25-cxsj-final/backend/routers/n_queens_router.py

67 lines
2.1 KiB
Python

from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
from typing import List, Optional, Dict, Any
from problems.n_queens.problem import NQueensProblem
router = APIRouter(
prefix="/n_queens",
tags=["n_queens"],
responses={404: {"description": "Not found"}},
)
problem_solver = NQueensProblem()
class GenerateRequest(BaseModel):
n: int = 8
class SolveRequest(BaseModel):
n: int
algorithms: List[str] = ["backtracking"]
class BenchmarkRequest(BaseModel):
sizes: List[int] = [4, 8, 10, 12]
algorithms: List[str] = ["backtracking"]
@router.post("/generate")
async def generate_case(req: GenerateRequest):
return problem_solver.generate_case(n=req.n)
@router.post("/solve")
async def solve_case(req: SolveRequest):
input_data = {"n": req.n}
results = []
for algo in req.algorithms:
try:
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 = {"n": size}
size_result = {"size": size, "algorithms": []}
for algo in req.algorithms:
try:
# Skip large N for backtracking if finding all solutions (though our solve defaults to one for large N)
# But for benchmark we might want to be careful.
if size > 14 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