"""
Vishwas - Trust, Fairness & Explainability API.
"""

from typing import TYPE_CHECKING, Any, Dict, List, Optional

if TYPE_CHECKING:
    from rotavision.client import Rotavision


class Vishwas:
    """
    Vishwas API client for bias detection and explainability.

    Usage:
        from rotavision import Rotavision

        client = Rotavision(api_key="rv_...")

        result = client.vishwas.analyze(
            text="Loan application response...",
            demographics=["caste", "gender", "region"]
        )

        print(result.bias_score)
    """

    def __init__(self, client: "Rotavision"):
        self._client = client

    def analyze(
        self,
        text: str,
        context: Optional[str] = None,
        demographics: Optional[List[str]] = None,
        model_id: Optional[str] = None,
        threshold: float = 0.7,
    ) -> Dict[str, Any]:
        """
        Analyze text for bias across Indian demographic dimensions.

        Args:
            text: Text or model output to analyze
            context: Business context (lending_decision, hiring, insurance, education)
            demographics: Dimensions to check (caste, religion, region, gender, language, age)
            model_id: Your model identifier for tracking
            threshold: Bias detection threshold (0-1, default: 0.7)

        Returns:
            Analysis result with bias_score, dimensions, and recommendations
        """
        raise NotImplementedError("Coming soon. Contact developers@rotavision.com")

    def explain(
        self,
        decision: str,
        inputs: Dict[str, Any],
        model_id: Optional[str] = None,
        format: str = "rti_ready",
        language: str = "en",
    ) -> Dict[str, Any]:
        """
        Generate RTI-ready explanations for AI decisions.

        Args:
            decision: The decision outcome to explain
            inputs: Input features that led to the decision
            model_id: Your model identifier
            format: Explanation format (rti_ready, technical, summary)
            language: Output language (en, hi, etc.)

        Returns:
            Explanation with summary, factors, and counterfactuals
        """
        raise NotImplementedError("Coming soon. Contact developers@rotavision.com")

    def report(
        self,
        model_id: str,
        start_date: Optional[str] = None,
        end_date: Optional[str] = None,
    ) -> Dict[str, Any]:
        """
        Get bias analysis report for a model.

        Args:
            model_id: Your model identifier
            start_date: Report start date (ISO format)
            end_date: Report end date (ISO format)

        Returns:
            Aggregated bias metrics and trends
        """
        raise NotImplementedError("Coming soon. Contact developers@rotavision.com")
