"""
AI Enhancement for How-To Guides (C3.3)

This module provides comprehensive AI enhancement for how-to guides with dual-mode support:
- API mode: Uses Anthropic API (requires ANTHROPIC_API_KEY)
- LOCAL mode: Uses a coding agent CLI (no API key needed)

Provides 5 automatic enhancements:
1. Step Descriptions - Natural language explanations (not just syntax)
2. Troubleshooting Solutions - Diagnostic flows + solutions for common errors
3. Prerequisites Explanations - Why each prerequisite is needed + setup instructions
4. Next Steps Suggestions - Related guides, variations, learning paths
5. Use Case Examples - Real-world scenarios showing when to use guide
"""

import json
import logging
from dataclasses import dataclass, field
from typing import TYPE_CHECKING

# Avoid circular imports by using TYPE_CHECKING
if TYPE_CHECKING:
    from .how_to_guide_builder import PrerequisiteItem, TroubleshootingItem
else:
    # Import at runtime to avoid circular dependency issues
    try:
        from .how_to_guide_builder import PrerequisiteItem, TroubleshootingItem
    except ImportError:
        # Fallback definitions if import fails
        @dataclass
        class PrerequisiteItem:
            name: str
            why: str
            setup: str

        @dataclass
        class TroubleshootingItem:
            problem: str
            symptoms: list[str] = field(default_factory=list)
            solution: str = ""
            diagnostic_steps: list[str] = field(default_factory=list)


logger = logging.getLogger(__name__)

# ANTHROPIC_AVAILABLE kept for backward compatibility — AgentClient handles detection
ANTHROPIC_AVAILABLE = True  # Detection delegated to AgentClient


@dataclass
class StepEnhancement:
    """Enhanced step information (internal use only)"""

    step_index: int
    explanation: str  # Natural language explanation
    variations: list[str] = field(default_factory=list)  # Alternative approaches


class GuideEnhancer:
    """
    AI enhancement for how-to guides with dual-mode support.

    Modes:
    - api: Uses Anthropic API (requires ANTHROPIC_API_KEY)
    - local: Uses a coding agent CLI (no API key needed)
    - auto: Automatically detect best mode
    """

    def __init__(self, mode: str = "auto"):
        """
        Initialize GuideEnhancer.

        Args:
            mode: Enhancement mode - "api", "local", or "auto"
        """
        from skill_seekers.cli.agent_client import AgentClient

        self._agent = AgentClient(mode=mode)
        self.mode = self._agent.mode

        if self._agent.is_available():
            self._agent.log_mode()
        else:
            logger.warning("⚠️  No AI enhancement available")
            self.mode = "none"

    def enhance_guide(self, guide_data: dict) -> dict:
        """
        Apply all 5 enhancements to a guide.

        Args:
            guide_data: Guide data dictionary with title, steps, etc.

        Returns:
            Enhanced guide data with all 5 enhancements
        """
        if self.mode == "none":
            logger.warning("⚠️  AI enhancement unavailable - returning original guide")
            return guide_data

        try:
            if self.mode == "api":
                return self._enhance_via_api(guide_data)
            else:
                return self._enhance_via_local(guide_data)
        except Exception as e:
            logger.error(f"❌ AI enhancement failed: {e}")
            logger.info("📝 Returning original guide without enhancement")
            return guide_data

    def enhance_step_descriptions(self, steps: list[dict]) -> list[StepEnhancement]:
        """
        Enhancement 1: Add natural language explanations to steps.

        Args:
            steps: List of workflow steps

        Returns:
            List of step enhancements with explanations
        """
        if not steps or self.mode == "none":
            return []

        prompt = self._create_step_description_prompt(steps)
        response = self._call_ai(prompt)

        if not response:
            return []

        try:
            data = json.loads(response)
            return [
                StepEnhancement(
                    step_index=item.get("step_index", i),
                    explanation=item.get("explanation", ""),
                    variations=item.get("variations", []),
                )
                for i, item in enumerate(data.get("step_descriptions", []))
            ]
        except (json.JSONDecodeError, KeyError) as e:
            logger.warning(f"⚠️  Failed to parse step descriptions: {e}")
            return []

    def enhance_troubleshooting(self, guide_data: dict) -> list[TroubleshootingItem]:
        """
        Enhancement 2: Generate diagnostic flows + solutions.

        Args:
            guide_data: Guide data with title, steps, language

        Returns:
            List of troubleshooting items with solutions
        """
        if self.mode == "none":
            return []

        prompt = self._create_troubleshooting_prompt(guide_data)
        response = self._call_ai(prompt)

        if not response:
            return []

        try:
            data = json.loads(response)
            return [
                TroubleshootingItem(
                    problem=item.get("problem", ""),
                    symptoms=item.get("symptoms", []),
                    diagnostic_steps=item.get("diagnostic_steps", []),
                    solution=item.get("solution", ""),
                )
                for item in data.get("troubleshooting", [])
            ]
        except (json.JSONDecodeError, KeyError) as e:
            logger.warning(f"⚠️  Failed to parse troubleshooting items: {e}")
            return []

    def enhance_prerequisites(self, prereqs: list[str]) -> list[PrerequisiteItem]:
        """
        Enhancement 3: Explain why prerequisites are needed.

        Args:
            prereqs: List of prerequisite names

        Returns:
            List of enhanced prerequisites with explanations
        """
        if not prereqs or self.mode == "none":
            return []

        prompt = self._create_prerequisites_prompt(prereqs)
        response = self._call_ai(prompt)

        if not response:
            return []

        try:
            data = json.loads(response)
            return [
                PrerequisiteItem(
                    name=item.get("name", ""), why=item.get("why", ""), setup=item.get("setup", "")
                )
                for item in data.get("prerequisites_detailed", [])
            ]
        except (json.JSONDecodeError, KeyError) as e:
            logger.warning(f"⚠️  Failed to parse prerequisites: {e}")
            return []

    def enhance_next_steps(self, guide_data: dict) -> list[str]:
        """
        Enhancement 4: Suggest related guides and variations.

        Args:
            guide_data: Guide data with title, topic

        Returns:
            List of next step suggestions
        """
        if self.mode == "none":
            return []

        prompt = self._create_next_steps_prompt(guide_data)
        response = self._call_ai(prompt)

        if not response:
            return []

        try:
            data = json.loads(response)
            return data.get("next_steps", [])
        except (json.JSONDecodeError, KeyError) as e:
            logger.warning(f"⚠️  Failed to parse next steps: {e}")
            return []

    def enhance_use_cases(self, guide_data: dict) -> list[str]:
        """
        Enhancement 5: Generate real-world scenario examples.

        Args:
            guide_data: Guide data with title, description

        Returns:
            List of use case examples
        """
        if self.mode == "none":
            return []

        prompt = self._create_use_cases_prompt(guide_data)
        response = self._call_ai(prompt)

        if not response:
            return []

        try:
            data = json.loads(response)
            return data.get("use_cases", [])
        except (json.JSONDecodeError, KeyError) as e:
            logger.warning(f"⚠️  Failed to parse use cases: {e}")
            return []

    # === AI Call Methods ===

    def _call_ai(self, prompt: str, max_tokens: int = 4000) -> str | None:
        """
        Call AI with the given prompt via AgentClient.

        Args:
            prompt: Prompt text
            max_tokens: Maximum tokens in response

        Returns:
            AI response text or None if failed
        """
        return self._agent.call(prompt, max_tokens=max_tokens)

    # === Prompt Creation Methods ===

    def _enhance_via_api(self, guide_data: dict) -> dict:
        """
        Enhance guide via API mode.

        Args:
            guide_data: Guide data dictionary

        Returns:
            Enhanced guide data
        """
        prompt = self._create_enhancement_prompt(guide_data)
        response = self._call_ai(prompt)

        if not response:
            return guide_data

        return self._parse_enhancement_response(response, guide_data)

    def _enhance_via_local(self, guide_data: dict) -> dict:
        """
        Enhance guide via LOCAL mode.

        Args:
            guide_data: Guide data dictionary

        Returns:
            Enhanced guide data
        """
        prompt = self._create_enhancement_prompt(guide_data)
        response = self._call_claude_local(prompt)

        if not response:
            return guide_data

        return self._parse_enhancement_response(response, guide_data)

    def _create_enhancement_prompt(self, guide_data: dict) -> str:
        """
        Create comprehensive enhancement prompt for all 5 enhancements.

        Args:
            guide_data: Guide data dictionary

        Returns:
            Complete prompt text
        """
        title = guide_data.get("title", "Unknown Guide")
        steps = guide_data.get("steps", [])
        language = guide_data.get("language", "python")
        prerequisites = guide_data.get("prerequisites", [])

        steps_text = self._format_steps_for_prompt(steps)
        prereqs_text = ", ".join(prerequisites) if prerequisites else "None specified"

        prompt = f"""I need you to enhance this how-to guide with 5 improvements:

CURRENT GUIDE:
Title: {title}
Steps: {len(steps)} steps
Code Language: {language}
Prerequisites: {prereqs_text}

STEP CODE:
{steps_text}

YOUR TASK - Provide JSON output with these 5 enhancements:

1. STEP_DESCRIPTIONS: For each step, write natural language explanation (not just syntax)
   - Explain what the code does
   - Explain why it's needed
   - Provide context and best practices

2. TROUBLESHOOTING: Generate 3-5 common errors with diagnostic flows + solutions
   - Identify likely errors for this type of workflow
   - Provide symptoms to recognize the error
   - Give diagnostic steps to confirm the issue
   - Provide clear solution steps

3. PREREQUISITES: Explain WHY each prerequisite is needed + setup instructions
   - For each prerequisite, explain its purpose
   - Provide installation/setup commands
   - Explain when it's used in the workflow

4. NEXT_STEPS: Suggest 3-5 related guides, variations, learning paths
   - Related guides that build on this one
   - Variations (e.g., async version, different approaches)
   - Next logical learning steps

5. USE_CASES: Provide 2-3 real-world scenarios when to use this guide
   - Specific situations where this workflow applies
   - Problems it solves
   - When NOT to use this approach

OUTPUT FORMAT (strict JSON):
{{
  "step_descriptions": [
    {{"step_index": 0, "explanation": "...", "variations": ["..."]}},
    {{"step_index": 1, "explanation": "...", "variations": ["..."]}},
    ...
  ],
  "troubleshooting": [
    {{
      "problem": "ImportError: No module named 'requests'",
      "symptoms": ["Import fails", "Module not found error"],
      "diagnostic_steps": ["Check pip list", "Verify virtual env"],
      "solution": "Run: pip install requests"
    }},
    ...
  ],
  "prerequisites_detailed": [
    {{"name": "requests", "why": "HTTP client for making web requests", "setup": "pip install requests"}},
    ...
  ],
  "next_steps": [
    "How to handle async workflows",
    "How to add error handling",
    ...
  ],
  "use_cases": [
    "Use when you need to automate web scraping tasks",
    "Ideal for building documentation archives",
    ...
  ]
}}

IMPORTANT: Return ONLY valid JSON, no markdown code blocks or extra text.
"""
        return prompt

    def _create_step_description_prompt(self, steps: list[dict]) -> str:
        """Create prompt for step descriptions only."""
        steps_text = self._format_steps_for_prompt(steps)
        return f"""Generate natural language explanations for these code steps:

{steps_text}

Return JSON:
{{
  "step_descriptions": [
    {{"step_index": 0, "explanation": "...", "variations": [""]}},
    ...
  ]
}}

IMPORTANT: Return ONLY valid JSON.
"""

    def _create_troubleshooting_prompt(self, guide_data: dict) -> str:
        """Create prompt for troubleshooting items."""
        title = guide_data.get("title", "Unknown")
        language = guide_data.get("language", "python")
        steps = guide_data.get("steps", [])
        steps_text = self._format_steps_for_prompt(steps)

        return f"""Generate troubleshooting guidance for this {language} workflow:

Title: {title}
Steps:
{steps_text}

Return JSON with 3-5 common errors:
{{
  "troubleshooting": [
    {{
      "problem": "...",
      "symptoms": ["...", "..."],
      "diagnostic_steps": ["...", "..."],
      "solution": "..."
    }},
    ...
  ]
}}

IMPORTANT: Return ONLY valid JSON.
"""

    def _create_prerequisites_prompt(self, prereqs: list[str]) -> str:
        """Create prompt for prerequisites enhancement."""
        prereqs_text = ", ".join(prereqs)
        return f"""Explain why these prerequisites are needed and how to install them:

Prerequisites: {prereqs_text}

Return JSON:
{{
  "prerequisites_detailed": [
    {{"name": "...", "why": "...", "setup": "..."}},
    ...
  ]
}}

IMPORTANT: Return ONLY valid JSON.
"""

    def _create_next_steps_prompt(self, guide_data: dict) -> str:
        """Create prompt for next steps suggestions."""
        title = guide_data.get("title", "Unknown")
        return f"""Suggest 3-5 related guides and learning paths after completing: {title}

Return JSON:
{{
  "next_steps": [
    "How to ...",
    "How to ...",
    ...
  ]
}}

IMPORTANT: Return ONLY valid JSON.
"""

    def _create_use_cases_prompt(self, guide_data: dict) -> str:
        """Create prompt for use case examples."""
        title = guide_data.get("title", "Unknown")
        description = guide_data.get("description", "")

        return f"""Generate 2-3 real-world use cases for this guide:

Title: {title}
Description: {description}

Return JSON:
{{
  "use_cases": [
    "Use when you need to ...",
    "Ideal for ...",
    ...
  ]
}}

IMPORTANT: Return ONLY valid JSON.
"""

    def _format_steps_for_prompt(self, steps: list[dict]) -> str:
        """Format steps for inclusion in prompts."""
        if not steps:
            return "No steps provided"

        formatted = []
        for i, step in enumerate(steps):
            desc = step.get("description", "")
            code = step.get("code", "")
            if code:
                formatted.append(f"Step {i + 1}: {desc}\n```\n{code}\n```")
            else:
                formatted.append(f"Step {i + 1}: {desc}")

        return "\n\n".join(formatted)

    def _parse_enhancement_response(self, response: str, guide_data: dict) -> dict:
        """
        Parse AI enhancement response.

        Args:
            response: AI response text (should be JSON)
            guide_data: Original guide data

        Returns:
            Enhanced guide data
        """
        try:
            # Try to extract JSON from response (in case there's extra text)
            json_start = response.find("{")
            json_end = response.rfind("}") + 1
            if json_start >= 0 and json_end > json_start:
                json_text = response[json_start:json_end]
                data = json.loads(json_text)
            else:
                data = json.loads(response)

            # Merge enhancements into guide_data
            enhanced = guide_data.copy()

            # Step descriptions
            if "step_descriptions" in data:
                enhanced["step_enhancements"] = [
                    StepEnhancement(
                        step_index=item.get("step_index", i),
                        explanation=item.get("explanation", ""),
                        variations=item.get("variations", []),
                    )
                    for i, item in enumerate(data["step_descriptions"])
                ]

            # Troubleshooting
            if "troubleshooting" in data:
                enhanced["troubleshooting_detailed"] = [
                    TroubleshootingItem(
                        problem=item.get("problem", ""),
                        symptoms=item.get("symptoms", []),
                        diagnostic_steps=item.get("diagnostic_steps", []),
                        solution=item.get("solution", ""),
                    )
                    for item in data["troubleshooting"]
                ]

            # Prerequisites
            if "prerequisites_detailed" in data:
                enhanced["prerequisites_detailed"] = [
                    PrerequisiteItem(
                        name=item.get("name", ""),
                        why=item.get("why", ""),
                        setup=item.get("setup", ""),
                    )
                    for item in data["prerequisites_detailed"]
                ]

            # Next steps
            if "next_steps" in data:
                enhanced["next_steps_detailed"] = data["next_steps"]

            # Use cases
            if "use_cases" in data:
                enhanced["use_cases"] = data["use_cases"]

            logger.info("✅ Successfully enhanced guide with all 5 improvements")
            return enhanced

        except (json.JSONDecodeError, KeyError) as e:
            logger.warning(f"⚠️  Failed to parse AI response: {e}")
            logger.debug(f"Response was: {response[:500]}...")
            return guide_data
