# -*- coding: utf-8 -*-
"""
arifOS AGI Server - The Mind (Delta)

Constitutional Alignment: F2 (Truth), F4 (Clarity), F7 (Humility), F10 (Ontology), F13 (Curiosity)
Stages: 111 SENSE, 222 THINK, 333 ATLAS
Version: v49.0.0
Authority: Delta (Architect)

Architecture:
- Hosts 13 MCP tools: brave_search, time, sequential_thinking, python, arxiv, wikipedia,
  http_client, memory, paradox_engine, perplexity_ask, executor, reddit, youtube_transcript
- Enforces F2/F4/F7/F10/F13 floors on all operations
- Routes to VAULT and APEX servers

NOTE: This is an ARCHITECTURAL BLUEPRINT. MCP tools are declared but not yet wired.
See IMPLEMENTATION_GAPS.md for production readiness status.
"""

import asyncio
import logging
from typing import Any, Dict, List, Optional

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field

# Constitutional imports (Phase 8.1: Canonical validators)
from arifos.core.floor_validators import (
    validate_f2_truth,
    validate_f4_clarity,
    validate_f7_humility,
    validate_f10_ontology,
    validate_f13_curiosity,
)

logger = logging.getLogger(__name__)

# =============================================================================
# REQUEST/RESPONSE MODELS
# =============================================================================

class AGIRequest(BaseModel):
    """Request to AGI server (from VAULT 000 or previous stage)."""
    session_id: str = Field(..., description="Session identifier")
    query: str = Field(..., description="User query")
    stage: str = Field(..., description="Target stage (111/222/333)")
    context: Dict[str, Any] = Field(default_factory=dict, description="Session context")
    floor_scores: Dict[str, Any] = Field(default_factory=dict, description="Previous floor scores")


class AGIResponse(BaseModel):
    """Response from AGI server."""
    verdict: str = Field(..., description="SEAL/PARTIAL/VOID/SABAR")
    stage: str = Field(..., description="Completed stage")
    floor_scores: Dict[str, Any] = Field(default_factory=dict, description="Updated floor scores")
    output: Dict[str, Any] = Field(default_factory=dict, description="Stage output")
    next_stage: str = Field(..., description="Next stage routing decision")
    latency_ms: float = Field(..., description="Processing time")


# =============================================================================
# AGI SERVER CLASS
# =============================================================================

class AGIServer:
    """
    AGI Server - The Mind (Δ)

    Hosts reasoning stages 111/222/333 with constitutional floor enforcement.
    """

    def __init__(self, vault_url: str = "http://vault_server:9000"):
        self.vault_url = vault_url
        self.mcp_tools = [
            "brave_search", "time", "sequential_thinking", "python",
            "arxiv", "wikipedia", "http_client", "memory",
            "paradox_engine", "perplexity_ask", "executor",
            "reddit", "youtube_transcript"
        ]
        self.floors = ["F2", "F4", "F7", "F10", "F13"]
        self.stages = ["111_SENSE", "222_THINK", "333_ATLAS"]

        logger.info(f"AGI Server initialized with {len(self.mcp_tools)} MCP tools")
        logger.info(f"Enforcing floors: {', '.join(self.floors)}")

    async def process_111_sense(self, request: AGIRequest) -> AGIResponse:
        """
        Stage 111 SENSE - Input reception and context gathering.

        Floors: F10 (Ontology), F11 (CommandAuth), F12 (InjectionDefense), F13 (Curiosity)
        """
        import time
        start_time = time.time()

        # F10: Ontology check (AI stays as tool, no soul claims)
        ontology_result = validate_f10_ontology(request.query)

        # F13: Curiosity check (exploring alternatives)
        curiosity_result = validate_f13_curiosity(request.query, request.context)

        # Aggregate floor scores
        floor_scores = {
            "F10_Ontology": ontology_result,
            "F13_Curiosity": curiosity_result,
        }

        # Determine verdict
        if not ontology_result["pass"]:
            verdict = "VOID"
            next_stage = "999_VAULT"  # Log violation and exit
        elif curiosity_result["score"] < 0.85:
            verdict = "PARTIAL"
            next_stage = "222_THINK"  # Continue with warning
        else:
            verdict = "SEAL"
            next_stage = "222_THINK"

        latency_ms = (time.time() - start_time) * 1000

        return AGIResponse(
            verdict=verdict,
            stage="111_SENSE",
            floor_scores=floor_scores,
            output={
                "parsed_intent": "query",  # TODO: Implement intent parser
                "enriched_query": request.query,
                "curiosity_signals": curiosity_result.get("signals", []),
            },
            next_stage=next_stage,
            latency_ms=latency_ms,
        )

    async def process_222_think(self, request: AGIRequest) -> AGIResponse:
        """
        Stage 222 THINK - Reasoning and fact verification.

        Floors: F2 (Truth), F4 (Clarity), F10 (Ontology)
        """
        import time
        start_time = time.time()

        # F2: Truth verification
        truth_result = validate_f2_truth(request.query, request.context)

        # F4: Clarity check (entropy reduction)
        clarity_result = validate_f4_clarity(request.query, request.context)

        # F10: Ontology re-check
        ontology_result = validate_f10_ontology(request.query)

        floor_scores = {
            "F2_Truth": truth_result,
            "F4_Clarity": clarity_result,
            "F10_Ontology": ontology_result,
        }

        # Determine verdict
        if not truth_result["pass"] or not ontology_result["pass"]:
            verdict = "VOID"
            next_stage = "999_VAULT"
        elif clarity_result["delta_s"] > 0:
            verdict = "PARTIAL"
            next_stage = "333_ATLAS"
        else:
            verdict = "SEAL"
            next_stage = "333_ATLAS"

        latency_ms = (time.time() - start_time) * 1000

        return AGIResponse(
            verdict=verdict,
            stage="222_THINK",
            floor_scores=floor_scores,
            output={
                "reasoning": "Placeholder reasoning output",  # TODO: Implement reasoner
                "truth_score": truth_result.get("score", 0.0),
                "entropy_delta": clarity_result.get("delta_s", 0.0),
            },
            next_stage=next_stage,
            latency_ms=latency_ms,
        )

    async def process_333_atlas(self, request: AGIRequest) -> AGIResponse:
        """
        Stage 333 ATLAS - Meta-cognition and paradox detection.

        Floors: F7 (Humility), F4 (Clarity)
        """
        import time
        start_time = time.time()

        # F7: Humility audit (Ω₀ ∈ [0.03, 0.05])
        humility_result = validate_f7_humility(request.query, request.context)

        # F4: Re-check clarity
        clarity_result = validate_f4_clarity(request.query, request.context)

        floor_scores = {
            "F7_Humility": humility_result,
            "F4_Clarity": clarity_result,
        }

        # Determine verdict and routing
        if not humility_result["pass"]:
            verdict = "VOID"
            next_stage = "999_VAULT"
        elif clarity_result["delta_s"] > 0:
            verdict = "PARTIAL"
            next_stage = "444_EVIDENCE"  # Route to APEX
        else:
            verdict = "SEAL"
            next_stage = "444_EVIDENCE"  # Route to APEX

        latency_ms = (time.time() - start_time) * 1000

        return AGIResponse(
            verdict=verdict,
            stage="333_ATLAS",
            floor_scores=floor_scores,
            output={
                "confidence_audit": "Placeholder audit",  # TODO: Implement paradox engine
                "omega_zero": humility_result.get("omega_zero", 0.04),
                "paradoxes": [],
            },
            next_stage=next_stage,
            latency_ms=latency_ms,
        )


# =============================================================================
# FASTAPI APPLICATION
# =============================================================================

app = FastAPI(title="arifOS AGI Server", version="v49.0.0")
agi_server = AGIServer()


@app.get("/health")
async def health_check():
    """Health check endpoint."""
    return {
        "status": "healthy",
        "server": "AGI",
        "version": "v49.0.0",
        "floors": agi_server.floors,
        "stages": agi_server.stages,
        "mcp_tools": len(agi_server.mcp_tools),
    }


@app.post("/process", response_model=AGIResponse)
async def process_stage(request: AGIRequest):
    """Process AGI stage request (111/222/333)."""
    try:
        if request.stage == "111_SENSE":
            return await agi_server.process_111_sense(request)
        elif request.stage == "222_THINK":
            return await agi_server.process_222_think(request)
        elif request.stage == "333_ATLAS":
            return await agi_server.process_333_atlas(request)
        else:
            raise HTTPException(status_code=400, detail=f"Unknown stage: {request.stage}")
    except Exception as e:
        logger.error(f"Error processing {request.stage}: {e}")
        raise HTTPException(status_code=500, detail=str(e))


# =============================================================================
# MCP TOOL PROXY (Phase 8.2 - Generic handler for all AGI MCP tools)
# =============================================================================

@app.post("/mcp/{tool_name}")
async def execute_mcp_tool(tool_name: str, request: Dict[str, Any]):
    """
    Generic MCP tool executor.

    Routes to existing mcp_*.py tools in arifos_core.mcp.tools/
    Validates with constitutional floors per tool specification.

    Phase 8.2: Proof-of-concept for 11 AGI tools
    - brave_search, wikipedia, arxiv (web research)
    - time, python, sequential_thinking (execution)
    - filesystem, http_client (I/O)
    - agi_think, agi_reflect, agi_atlas (reasoning)
    """
    import importlib
    import time
    start_time = time.time()

    try:
        # Dynamic import of MCP tool
        tool_module_name = f"arifos_core.mcp.tools.mcp_{tool_name}"
        try:
            tool_module = importlib.import_module(tool_module_name)
        except ModuleNotFoundError:
            # Try alternative naming (e.g., brave_search → mcp_111_sense for 111 SENSE)
            raise HTTPException(
                status_code=404,
                detail=f"MCP tool '{tool_name}' not found. Available: {', '.join(agi_server.mcp_tools)}"
            )

        # Execute tool (assumes tool has main execution function)
        if hasattr(tool_module, 'execute'):
            result = await tool_module.execute(request)
        elif hasattr(tool_module, tool_name):
            func = getattr(tool_module, tool_name)
            result = await func(request) if asyncio.iscoroutinefunction(func) else func(request)
        else:
            raise HTTPException(
                status_code=500,
                detail=f"Tool module '{tool_module_name}' has no execute() or {tool_name}() function"
            )

        latency_ms = (time.time() - start_time) * 1000

        return {
            "mcp_tool": tool_name,
            "result": result,
            "latency_ms": latency_ms,
            "server": "AGI",
            "floors": agi_server.floors,
        }

    except Exception as e:
        logger.error(f"MCP tool '{tool_name}' error: {e}")
        raise HTTPException(status_code=500, detail=str(e))


if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=9001)
