io_utils.py

import os
import subprocess
import gradio as gr
import yaml

import pipe

YAML_PATH = "./cicd/configs"


class Dumper(yaml.Dumper):
    def increase_indent(self, flow=False, *args, **kwargs):
        return super().increase_indent(flow=flow, indentless=False)


def get_yaml_path(uid):
    if not os.path.exists(YAML_PATH):
        os.makedirs(YAML_PATH)
    if not os.path.exists(f"{YAML_PATH}/{uid}_config.yaml"):
        os.system(f"cp config.yaml {YAML_PATH}/{uid}_config.yaml")
    return f"{YAML_PATH}/{uid}_config.yaml"


# read scanners from yaml file
# return a list of scanners
def read_scanners(uid):
    scanners = []
    with open(get_yaml_path(uid), "r") as f:
        config = yaml.load(f, Loader=yaml.FullLoader)
        scanners = config.get("detectors", [])
    f.close()
    return scanners


# convert a list of scanners to yaml file
def write_scanners(scanners, uid):
    with open(get_yaml_path(uid), "r+") as f:
        config = yaml.load(f, Loader=yaml.FullLoader)
        if config:
            config["detectors"] = scanners
            # save scanners to detectors in yaml
            yaml.dump(config, f, Dumper=Dumper)
    f.close()


# read model_type from yaml file
def read_inference_type(uid):
    inference_type = ""
    with open(get_yaml_path(uid), "r") as f:
        config = yaml.load(f, Loader=yaml.FullLoader)
        inference_type = config.get("inference_type", "")
    f.close()
    return inference_type


# write model_type to yaml file
def write_inference_type(use_inference, uid):
    with open(get_yaml_path(uid), "r+") as f:
        config = yaml.load(f, Loader=yaml.FullLoader)
        if use_inference:
            config["inference_type"] = "hf_inference_api"
        else:
            config["inference_type"] = "hf_pipeline"
        # save inference_type to inference_type in yaml
        yaml.dump(config, f, Dumper=Dumper)
    f.close()
    return (gr.update(visible=(use_inference == "hf_inference_api")))



# read column mapping from yaml file
def read_column_mapping(uid):
    column_mapping = {}
    with open(get_yaml_path(uid), "r") as f:
        config = yaml.load(f, Loader=yaml.FullLoader)
        if config:
            column_mapping = config.get("column_mapping", dict())
    f.close()
    return column_mapping


# write column mapping to yaml file
def write_column_mapping(mapping, uid):
    with open(get_yaml_path(uid), "r") as f:
        config = yaml.load(f, Loader=yaml.FullLoader)
    if config is None:
        return
    if mapping is None and "column_mapping" in config.keys():
        del config["column_mapping"]
    else:
        config["column_mapping"] = mapping
    with open(get_yaml_path(uid), "w") as f:
        # save column_mapping to column_mapping in yaml
        yaml.dump(config, f, Dumper=Dumper)
    f.close()


# convert column mapping dataframe to json
def convert_column_mapping_to_json(df, label=""):
    column_mapping = {}
    column_mapping[label] = []
    for _, row in df.iterrows():
        column_mapping[label].append(row.tolist())
    return column_mapping


def get_logs_file(uid):
    try:
        file = open(f"./tmp/{uid}_log", "r")
        return file.read()
    except Exception:
        return "Log file does not exist"


def write_log_to_user_file(id, log):
    with open(f"./tmp/{id}_log", "a") as f:
        f.write(log)
    f.close()


def save_job_to_pipe(id, job, lock):
    with lock:
        pipe.jobs.append((id, job))


def pop_job_from_pipe():
    if len(pipe.jobs) == 0:
        return
    job_info = pipe.jobs.pop()
    write_log_to_user_file(job_info[0], f"Running job id {job_info[0]}\n")
    command = job_info[1]

    log_file = open(f"./tmp/{job_info[0]}_log", "a")
    subprocess.Popen(
        command,
        stdout=log_file,
        stderr=log_file,
    )