Files
status-python-sdk/monitor.py
T
2026-03-24 18:46:36 +02:00

224 lines
7.8 KiB
Python

import datetime, os, pickle, yaml, time
import pandas as pd
from pathlib import Path
from dotenv import load_dotenv
# Manual file imports
from bot import Account, Logger
from postgres import Postgres
def load_config(file_path: str) -> dict:
"""
Load the config file and the `.env` variables
Parameter:
- `file_path` - the file path of the config yaml file. The `.env` variable must be in the same folder
Output:
- The config variables and secret from `.env`
"""
with open(file_path, "r") as f:
config: dict = yaml.safe_load(f)
env_file_path = os.path.join(os.path.dirname(file_path), ".env")
load_dotenv(env_file_path)
config["env_vars"] = {
key: value
for key, value in os.environ.items()
if key.startswith(("POSTGRES_", "STATUS_"))
}
return config
def extract_community_members(account: Account, community_id: str) -> pd.DataFrame:
"""
Extract the community members. Uses manual RPC call function.
Parameters:
- `account` - logged in Status Bot account
- `community_id` - community ID as it is in `account.communities`
Output:
- DataFrame with all of the members
"""
data = account.call_rpc("messaging", "communities")
for result in data["result"]:
if result["id"] != community_id:
continue
extract_timestamp = datetime.datetime.now()
members = pd.DataFrame([
{
"community_id": community_id,
"member_id": member_id,
"last_checked": datetime.datetime.fromtimestamp(info["last_update_clock"]) if "last_update_clock" in info else None,
"extract_timestamp": extract_timestamp
}
for member_id, info in result["members"].items()
])
return members
return pd.DataFrame()
def extract_community_channels(account: Account, community: dict, start_timestamp: datetime.datetime, end_timestamp: datetime.datetime) -> pd.DataFrame:
"""
Extract the community channel messages.
Parameters:
- `account` - logged in Status Bot account
- `community` - the current community from `account`
- `start_timestamp` - start timestamp for message fetching
- `end_timestamp` - end timestamp for message fetching
Output:
- DataFrame with all of the community messages for the given start and end timestamps
"""
final = []
for channel in community["channels"]:
messages = account.get_messages(channel["chat_id"], start_timestamp, end_timestamp)
messages = pd.DataFrame(messages)
if len(messages) == 0:
account.logger.info(f"No messages found for # {channel['name']}")
continue
account.logger.info(f"Extracted {len(messages)} message(s) from # {channel['name']}")
messages = messages.assign(
community_id = community["id"],
extracted_timestamp = datetime.datetime.now()
)
final.append(messages)
return pd.concat(final, ignore_index=True) if final else pd.DataFrame()
def download(folder: str, config: dict):
"""
Download Status App messages / info from communities and store them in pickle files.
Parameters:
- `folder` - the folder where the files will be created. Sub folders are automatically created
- `config` - the `load_config` configuration
"""
account = Account(**config.get("bot_params", {}))
available_accounts = [acc["display_name"] for acc in account.available_accounts]
messages_folder = os.path.join(folder, "messages")
os.makedirs(messages_folder, exist_ok=True)
community_info_folder = os.path.join(folder, "community")
os.makedirs(community_info_folder, exist_ok=True)
members_info_folder = os.path.join(folder, "members")
os.makedirs(members_info_folder, exist_ok=True)
prefix = "STATUS_"
params = {
key.replace(prefix, "").lower(): value
for key, value in os.environ.items()
if key.startswith(prefix)
}
if params["display_name"] in available_accounts:
params.pop("mnemonic")
account.login(**params)
now = datetime.datetime.now()
# Node will only return known / fetched messages for this channel.
# Without enabling community archives feature the node can only fetch last 30 days (from store nodes).
to_midnight = lambda date: date.replace(minute=0, second=0, hour=0, microsecond=0)
start_timestamp: datetime.datetime = to_midnight(now - datetime.timedelta(days=30))
end_timestamp: datetime.datetime = to_midnight(now - datetime.timedelta(days=1))
get_file_name = lambda: str(to_midnight(datetime.datetime.now()).timestamp()).replace(".", "") + ".pkl"
for community in account.communities:
account.logger.info(f"Extracting data for {community['name']} from {start_timestamp} to {end_timestamp}")
community["extracted_timestamp"] = datetime.datetime.now()
file_path = os.path.join(community_info_folder, get_file_name())
if not os.path.exists(file_path):
with open(file_path, "wb") as f:
pickle.dump(community, f)
account.logger.info(f"Created {file_path}")
file_path = os.path.join(members_info_folder, get_file_name())
if not os.path.exists(file_path):
members = extract_community_members(account, community["id"])
if len(members) > 0:
members.to_pickle(file_path)
account.logger.info(f"Created {file_path}")
file_path = os.path.join(messages_folder, get_file_name())
if not os.path.exists(file_path):
messages = extract_community_channels(account, community, start_timestamp, end_timestamp)
if len(messages) > 0:
messages.to_pickle(file_path)
account.logger.info(f"Created {file_path}")
def store(folder: str, config: dict):
"""
Upload Status App `download` file to Postgres.
NOTE: The Postgres schema must already exist
Parameters:
- `folder` - the folder where the files will be created. Sub folders are automatically created
- `config` - the `load_config` configuration
"""
path = Path(folder)
table_name_mapping: dict[str, str] = config["postgres"]["tables"]
table_schema = config["postgres"]["schema"]
upload: dict[str, list] = {}
completed = []
for file_path in path.rglob("*.pkl"):
table_name = table_name_mapping.get(file_path.parent.name)
if not table_name:
continue
data = pd.read_pickle(file_path)
if isinstance(data, dict):
data = pd.DataFrame([data])
if table_name not in upload:
upload[table_name] = []
upload[table_name].append(data)
completed.append(str(file_path))
prefix = "POSTGRES_"
params = {
key.replace(prefix, "").lower(): value
for key, value in config["env_vars"].items()
if key.startswith(prefix)
}
connector = Postgres(**params)
for table_name, data in upload.items():
if len(data) == 0:
continue
df = pd.concat(data, ignore_index=True)
json_columns = [
column
for column in df.columns
if isinstance(df[column].dropna().reset_index(drop=True).iloc[0], (dict, list))
]
connector.insert(df, table_name, table_schema, json_columns)
for file_path in completed:
os.remove(file_path)
if __name__ == "__main__":
folder = os.path.dirname(__file__)
config = load_config(os.path.join(folder, "config.yaml"))
upload_folder = os.path.join(os.path.dirname(__file__), "uploads")
logger = Logger()
while True:
download(upload_folder, config)
store(upload_folder, config)
logger.info(f"Sleeping for {config['sleep']} minute(s)")
time.sleep(config["sleep"])