processed": 1000,
                    "operation": step.get("operation", "unknown"),
                }
            elif step_name == "train":
                results[step_name] = {
                    "accuracy": 0.89,
                    "algorithm": step.get("algorithm", "unknown"),
                }
            else:
                results[step_name] = {"status": "completed"}

        return results

    def _collect_metrics(self, pipeline_results: dict) -> dict:
        """Collect performance metrics from pipeline execution."""
        metrics = {
            "total_steps": len(pipeline_results),
            "successful_steps": len([r for r in pipeline_results.values() if r]),
        }

        # Extract specific metrics from results
        if "train" in pipeline_results:
            metrics["accuracy"] = pipeline_results["train"].get("accuracy", 0.0)

        if "extract" in pipeline_results:
            metrics["rows_processed"] = pipeline_results["extract"].get("rows", 0)

        return metrics

    def _store_pipeline_metrics(self, pipeline_name: str, metrics: dict):
        """Store pipeline metrics in database for tracking."""
        pipeline_record = {
            "pipeline_name": pipeline_name,
            "metrics": str(metrics),
            "timestamp": "2025-10-05T00:00:00Z",
        }

        if self.db_connection:
            try:
                self.insert_database(table="pipeline_metrics", data=pipeline_record)
            except Exception:
                # If table doesn't exist, skip storage
                pass
