from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
from typing import List, Optional, Dict, Any
from problems.knapsack.problem import KnapsackProblem

router = APIRouter(
    prefix="/knapsack",
    tags=["knapsack"],
    responses={404: {"description": "Not found"}},
)

problem_solver = KnapsackProblem()

class GenerateRequest(BaseModel):
    num_items: int = 10
    max_weight: int = 50
    max_value: int = 100

class SolveRequest(BaseModel):
    items: List[Dict[str, int]]
    capacity: int
    algorithms: List[str] = ["dp", "backtracking"]

class BenchmarkRequest(BaseModel):
    sizes: List[int] = [10, 20, 30]
    algorithms: List[str] = ["dp", "backtracking"]

@router.post("/generate")
async def generate_case(req: GenerateRequest):
    return problem_solver.generate_case(num_items=req.num_items, max_weight=req.max_weight, max_value=req.max_value)

@router.post("/solve")
async def solve_case(req: SolveRequest):
    input_data = {"items": req.items, "capacity": req.capacity}
    results = []
    for algo in req.algorithms:
        try:
            # Skip Backtracking for large inputs
            if len(req.items) > 22 and algo == "backtracking":
                 results.append({
                    "algorithm": algo,
                    "error": "Input too large for Backtracking (limit 22)",
                    "skipped": True
                })
                 continue

            res = problem_solver.solve(input_data, algo)
            results.append(res)
        except ValueError as e:
            results.append({"algorithm": algo, "error": str(e)})
    return results

@router.post("/benchmark")
async def benchmark(req: BenchmarkRequest):
    benchmark_results = []
    for size in req.sizes:
        input_data = problem_solver.generate_case(num_items=size)
        
        size_result = {"size": size, "algorithms": []}
        for algo in req.algorithms:
            try:
                if size > 22 and algo == "backtracking":
                     size_result["algorithms"].append({
                        "algorithm": algo,
                        "time_seconds": None,
                        "skipped": True
                    })
                     continue

                res = problem_solver.solve(input_data, algo)
                size_result["algorithms"].append(res)
            except Exception as e:
                size_result["algorithms"].append({"algorithm": algo, "error": str(e)})
        
        benchmark_results.append(size_result)
    return benchmark_results