from fastapi import FastAPI, __version__
from pydantic import BaseModel
from typing import Set, List, Optional, Dict

app = FastAPI()

class MetaData(BaseModel):
  key: str
  value: str
  field_id: Optional[int]
  option_id: Optional[int]

class Item(BaseModel):
  id: int
  metadata: List[MetaData]

class Constraint(BaseModel):
  key: str
  value: str
  field_id: Optional[int]
  option_id: Optional[int]
  minimum: int
  maximum: int

class IRTModel(BaseModel):
  a_param: float
  b_param: float
  c_param: float
  model: str

class Targets(BaseModel):
  neg_2_5: int
  neg_1_5: int
  neg_0_5: int
  _0_5: int
  _1: int

class ObjectiveFunction(BaseModel):
  # minimizing tif/tcc target value is only option currently
  # as we add more we can build this out to be more dynamic
  # likely with models representing each objective function type
  tif_targets: Targets
  tcc_targets: Targets
  weight: Dict = {'tif': 1, 'tcc': 1}

class AdvancedOptions(BaseModel):
  # will supported currently
  linearity_check: bool
  show_progress: bool
  max_solution_time: Optional[int]
  brand_bound_tolerance: Optional[float]
  max_forms: Optional[int]
  precision: Optional[float]
  extra_param_range: Optional[List[Dict]]

class SolverContent(BaseModel):
  items: List[Item]
  constraints: List[Constraint]
  irt_model: IRTModel
  objective_fuction: ObjectiveFunction
  total_form_items: int
  advanced_options: Optional[List[AdvancedOptions]]
  engine: str

@app.get("/")
async def root():
  return {"message": "Welcome to Measures LOFT solver service. v0.1"}

@app.get("/healthcheck")
async def health():
  content = {
    "maintainer": "Meazure Horizon Team",
    "git_repo": "https://github.com/yardstick/measure-solver",
    "server": "OK",
    "fastapi version": __version__,
    "app version": "0.1.1"
  }
  return content

@app.get('/readycheck')
async def ready():
  return 'OK'  # just means we're on air

@app.post('/solve/')
async def solve(solver_content: SolverContent):
  return solver_content