Atomizer/optimization_engine/interview/interview_presenter.py

"""
Interview Presenter

Abstract presentation layer for different UI modes.
Handles:
- Formatting questions for display
- Parsing user responses
- Showing summaries and warnings
"""

from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Any, Optional, List, Dict
import re

from .question_engine import Question, QuestionOption


@dataclass
class PresentedQuestion:
    """A question formatted for presentation."""
    question_id: str
    formatted_text: str
    question_number: int
    total_questions: int
    category_name: str


class InterviewPresenter(ABC):
    """
    Abstract base for interview presentation.

    Different presenters handle UI-specific rendering:
    - ClaudePresenter: Markdown for Claude conversation
    - DashboardPresenter: WebSocket events for React UI (future)
    - CLIPresenter: Interactive terminal prompts (future)
    """

    @abstractmethod
    def present_question(
        self,
        question: Question,
        question_number: int,
        total_questions: int,
        category_name: str,
        dynamic_content: Optional[str] = None
    ) -> str:
        """
        Format a question for display.

        Args:
            question: Question to present
            question_number: Current question number
            total_questions: Estimated total questions
            category_name: Name of the question category
            dynamic_content: Dynamic content to inject (e.g., extractor summary)

        Returns:
            Formatted question string
        """
        pass

    @abstractmethod
    def parse_response(self, response: str, question: Question) -> Any:
        """
        Parse user's response into structured value.

        Args:
            response: Raw user response
            question: Question being answered

        Returns:
            Parsed answer value
        """
        pass

    @abstractmethod
    def show_summary(self, blueprint: "StudyBlueprint") -> str:
        """
        Format interview summary/blueprint for display.

        Args:
            blueprint: Generated study blueprint

        Returns:
            Formatted summary string
        """
        pass

    @abstractmethod
    def show_warning(self, warning: str, severity: str = "warning") -> str:
        """
        Format a warning message for display.

        Args:
            warning: Warning message
            severity: "error", "warning", or "info"

        Returns:
            Formatted warning string
        """
        pass

    @abstractmethod
    def show_progress(self, current: int, total: int, phase: str) -> str:
        """
        Format progress indicator.

        Args:
            current: Current question number
            total: Estimated total questions
            phase: Current phase name

        Returns:
            Formatted progress string
        """
        pass


class ClaudePresenter(InterviewPresenter):
    """
    Presenter for Claude conversation mode (VS Code, Web).

    Formats questions and responses as markdown for natural
    conversation flow with Claude.
    """

    def present_question(
        self,
        question: Question,
        question_number: int,
        total_questions: int,
        category_name: str,
        dynamic_content: Optional[str] = None
    ) -> str:
        """Format question as markdown for Claude to present."""
        lines = []

        # Header with progress
        lines.append(f"### Question {question_number} of ~{total_questions}: {category_name}")
        lines.append("")

        # Main question text
        lines.append(question.text)
        lines.append("")

        # Dynamic content if provided
        if dynamic_content:
            lines.append(dynamic_content)
            lines.append("")

        # Options for choice questions
        if question.options and question.question_type in ["choice", "multi_choice"]:
            for i, opt in enumerate(question.options, 1):
                desc = f" - {opt.description}" if opt.description else ""
                lines.append(f"{i}. **{opt.label}**{desc}")
            lines.append("")

        # Help text
        if question.help_text:
            lines.append(f"> {question.help_text}")
            lines.append("")

        # Engineering guidance
        if question.engineering_guidance:
            lines.append(f"> **Tip**: {question.engineering_guidance}")
            lines.append("")

        # Default value hint
        if question.default is not None and question.default != []:
            if isinstance(question.default, list):
                default_str = ", ".join(str(d) for d in question.default)
            else:
                default_str = str(question.default)
            lines.append(f"*Default: {default_str}*")
            lines.append("")

        # Input prompt based on type
        if question.question_type == "text":
            lines.append("Please describe:")
        elif question.question_type == "numeric":
            units = question.validation.units if question.validation else ""
            lines.append(f"Enter value{f' ({units})' if units else ''}:")
        elif question.question_type == "choice":
            lines.append("Type your choice (number or description):")
        elif question.question_type == "multi_choice":
            lines.append("Type your choices (numbers or descriptions, comma-separated):")
        elif question.question_type == "confirm":
            lines.append("Type **yes** or **no**:")
        elif question.question_type == "parameter_select":
            lines.append("Type parameter names (comma-separated) or select by number:")
        elif question.question_type == "bounds":
            lines.append("Enter bounds (e.g., '2 to 10' or 'min 2, max 10'):")

        return "\n".join(lines)

    def parse_response(self, response: str, question: Question) -> Any:
        """Parse natural language response into structured answer."""
        response = response.strip()

        if question.question_type == "text":
            return response

        elif question.question_type == "numeric":
            return self._parse_numeric(response, question)

        elif question.question_type == "confirm":
            return self._parse_confirm(response)

        elif question.question_type == "choice":
            return self._parse_choice(response, question)

        elif question.question_type == "multi_choice":
            return self._parse_multi_choice(response, question)

        elif question.question_type == "parameter_select":
            return self._parse_parameter_select(response, question)

        elif question.question_type == "bounds":
            return self._parse_bounds(response)

        return response

    def _parse_numeric(self, response: str, question: Question) -> Optional[float]:
        """Parse numeric response with unit handling."""
        # Remove common unit suffixes
        cleaned = re.sub(r'\s*(mm|cm|m|kg|g|MPa|Pa|GPa|Hz|kHz|MHz|°|deg)s?\s*$', '', response, flags=re.I)

        # Extract number
        match = re.search(r'[-+]?\d*\.?\d+', cleaned)
        if match:
            return float(match.group())
        return None

    def _parse_confirm(self, response: str) -> Optional[bool]:
        """Parse yes/no confirmation."""
        lower = response.lower().strip()

        # Positive responses
        if lower in ["yes", "y", "true", "1", "ok", "sure", "yep", "yeah", "correct", "confirmed", "confirm", "affirmative"]:
            return True

        # Negative responses
        if lower in ["no", "n", "false", "0", "nope", "nah", "cancel", "incorrect", "negative"]:
            return False

        # Try to detect intent from natural language
        if "yes" in lower or "ok" in lower or "correct" in lower:
            return True
        if "no" in lower or "don't" in lower or "not" in lower:
            return False

        return None

    def _parse_choice(self, response: str, question: Question) -> Any:
        """Parse single choice response."""
        if not question.options:
            return response

        # Try by number
        if response.isdigit():
            idx = int(response) - 1
            if 0 <= idx < len(question.options):
                return question.options[idx].value

        # Try by value (exact match)
        for opt in question.options:
            if response.lower() == str(opt.value).lower():
                return opt.value

        # Try by label (exact match)
        for opt in question.options:
            if response.lower() == opt.label.lower():
                return opt.value

        # Try fuzzy match on label
        for opt in question.options:
            if response.lower() in opt.label.lower():
                return opt.value

        # Return as-is for custom values
        return response

    def _parse_multi_choice(self, response: str, question: Question) -> List[Any]:
        """Parse multiple choice response."""
        # Split by comma, 'and', or numbers
        parts = re.split(r'[,&]|\band\b|\s+', response)
        parts = [p.strip() for p in parts if p.strip()]

        values = []
        for part in parts:
            if not part:
                continue

            # Try by number
            if part.isdigit() and question.options:
                idx = int(part) - 1
                if 0 <= idx < len(question.options):
                    value = question.options[idx].value
                    if value not in values:
                        values.append(value)
                    continue

            # Try by value/label
            if question.options:
                found = False
                for opt in question.options:
                    if part.lower() == str(opt.value).lower() or part.lower() == opt.label.lower():
                        if opt.value not in values:
                            values.append(opt.value)
                        found = True
                        break
                    if part.lower() in opt.label.lower():
                        if opt.value not in values:
                            values.append(opt.value)
                        found = True
                        break
                if found:
                    continue

            # Add as custom value
            if part not in values:
                values.append(part)

        return values

    def _parse_parameter_select(self, response: str, question: Question) -> List[str]:
        """Parse parameter selection response."""
        # Split by comma, 'and', or numbers
        parts = re.split(r'[,&]|\band\b', response)
        parameters = []

        for part in parts:
            part = part.strip()
            if not part:
                continue

            # Try by number if we have options
            if part.isdigit() and question.options:
                idx = int(part) - 1
                if 0 <= idx < len(question.options):
                    parameters.append(question.options[idx].value)
                    continue

            # Add as parameter name
            parameters.append(part)

        return parameters

    def _parse_bounds(self, response: str) -> Optional[Dict[str, float]]:
        """Parse bounds specification."""
        bounds = {}

        # Try "min to max" format
        match = re.search(r'(\d+\.?\d*)\s*(?:to|-)\s*(\d+\.?\d*)', response)
        if match:
            bounds["min"] = float(match.group(1))
            bounds["max"] = float(match.group(2))
            return bounds

        # Try "min: X, max: Y" format
        min_match = re.search(r'min[:\s]+(\d+\.?\d*)', response, re.I)
        max_match = re.search(r'max[:\s]+(\d+\.?\d*)', response, re.I)

        if min_match:
            bounds["min"] = float(min_match.group(1))
        if max_match:
            bounds["max"] = float(max_match.group(1))

        return bounds if bounds else None

    def show_summary(self, blueprint: "StudyBlueprint") -> str:
        """Format interview summary/blueprint for display."""
        lines = []

        lines.append(f"## Study Blueprint: {blueprint.study_name}")
        lines.append("")

        # Description
        if blueprint.study_description:
            lines.append(f"**Description**: {blueprint.study_description}")
            lines.append("")

        # Design Variables
        lines.append(f"### Design Variables ({len(blueprint.design_variables)})")
        lines.append("")
        lines.append("| Parameter | Current | Min | Max | Units |")
        lines.append("|-----------|---------|-----|-----|-------|")
        for dv in blueprint.design_variables:
            lines.append(f"| {dv.parameter} | {dv.current_value} | {dv.min_value} | {dv.max_value} | {dv.units or '-'} |")
        lines.append("")

        # Objectives
        lines.append(f"### Objectives ({len(blueprint.objectives)})")
        lines.append("")
        lines.append("| Goal | Extractor | Parameters |")
        lines.append("|------|-----------|------------|")
        for obj in blueprint.objectives:
            params = ", ".join(f"{k}={v}" for k, v in (obj.extractor_params or {}).items()) or "-"
            lines.append(f"| {obj.goal} | {obj.extractor} | {params} |")
        lines.append("")

        # Constraints
        if blueprint.constraints:
            lines.append(f"### Constraints ({len(blueprint.constraints)})")
            lines.append("")
            lines.append("| Type | Threshold | Extractor |")
            lines.append("|------|-----------|-----------|")
            for con in blueprint.constraints:
                op = "<=" if con.constraint_type == "max" else ">="
                lines.append(f"| {con.name} | {op} {con.threshold} | {con.extractor} |")
            lines.append("")

        # Settings
        lines.append("### Settings")
        lines.append("")
        lines.append(f"- **Protocol**: {blueprint.protocol}")
        lines.append(f"- **Trials**: {blueprint.n_trials}")
        lines.append(f"- **Sampler**: {blueprint.sampler}")
        lines.append("")

        # Warnings
        if blueprint.warnings_acknowledged:
            lines.append("### Acknowledged Warnings")
            lines.append("")
            for warning in blueprint.warnings_acknowledged:
                lines.append(f"- {warning}")
            lines.append("")

        lines.append("---")
        lines.append("")
        lines.append("Does this look correct? Reply **yes** to generate the study, or describe what to change.")

        return "\n".join(lines)

    def show_warning(self, warning: str, severity: str = "warning") -> str:
        """Format a warning message for display."""
        icons = {
            "error": "X",
            "warning": "!",
            "info": "i"
        }
        icon = icons.get(severity, "!")

        if severity == "error":
            return f"\n**[{icon}] ERROR**: {warning}\n"
        elif severity == "warning":
            return f"\n**[{icon}] Warning**: {warning}\n"
        else:
            return f"\n*[{icon}] Note*: {warning}\n"

    def show_progress(self, current: int, total: int, phase: str) -> str:
        """Format progress indicator."""
        percentage = int((current / total) * 100) if total > 0 else 0
        bar_length = 20
        filled = int(bar_length * current / total) if total > 0 else 0
        bar = "=" * filled + "-" * (bar_length - filled)

        return f"**Progress**: [{bar}] {percentage}% - {phase}"


class DashboardPresenter(InterviewPresenter):
    """
    Presenter for dashboard UI mode (future).

    Emits WebSocket events for React UI to render.
    """

    def present_question(
        self,
        question: Question,
        question_number: int,
        total_questions: int,
        category_name: str,
        dynamic_content: Optional[str] = None
    ) -> str:
        """Emit WebSocket event for dashboard to render."""
        # This would emit an event to the dashboard
        # For now, return JSON representation
        import json
        return json.dumps({
            "type": "question",
            "data": {
                "question_id": question.id,
                "question_number": question_number,
                "total_questions": total_questions,
                "category": category_name,
                "text": question.text,
                "question_type": question.question_type,
                "options": [{"value": o.value, "label": o.label} for o in (question.options or [])],
                "help_text": question.help_text,
                "default": question.default,
                "dynamic_content": dynamic_content,
            }
        })

    def parse_response(self, response: str, question: Question) -> Any:
        """Parse JSON response from dashboard."""
        import json
        try:
            data = json.loads(response)
            return data.get("value", response)
        except json.JSONDecodeError:
            # Fall back to Claude parser
            claude = ClaudePresenter()
            return claude.parse_response(response, question)

    def show_summary(self, blueprint: "StudyBlueprint") -> str:
        """Emit summary event for dashboard."""
        import json
        return json.dumps({
            "type": "summary",
            "data": blueprint.to_dict() if hasattr(blueprint, 'to_dict') else str(blueprint)
        })

    def show_warning(self, warning: str, severity: str = "warning") -> str:
        """Emit warning event for dashboard."""
        import json
        return json.dumps({
            "type": "warning",
            "data": {"message": warning, "severity": severity}
        })

    def show_progress(self, current: int, total: int, phase: str) -> str:
        """Emit progress event for dashboard."""
        import json
        return json.dumps({
            "type": "progress",
            "data": {"current": current, "total": total, "phase": phase}
        })


class CLIPresenter(InterviewPresenter):
    """
    Presenter for CLI wizard mode (future).

    Interactive terminal prompts using Rich/Questionary.
    """

    def present_question(
        self,
        question: Question,
        question_number: int,
        total_questions: int,
        category_name: str,
        dynamic_content: Optional[str] = None
    ) -> str:
        """Format for CLI display."""
        # Simple text format for CLI
        lines = []
        lines.append(f"\n[{question_number}/{total_questions}] {category_name}")
        lines.append("-" * 50)
        lines.append(question.text)

        if question.options:
            for i, opt in enumerate(question.options, 1):
                lines.append(f"  {i}. {opt.label}")

        if question.help_text:
            lines.append(f"\nHint: {question.help_text}")

        lines.append("")
        return "\n".join(lines)

    def parse_response(self, response: str, question: Question) -> Any:
        """Parse CLI response (delegate to Claude parser)."""
        claude = ClaudePresenter()
        return claude.parse_response(response, question)

    def show_summary(self, blueprint: "StudyBlueprint") -> str:
        """Format summary for CLI."""
        claude = ClaudePresenter()
        return claude.show_summary(blueprint)

    def show_warning(self, warning: str, severity: str = "warning") -> str:
        """Format warning for CLI."""
        icons = {"error": "[ERROR]", "warning": "[WARN]", "info": "[INFO]"}
        return f"\n{icons.get(severity, '[WARN]')} {warning}\n"

    def show_progress(self, current: int, total: int, phase: str) -> str:
        """Format progress for CLI."""
        return f"Progress: {current}/{total} ({phase})"


# Import for type hints
from typing import TYPE_CHECKING
if TYPE_CHECKING:
    from .study_blueprint import StudyBlueprint