backend/app/api/routes.py

"""API routes for the delivery search application."""
from fastapi import APIRouter, HTTPException
from typing import List

from ..models.requests import (
    GridConfig,
    SearchRequest,
    PathRequest,
    CompareRequest,
    Position,
    GenerateResponse,
    SearchResponse,
    PlanResponse,
    ComparisonResult,
    CompareResponse,
    AlgorithmInfo,
    AlgorithmsResponse,
    GridData,
    StoreData,
    DestinationData,
    TunnelData,
    SegmentData,
)
from ..services import gen_grid, parse_full_state, measure_performance
from ..core import DeliverySearch, DeliveryPlanner


router = APIRouter()


# Algorithm metadata
ALGORITHMS = [
    AlgorithmInfo(
        code="BF",
        name="Breadth-First Search",
        description="Explores all nodes at current depth before moving deeper. Finds shortest path in terms of steps."
    ),
    AlgorithmInfo(
        code="DF",
        name="Depth-First Search",
        description="Explores as far as possible along each branch. Memory efficient but may not find optimal path."
    ),
    AlgorithmInfo(
        code="ID",
        name="Iterative Deepening",
        description="Combines BFS completeness with DFS space efficiency. Good for unknown depth goals."
    ),
    AlgorithmInfo(
        code="UC",
        name="Uniform Cost Search",
        description="Expands lowest-cost node first. Always finds the optimal (minimum cost) solution."
    ),
    AlgorithmInfo(
        code="GR1",
        name="Greedy (Manhattan)",
        description="Uses Manhattan distance heuristic. Fast but may not find optimal path."
    ),
    AlgorithmInfo(
        code="GR2",
        name="Greedy (Euclidean)",
        description="Uses Euclidean distance heuristic. Fast but may not find optimal path."
    ),
    AlgorithmInfo(
        code="AS1",
        name="A* (Manhattan)",
        description="A* with Manhattan distance. Optimal and complete with admissible heuristic."
    ),
    AlgorithmInfo(
        code="AS2",
        name="A* (Tunnel-Aware)",
        description="A* considering tunnel shortcuts. More informed for grids with tunnels."
    ),
]


@router.get("/api/health")
async def health_check():
    """Health check endpoint."""
    return {"status": "ok"}


@router.get("/api/algorithms", response_model=AlgorithmsResponse)
async def list_algorithms():
    """List available search algorithms."""
    return AlgorithmsResponse(algorithms=ALGORITHMS)


@router.post("/api/grid/generate", response_model=GenerateResponse)
async def generate_grid(config: GridConfig):
    """Generate a random grid configuration."""
    try:
        initial_state, traffic, state = gen_grid(
            width=config.width,
            height=config.height,
            num_stores=config.num_stores,
            num_destinations=config.num_destinations,
            num_tunnels=config.num_tunnels,
            obstacle_density=config.obstacle_density
        )

        # Convert to GridData for frontend
        parsed = GridData(
            width=state.grid.width,
            height=state.grid.height,
            stores=[
                StoreData(id=s.id, position=Position(x=s.position[0], y=s.position[1]))
                for s in state.stores
            ],
            destinations=[
                DestinationData(id=d.id, position=Position(x=d.position[0], y=d.position[1]))
                for d in state.destinations
            ],
            tunnels=[
                TunnelData(
                    entrance1=Position(x=t.entrance1[0], y=t.entrance1[1]),
                    entrance2=Position(x=t.entrance2[0], y=t.entrance2[1]),
                    cost=t.cost
                )
                for t in state.tunnels
            ],
            segments=[
                SegmentData(
                    src=Position(x=seg.src[0], y=seg.src[1]),
                    dst=Position(x=seg.dst[0], y=seg.dst[1]),
                    traffic=seg.traffic
                )
                for seg in state.grid.segments.values()
            ]
        )

        return GenerateResponse(
            initial_state=initial_state,
            traffic=traffic,
            parsed=parsed
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@router.post("/api/search/path", response_model=SearchResponse)
async def find_path(request: PathRequest):
    """Find path from start to goal using specified strategy."""
    try:
        from ..models.grid import Grid
        from ..models.entities import Tunnel

        # Build grid from request
        grid = Grid(width=request.grid_width, height=request.grid_height)
        for seg in request.segments:
            grid.add_segment(
                (seg.src.x, seg.src.y),
                (seg.dst.x, seg.dst.y),
                seg.traffic
            )

        # Build tunnels
        tunnels = [
            Tunnel(
                entrance1=(t.entrance1.x, t.entrance1.y),
                entrance2=(t.entrance2.x, t.entrance2.y)
            )
            for t in request.tunnels
        ]

        # Run search with metrics
        with measure_performance() as metrics:
            result, steps = DeliverySearch.path(
                grid,
                (request.start.x, request.start.y),
                (request.goal.x, request.goal.y),
                tunnels,
                request.strategy.value,
                visualize=True
            )
            metrics.sample()

        return SearchResponse(
            plan=result.plan,
            cost=result.cost,
            nodes_expanded=result.nodes_expanded,
            runtime_ms=metrics.runtime_ms,
            memory_mb=max(0, metrics.memory_mb),
            cpu_percent=metrics.cpu_percent,
            path=[Position(x=p[0], y=p[1]) for p in result.path],
            steps=[s.to_dict() for s in steps] if steps else None
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@router.post("/api/search/plan", response_model=PlanResponse)
async def create_plan(request: SearchRequest):
    """Create full delivery plan for all trucks and destinations."""
    try:
        # Parse state
        state = parse_full_state(request.initial_state, request.traffic)

        # Run planner with metrics
        with measure_performance() as metrics:
            plan_result, viz_data = DeliveryPlanner.plan_from_state(
                state.grid,
                state.stores,
                state.destinations,
                state.tunnels,
                request.strategy.value,
                request.visualize
            )
            metrics.sample()

        return PlanResponse(
            output=plan_result.to_string(),
            assignments=[a.to_dict() for a in plan_result.assignments],
            total_cost=plan_result.total_cost,
            total_nodes_expanded=plan_result.total_nodes_expanded,
            runtime_ms=metrics.runtime_ms,
            memory_mb=max(0, metrics.memory_mb),
            cpu_percent=metrics.cpu_percent
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@router.post("/api/search/compare", response_model=CompareResponse)
async def compare_algorithms(request: CompareRequest):
    """Run all algorithms on same problem and return comparison."""
    try:
        state = parse_full_state(request.initial_state, request.traffic)

        results: List[ComparisonResult] = []
        optimal_cost = float('inf')

        # Run each algorithm
        for algo_info in ALGORITHMS:
            with measure_performance() as metrics:
                plan_result, _ = DeliveryPlanner.plan_from_state(
                    state.grid,
                    state.stores,
                    state.destinations,
                    state.tunnels,
                    algo_info.code,
                    visualize=False
                )
                metrics.sample()

            # Track optimal cost (from UCS or A*)
            if algo_info.code in ["UC", "AS1", "AS2"]:
                optimal_cost = min(optimal_cost, plan_result.total_cost)

            results.append(ComparisonResult(
                algorithm=algo_info.code,
                name=algo_info.name,
                plan=plan_result.to_string(),
                cost=plan_result.total_cost,
                nodes_expanded=plan_result.total_nodes_expanded,
                runtime_ms=metrics.runtime_ms,
                memory_mb=max(0, metrics.memory_mb),
                cpu_percent=metrics.cpu_percent,
                is_optimal=False  # Will be set below
            ))

        # Mark optimal solutions
        for result in results:
            result.is_optimal = (result.cost == optimal_cost)

        return CompareResponse(
            comparisons=results,
            optimal_cost=optimal_cost
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))