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"""Models for the Folding Studio API."""
import json
import logging
import os
import sys
import time
from io import StringIO
from pathlib import Path
from typing import Any
import folding_studio
import gradio as gr
import numpy as np
from folding_studio import single_job_prediction
from folding_studio.client import Client
from folding_studio.commands.experiment import results as get_results
from folding_studio.commands.experiment import status as get_status
from folding_studio.query import Query
from folding_studio.query.boltz import BoltzQuery
from folding_studio.query.chai import ChaiQuery
from folding_studio.query.protenix import ProtenixQuery
from folding_studio_data_models import AF2Parameters, OpenFoldParameters
from folding_studio_data_models.parameters.base import BaseFoldingParameters
from folding_studio_demo.model_fasta_validators import (
BaseFastaValidator,
BoltzFastaValidator,
ChaiFastaValidator,
ProtenixFastaValidator,
)
class Capturing(list):
"""Capture stdout output."""
def __enter__(self):
self._stdout = sys.stdout
sys.stdout = self._stringio = StringIO()
return self
def __exit__(self, *args):
self.extend(self._stringio.getvalue().splitlines())
del self._stringio # free up some memory
sys.stdout = self._stdout
logger = logging.getLogger(__name__)
class AF3Model:
model_name = None
def __init__(self, api_key: str, query: Query, validator: BaseFastaValidator):
self.api_key = api_key
self.query = query
self.validator = validator
def call(
self, seq_file: Path | str, output_dir: Path, format_fasta: bool = False
) -> None:
"""Predict protein structure from amino acid sequence using AF3 model.
Args:
seq_file (Path | str): Path to FASTA file containing amino acid sequence
output_dir (Path): Path to output directory
format_description (bool): Whether to format the description of the sequence
"""
# Validate FASTA format before calling
is_valid, error_msg = self.check_file_description(seq_file)
if format_fasta and not is_valid:
logger.info("Invalid FASTA file format, forcing formatting...")
self.format_fasta(seq_file)
elif not is_valid:
logger.error(error_msg)
raise gr.Error(error_msg)
# Create a client using API key
logger.info("Authenticating client with API key")
client = Client.from_api_key(api_key=self.api_key)
# Define query
query: Query = self.query.from_file(path=seq_file, query_name="gradio")
query.save_parameters(output_dir)
logger.info("Payload: %s", query.payload)
# Send a request
logger.info(f"Sending {self.model_name} request to Folding Studio API")
response = client.send_request(
query, project_code=os.environ["FOLDING_PROJECT_CODE"]
)
# Access confidence data
logger.info("Confidence data: %s", response.confidence_data)
response.download_results(output_dir=output_dir, force=True, unzip=True)
logger.info("Results downloaded to %s", output_dir)
def format_fasta(self, seq_file: Path | str) -> None:
"""Format sequence to FASTA format.
Args:
seq_file (Path | str): Path to FASTA file
"""
formatted_fasta = self.validator.transform_fasta(seq_file)
with open(seq_file, "w") as f:
f.write(formatted_fasta)
def predictions(self, output_dir: Path) -> list[Path]:
"""Get the path to the prediction.
Args:
output_dir (Path): Path to output directory
Returns:
list[Path]: List of paths to predictions
"""
raise NotImplementedError()
def has_prediction(self, output_dir: Path) -> bool:
"""Check if prediction exists in output directory."""
return len(self.predictions(output_dir)) > 0
def check_file_description(self, seq_file: Path | str) -> tuple[bool, str | None]:
"""Check if the file description is correct.
Args:
seq_file (Path | str): Path to FASTA file
Returns:
tuple[bool, str | None]: Tuple containing a boolean indicating if the format is correct and an error message if not
"""
is_valid, error_msg = self.validator.is_valid_fasta(seq_file)
if not is_valid:
return False, error_msg
return True, None
class ChaiModel(AF3Model):
model_name = "Chai"
def __init__(self, api_key: str):
super().__init__(api_key, ChaiQuery, ChaiFastaValidator())
def call(
self, seq_file: Path | str, output_dir: Path, format_fasta: bool = False
) -> None:
"""Predict protein structure from amino acid sequence using Chai model.
Args:
seq_file (Path | str): Path to FASTA file containing amino acid sequence
output_dir (Path): Path to output directory
format_fasta (bool): Whether to format the FASTA file
"""
super().call(seq_file, output_dir, format_fasta)
def predictions(self, output_dir: Path) -> dict[Path, dict[str, Any]]:
"""Get the path to the prediction."""
prediction = next(output_dir.rglob("pred.model_idx_[0-9].cif"), None)
if prediction is None:
return {}
cif_files = {
int(f.stem.split("model_idx_")[1]): f
for f in prediction.parent.glob("pred.model_idx_*.cif")
}
# Get all npz files and extract their indices
npz_files = {
int(f.stem.split("model_idx_")[1]): f
for f in prediction.parent.glob("scores.model_idx_*.npz")
}
# Find common indices and create pairs
common_indices = sorted(set(cif_files.keys()) & set(npz_files.keys()))
return {
idx: {"prediction_path": cif_files[idx], "metrics": np.load(npz_files[idx])}
for idx in common_indices
}
class ProtenixModel(AF3Model):
model_name = "Protenix"
def __init__(self, api_key: str):
super().__init__(api_key, ProtenixQuery, ProtenixFastaValidator())
def call(
self, seq_file: Path | str, output_dir: Path, format_fasta: bool = False
) -> None:
"""Predict protein structure from amino acid sequence using Protenix model.
Args:
seq_file (Path | str): Path to FASTA file containing amino acid sequence
output_dir (Path): Path to output directory
format_fasta (bool): Whether to format the FASTA file
"""
super().call(seq_file, output_dir, format_fasta)
def predictions(self, output_dir: Path) -> list[Path]:
"""Get the path to the prediction."""
prediction = next(output_dir.rglob("sequence_*_sample_[0-9].cif"), None)
if prediction is None:
return {}
cif_files = {
int(f.stem[-1]): f
for f in prediction.parent.glob("sequence_*_sample_[0-9].cif")
}
# Get all npz files and extract their indices
json_files = {
int(f.stem[-1]): f
for f in prediction.parent.glob(
"sequence_*_summary_confidence_sample_[0-9].json"
)
}
# Find common indices and create pairs
common_indices = sorted(set(cif_files.keys()) & set(json_files.keys()))
return {
idx: {
"prediction_path": cif_files[idx],
"metrics": json.load(open(json_files[idx])),
}
for idx in common_indices
}
class BoltzModel(AF3Model):
model_name = "Boltz"
def __init__(self, api_key: str):
super().__init__(api_key, BoltzQuery, BoltzFastaValidator())
def call(
self, seq_file: Path | str, output_dir: Path, format_fasta: bool = False
) -> None:
"""Predict protein structure from amino acid sequence using Boltz model.
Args:
seq_file (Path | str): Path to FASTA file containing amino acid sequence
output_dir (Path): Path to output directory
format_fasta (bool): Whether to format the FASTA file
"""
super().call(seq_file, output_dir, format_fasta)
def predictions(self, output_dir: Path) -> list[Path]:
"""Get the path to the prediction."""
prediction_paths = list(output_dir.rglob("*_model_[0-9].cif"))
return {
int(cif_path.stem[-1]): {
"prediction_path": cif_path,
"metrics": np.load(list(cif_path.parent.glob("plddt_*.npz"))[0]),
}
for cif_path in prediction_paths
}
class OldModel:
model_name = None
def __init__(self, api_key: str):
self.api_key = api_key
def call(
self,
seq_file: Path | str,
output_dir: Path,
parameters: BaseFoldingParameters,
*args,
**kwargs,
) -> None:
"""Predict protein structure from amino acid sequence using AF2 model.
Args:
seq_file (Path | str): Path to FASTA file containing amino acid sequence
output_dir (Path): Path to output directory
"""
output = single_job_prediction(
fasta_file=seq_file,
parameters=parameters,
api_key=self.api_key,
)
experiment_id = output["message"]["experiment_id"]
done = False
while not done:
with Capturing() as output:
get_status(experiment_id, api_key=self.api_key)
status = output[0]
logger.info(f"Experiment {experiment_id} status: {status}")
if status == "Done":
done = True
logger.info("Downloading results")
get_results(
experiment_id,
force=True,
unzip=True,
output=output_dir / "results.zip",
api_key=self.api_key,
)
logger.info("Results downloaded to %s", output_dir)
else:
logger.info("Sleeping for 10 seconds")
time.sleep(10)
def format_fasta(self, seq_file: Path | str) -> None:
"""Format sequence to FASTA format.
Args:
seq_file (Path | str): Path to FASTA file
"""
return
def predictions(self, output_dir: Path) -> dict[int, dict[str, Any]]:
"""Get the path to the prediction.
Args:
output_dir (Path): Path to output directory
Returns:
dict[int, dict[str, Any]]: Dictionary mapping model indices to their prediction paths and metrics
"""
prediction_paths = list(
(output_dir / "results").rglob("relaxed_model_[0-9]_*_pred_0.pdb")
)
metrics_path = output_dir / "results" / "metrics_per_model.json"
if not metrics_path.exists():
return {}
with open(metrics_path, "r") as f:
metrics = json.load(f)
output = {}
for pred_path in prediction_paths:
model_id = int(pred_path.stem.split("_")[2])
model_name = "_".join(pred_path.stem.split("_")[1:-2])
output[model_id] = {
"prediction_path": pred_path,
"metrics": metrics[model_name],
}
return output
def has_prediction(self, output_dir: Path) -> bool:
"""Check if prediction exists in output directory."""
return len(self.predictions(output_dir)) > 0
def check_file_description(self, seq_file: Path | str) -> tuple[bool, str | None]:
"""Check if the file description is correct.
Args:
seq_file (Path | str): Path to FASTA file
Returns:
tuple[bool, str | None]: Tuple containing a boolean indicating if the format is correct and an error message if not
"""
return True, None
class AF2Model(OldModel):
model_name = "AlphaFold2"
def call(self, seq_file: Path | str, output_dir: Path, *args, **kwargs) -> None:
super().call(seq_file, output_dir, AF2Parameters(), *args, **kwargs)
class OpenFoldModel(OldModel):
model_name = "OpenFold"
def call(self, seq_file: Path | str, output_dir: Path, *args, **kwargs) -> None:
super().call(seq_file, output_dir, OpenFoldParameters(), *args, **kwargs)
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