File size: 1,846 Bytes
44459bb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
"""Base module for model prediction endpoint query."""
from __future__ import annotations
import json
import logging
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any
from folding_studio_data_models import FoldingModel
from pydantic import BaseModel
class Query(ABC):
"""Interface to define folding job queries."""
MODEL: FoldingModel | None = None
@classmethod
@abstractmethod
def from_file(cls, path: str | Path, **kwargs) -> Query:
"""Instantiates a Query object from a file."""
...
@classmethod
@abstractmethod
def from_directory(cls, path: str | Path, **kwargs) -> Query:
"""Instantiates a Query object from a directory."""
...
@classmethod
@abstractmethod
def from_protein_sequence(cls, protein: str, **kwargs) -> Query:
"""Instantiates a Query object from string representation of a protein."""
...
@property
@abstractmethod
def payload(self) -> dict[str, Any]:
"""Returns the payload to be sent in the POST request."""
...
@property
@abstractmethod
def parameters(self) -> BaseModel:
"""Parameters of the query."""
...
def save_parameters(self, output_dir: Path) -> None:
"""Writes the input parameters to a JSON file inside the output directory.
Args:
output_dir (Path): The directory where the inference parameters JSON file will be saved.
"""
inference_parameters_path = output_dir / "query_parameters.json"
output_dir.mkdir(parents=True, exist_ok=True)
with inference_parameters_path.open("w", encoding="utf-8") as f:
json.dump(self.parameters.model_dump(mode="json"), f, indent=4)
logging.info(f"Input parameters written to {inference_parameters_path}")
|