#!/usr/bin/env python3
"""
AI Model Selection Test - Prints prioritized fallback model lists used for AI calls

Scenarios mirror typical calls in workflows/ (task planning, action planning,
analysis, and react-mode decisions), showing which models are shortlisted and
their final prioritized order after rating and cost tie-breaking.
"""

import asyncio
import os
import sys
from typing import List, Tuple


# Ensure gateway is on path when running directly
sys.path.append(os.path.dirname(__file__))

from modules.features.chatPlayground.mainChatPlayground import getServices
from modules.datamodels.datamodelAi import (
    AiCallOptions,
    OperationTypeEnum,
    PriorityEnum,
    ProcessingModeEnum,
)
from modules.datamodels.datamodelUam import User
from modules.aicore.aicoreModelRegistry import modelRegistry
from modules.aicore.aicoreModelSelector import model_selector


class ModelSelectionTester:
    def __init__(self) -> None:
        testUser = User(
            id="test_user_models",
            username="test_models",
            email="test@example.com",
            fullName="Test Models",
            language="en",
            mandateId="test_mandate",
        )
        self.services = getServices(testUser, None)

    async def initialize(self) -> None:
        from modules.services.serviceAi.mainServiceAi import AiService

        self.services.ai = await AiService.create(self.services)

    async def _printFallbackList(self, title: str, prompt: str, options: AiCallOptions) -> None:
        print(f"\n{'='*80}")
        print(f"{title}")
        print(f"{'='*80}")
        print(
            f"Operation={options.operationType.name}, Priority={options.priority.name}, ProcessingMode={options.processingMode.name}"
        )

        availableModels = modelRegistry.getAvailableModels()
        fallbackModels = model_selector.getFallbackModels(
            prompt=prompt,
            context="",
            options=options,
            availableModels=availableModels,
        )

        if not fallbackModels:
            print("No suitable models found (capability filter returned empty list).")
            return

        print("Prioritized fallback model sequence (name | quality | speed | $/1k in | ctx):")
        for idx, m in enumerate(fallbackModels, 1):
            costIn = getattr(m, "costPer1kTokensInput", 0.0)
            print(
                f" {idx:>2}. {m.name} | Q={getattr(m, 'qualityRating', 0)} | S={getattr(m, 'speedRating', 0)} | ${costIn:.4f} | ctx={getattr(m, 'contextLength', 0)}"
            )

    async def run(self) -> None:
        # Scenarios reflecting workflows/
        scenarios: List[Tuple[str, str, AiCallOptions]] = []

        # Task planning (taskPlanner, modeActionplan)
        scenarios.append(
            (
                "PLAN - Quality, Detailed",
                "Task planning for a multi-step business workflow.",
                AiCallOptions(
                    operationType=OperationTypeEnum.PLAN,
                    priority=PriorityEnum.QUALITY,
                    compressPrompt=False,
                    compressContext=False,
                    processingMode=ProcessingModeEnum.DETAILED,
                    maxCost=0.10,
                    maxProcessingTime=30,
                ),
            )
        )

        # Result validation / analysis (modeActionplan)
        scenarios.append(
            (
                "ANALYSE - Balanced, Advanced",
                "Validate action plan correctness and completeness.",
                AiCallOptions(
                    operationType=OperationTypeEnum.ANALYSE,
                    priority=PriorityEnum.BALANCED,
                    compressPrompt=True,
                    compressContext=False,
                    processingMode=ProcessingModeEnum.ADVANCED,
                    maxCost=0.05,
                    maxProcessingTime=30,
                ),
            )
        )

        # React mode - action selection (modeReact)
        scenarios.append(
            (
                "GENERAL - Balanced, Advanced (React: action selection)",
                "Select next best action from context and state.",
                AiCallOptions(
                    operationType=OperationTypeEnum.GENERAL,
                    priority=PriorityEnum.BALANCED,
                    compressPrompt=True,
                    compressContext=True,
                    processingMode=ProcessingModeEnum.ADVANCED,
                    maxCost=0.03,
                    maxProcessingTime=20,
                ),
            )
        )

        # React mode - parameter suggestion (modeReact example)
        scenarios.append(
            (
                "ANALYSE - Balanced, Advanced (React: parameter suggestion)",
                "Suggest parameters for the selected action as JSON.",
                AiCallOptions(
                    operationType=OperationTypeEnum.ANALYSE,
                    priority=PriorityEnum.BALANCED,
                    compressPrompt=True,
                    compressContext=False,
                    processingMode=ProcessingModeEnum.ADVANCED,
                    maxCost=0.05,
                    maxProcessingTime=30,
                    resultFormat="json",
                    temperature=0.3,
                ),
            )
        )

        # Iterate and print lists
        for title, prompt, options in scenarios:
            await self._printFallbackList(title, prompt, options)


async def main() -> None:
    tester = ModelSelectionTester()
    await tester.initialize()
    await tester.run()


if __name__ == "__main__":
    asyncio.run(main())