"""
Claude Model Provider Implementation for Claude MPM Framework
=============================================================

WHY: Provides cloud-based content analysis via Claude API as fallback
when local models are unavailable or for tasks requiring higher quality.

DESIGN DECISION: This is a placeholder/wrapper implementation that assumes
Claude API integration will be added later. For Phase 1, this provides the
interface contract that the router expects.

FUTURE: Will integrate with Anthropic SDK when content agent needs cloud fallback.

Note: This implementation returns mock responses for Phase 1. Phase 2 will
add actual Claude API integration.
"""

from typing import Any

from claude_mpm.services.core.interfaces.model import ModelCapability, ModelResponse
from claude_mpm.services.model.base_provider import BaseModelProvider


class ClaudeProvider(BaseModelProvider):
    """
    Claude API model provider (cloud-based).

    WHY: Provides high-quality cloud-based content analysis with guaranteed
    availability as fallback from local models.

    Configuration:
        api_key: Anthropic API key (optional, can use env var)
        model: Default model to use (default: claude-3-5-sonnet-20241022)
        max_tokens: Maximum response tokens (default: 4096)

    Usage:
        provider = ClaudeProvider(config={
            "api_key": "sk-ant-...",
            "model": "claude-3-5-sonnet-20241022"
        })

        response = await provider.analyze_content(
            content="Your content",
            task=ModelCapability.SEO_ANALYSIS
        )

    Note: Phase 1 implementation provides interface. Phase 2 will add
    actual Claude API integration.
    """

    # Available Claude models
    AVAILABLE_MODELS = [
        "claude-3-5-sonnet-20241022",
        "claude-3-5-haiku-20241022",
        "claude-3-opus-20240229",
        "claude-3-sonnet-20240229",
        "claude-3-haiku-20240307",
    ]

    def __init__(self, config: dict[str, Any] | None = None):
        """
        Initialize Claude provider.

        Args:
            config: Configuration dict with:
                - api_key: Anthropic API key
                - model: Default model
                - max_tokens: Maximum response tokens
        """
        super().__init__(provider_name="claude", config=config or {})

        self.api_key = self.get_config("api_key", None)
        self.default_model = self.get_config("model", "claude-3-5-sonnet-20241022")
        self.max_tokens = self.get_config("max_tokens", 4096)

        # TODO Phase 2: Initialize Anthropic SDK client
        self._client = None

    async def initialize(self) -> bool:
        """
        Initialize Claude provider.

        Returns:
            True if initialization successful
        """
        self.log_info("Initializing Claude provider")

        # TODO Phase 2: Initialize Anthropic SDK
        # if not self.api_key:
        #     self.log_warning("No API key provided, checking environment")
        #     self.api_key = os.getenv("ANTHROPIC_API_KEY")
        #
        # if not self.api_key:
        #     self.log_error("No Claude API key available")
        #     return False
        #
        # try:
        #     from anthropic import AsyncAnthropic
        #     self._client = AsyncAnthropic(api_key=self.api_key)
        # except ImportError:
        #     self.log_error("anthropic package not installed")
        #     return False

        # Phase 1: Mock initialization
        self.log_info("Claude provider initialized (Phase 1 mock mode)")
        self._initialized = True
        return True

    async def shutdown(self) -> None:
        """Shutdown provider and cleanup resources."""
        self.log_info("Shutting down Claude provider")

        # TODO Phase 2: Cleanup Anthropic client
        if self._client:
            # await self._client.close()
            pass

        self._shutdown = True

    async def is_available(self) -> bool:
        """
        Check if Claude API is available.

        WHY: Cloud APIs are generally always available if API key is valid.

        Returns:
            True if API key configured, False otherwise
        """
        # Phase 1: Return True to enable testing
        # Phase 2: Check API key and test connection
        return True

    async def get_available_models(self) -> list[str]:
        """
        List available Claude models.

        Returns:
            List of model identifiers
        """
        return self.AVAILABLE_MODELS

    def get_supported_capabilities(self) -> list[ModelCapability]:
        """
        Return all supported capabilities.

        WHY: Claude supports all content analysis capabilities.

        Returns:
            List of all ModelCapability values
        """
        return list(ModelCapability)

    async def analyze_content(
        self,
        content: str,
        task: ModelCapability,
        model: str | None = None,
        **kwargs,
    ) -> ModelResponse:
        """
        Analyze content using Claude API.

        Args:
            content: Text content to analyze
            task: Type of analysis
            model: Optional specific model
            **kwargs: Additional options:
                - temperature: Sampling temperature
                - max_tokens: Maximum response tokens

        Returns:
            ModelResponse with analysis results
        """
        # Validate content
        if not self.validate_content(content, max_length=200000):
            return self.create_response(
                success=False,
                model=model or self.default_model,
                task=task,
                error="Invalid content provided",
            )

        # Check if initialized
        if not self._initialized:
            await self.initialize()

        if not self._initialized:
            return self.create_response(
                success=False,
                model=model or self.default_model,
                task=task,
                error="Claude provider not initialized",
            )

        # Select model
        selected_model = model or self.default_model

        # Generate prompt
        prompt = self.get_task_prompt(task, content)

        # Call Claude API with retry logic
        return await self.analyze_with_retry(
            self._call_claude_api,
            prompt,
            task,
            selected_model,
            **kwargs,
        )

    async def _call_claude_api(
        self,
        prompt: str,
        task: ModelCapability,
        model: str,
        **kwargs,
    ) -> ModelResponse:
        """
        Internal method to call Claude API.

        Args:
            prompt: Generated prompt
            task: Task capability
            model: Model to use
            **kwargs: Additional options

        Returns:
            ModelResponse
        """
        # TODO Phase 2: Implement actual Claude API call
        # try:
        #     message = await self._client.messages.create(
        #         model=model,
        #         max_tokens=kwargs.get("max_tokens", self.max_tokens),
        #         temperature=kwargs.get("temperature", 0.7),
        #         messages=[{
        #             "role": "user",
        #             "content": prompt
        #         }]
        #     )
        #
        #     result_text = message.content[0].text
        #
        #     metadata = {
        #         "model": model,
        #         "stop_reason": message.stop_reason,
        #         "usage": {
        #             "input_tokens": message.usage.input_tokens,
        #             "output_tokens": message.usage.output_tokens,
        #         }
        #     }
        #
        #     return self.create_response(
        #         success=True,
        #         model=model,
        #         task=task,
        #         result=result_text,
        #         metadata=metadata,
        #     )
        #
        # except Exception as e:
        #     return self.create_response(
        #         success=False,
        #         model=model,
        #         task=task,
        #         error=f"Claude API error: {str(e)}",
        #     )

        # Phase 1: Return mock response for testing
        mock_analysis = self._generate_mock_analysis(task)

        return self.create_response(
            success=True,
            model=model,
            task=task,
            result=mock_analysis,
            metadata={
                "phase": "1",
                "mode": "mock",
                "note": "Phase 2 will implement actual Claude API integration",
            },
        )

    def _generate_mock_analysis(self, task: ModelCapability) -> str:
        """
        Generate mock analysis for Phase 1 testing.

        Args:
            task: Task capability

        Returns:
            Mock analysis text
        """
        mock_responses = {
            ModelCapability.SEO_ANALYSIS: """SEO Analysis (Mock):
1. Primary keywords: content, analysis, optimization
2. Keyword density: Moderate (2-3%)
3. Meta description: Well-structured content with clear focus
4. Title optimization: Consider adding target keywords
5. Content structure: Good use of headers
6. SEO Score: 75/100 - Good foundation, room for improvement""",
            ModelCapability.READABILITY: """Readability Analysis (Mock):
1. Flesch Reading Ease: 65 (Standard)
2. Average sentence length: 15 words
3. Complex words: 12% of total
4. Grade level: 10th grade
5. Suggestions: Consider simplifying complex sentences
6. Overall rating: Medium readability""",
            ModelCapability.GRAMMAR: """Grammar Check (Mock):
1. Grammatical errors: None detected
2. Spelling: All correct
3. Punctuation: Appropriate usage
4. Style: Consistent and clear
5. Clarity: Well-expressed ideas
6. Quality score: 95/100 - Excellent""",
            ModelCapability.SUMMARIZATION: """Summary (Mock):
Main Points:
- Content provides valuable information
- Structure is logical and well-organized
- Key concepts are clearly explained

TL;DR: Well-structured content with clear messaging and good organization.""",
            ModelCapability.KEYWORD_EXTRACTION: """Keyword Extraction (Mock):
Primary Keywords:
1. content (relevance: 0.95)
2. analysis (relevance: 0.88)
3. quality (relevance: 0.82)
4. structure (relevance: 0.75)
5. optimization (relevance: 0.70)

Long-tail phrases:
- "content analysis"
- "quality optimization"
- "structured content"

Suggested additions: performance, effectiveness, improvement""",
            ModelCapability.ACCESSIBILITY: """Accessibility Analysis (Mock):
1. Language complexity: Moderate (10th grade level)
2. Inclusivity: Good, neutral language used
3. Plain language: Some jargon, consider simplification
4. Potential barriers: Technical terminology may challenge some readers
5. WCAG compliance: Meets basic guidelines
6. Accessibility score: 80/100 - Good with room for improvement""",
            ModelCapability.SENTIMENT: """Sentiment Analysis (Mock):
1. Overall sentiment: Positive
2. Sentiment score: +0.6 (Moderately positive)
3. Emotional tone: Professional, informative
4. Audience perception: Likely to be well-received
5. Tone consistency: Maintained throughout""",
            ModelCapability.GENERAL: """General Analysis (Mock):
1. Overview: Well-structured content with clear objectives
2. Quality: High-quality writing with good organization
3. Structure: Logical flow with appropriate sections
4. Improvements: Consider adding more examples
5. Effectiveness: 80/100 - Strong overall performance""",
        }

        return mock_responses.get(
            task,
            "Mock analysis completed. Phase 2 will provide detailed results.",
        )

    async def get_model_info(self, model: str) -> dict[str, Any]:
        """
        Get detailed information about a Claude model.

        Args:
            model: Model identifier

        Returns:
            Dictionary with model information
        """
        # Model information based on public documentation
        model_info = {
            "claude-3-5-sonnet-20241022": {
                "name": "Claude 3.5 Sonnet",
                "version": "20241022",
                "context_window": 200000,
                "max_output": 8192,
                "capabilities": ["analysis", "reasoning", "coding", "writing"],
                "speed": "fast",
                "cost": "medium",
            },
            "claude-3-5-haiku-20241022": {
                "name": "Claude 3.5 Haiku",
                "version": "20241022",
                "context_window": 200000,
                "max_output": 8192,
                "capabilities": ["quick_analysis", "summarization"],
                "speed": "fastest",
                "cost": "low",
            },
            "claude-3-opus-20240229": {
                "name": "Claude 3 Opus",
                "version": "20240229",
                "context_window": 200000,
                "max_output": 4096,
                "capabilities": ["complex_reasoning", "detailed_analysis"],
                "speed": "slower",
                "cost": "high",
            },
        }

        return model_info.get(
            model,
            {
                "name": model,
                "error": "Model information not available",
            },
        )


__all__ = ["ClaudeProvider"]
